text:
\section*{Acknowledgments}
We would like to thank TTIC SL@TTIC researchers who participated in a preliminary annotation study.

\subsection{Evaluating Sets of Continuations}
\label{sec:evalsets}

We now evaluate \emph{sets} of continuations using our set-level metrics.
Standard methods to generate sets of outputs include beam search (BS) and temperature-based sampling (TS), which we use as baselines.

TS with temperature $\tau$ corresponds to transforming probabilities $p_i$ as follows: $\hat{p}_i \propto p_i^{\frac{1}{\tau}}$.

A high temperature $\tau$ leads to high variety in generated samples, but also more noise, while lower temperatures lead to samples with less noise but also less diversity.

For each attribute, we generate continuations for each of its values, and compare to BS and TS systems with the same number of outputs.

For example, for sentiment, we generate continuations for each of the 3 sentiment values and compare to BS and TS with 3 continuations.

Results are shown in Table \ref{table:diversity}. BS shows the least diversity (as evidenced by its high self-BLEU scores). However, it generally yields high average ROUGE and BLEU scores. TS does very well in terms of diversity, and this diversity enables it to produce higher max scores than BS, but it has lower averages when using small numbers of continuations (3 or 5).

Our sentiment- and cluster-controlled systems outperform TS in max metric scores and BS in diversity (self-BLEU). They also have the highest average BLEU scores, though the differences are small. With 30 continuations, TS with $\tau = 0.5$ performs best across all metrics; this number of continuations appears to be well-suited for temperature $0.5$. As we move to 100 continuations, we find that using our frame control variable leads to better diversity than TS, suggesting that the move to 100 samples has introduced some amount of repetition. By contrast, the 100 distinct frames and frame sets yield better diversity.

\subsection{Automatically Choosing Attribute Values}
\label{automic_generation}

Using our framework, we can generate continuations with any attribute values. However, if we are interested in generating a single continuation, we do not know the ideal attribute values to use. So, we propose two methods to automatically select a small set of values for the frame attribute.

\paragraph{Frames + Reranking:} Following \citet{LiGBGD15}, we rerank the outputs from the 100 most frequent frame sets by linearly combining the forward score $p(y\mid x)$ and the ``reverse score'' $ \lambda p(x\mid y)$, where the latter comes from a separately-trained seq2seq model. The forward score $p(y\mid x)$ is adjusted by dividing by the length of $y$ in order to not favor shorter outputs.

\paragraph{Predicted Frames:} We also build a model to automatically predict the frames in the continuation.
Given the frames in a sentence $x$, we compute a binary frame vector $f_x$ where
entry $j$ is 1 if frame $j$ appears in $x$. We train a model that predicts the frame vector of the continuation given the frame vectors of the previous 4 sentences.  The model is an LSTM followed by averaging of hidden states. Mean squared error is minimized during training. After training, the $k$ continuations are selected based on the $k$ frames with the highest predicted score under this frame prediction model.

We use these two methods to produce 3 continuations for each story and report results in Table~\ref{table:diversity}. They both achieve a similar balance of quality and diversity as TS with $\tau=0.6$, with reranking leading to greater diversity than frame prediction and the latter showing higher ROUGE/BLEU scores.

\section{Conclusion and Future Work}
We proposed a controllable framework that generates the next sentence of a story given its context. We experimented with a broad range of control attributes and demonstrated that our framework can accurately generate outputs that match the target values.
Sets of outputs from our method show high diversity and high oracle metric scores.
The human evaluation shows that the multiple suggestions from our model show promise for integration in a collaborative writing system. Future work could explore other control attributes as well as a compositional framework to control multiple attributes jointly.

\section{Results}
We now present our experimental results. Section~\ref{sec:controllability} includes results related to how well our generated output matches the desired attribute values. Section~\ref{sec:oracle} presents results when generating continuations with oracle attribute values.
In Section~\ref{sec:evalsets} we use our set-level metrics to evaluate sets of outputs with various attribute values. In Section~\ref{automic_generation} we report results when attempting to automatically infer attribute values to generate a small set of high-quality outputs.

\subsection{Controllability Evaluation}
\label{sec:controllability}
In this section, we evaluate the controllability accuracy of our framework by automatically measuring the match between the attribute values of the generated continuations and the desired values.
For certain control variables, like sentiment and frames, this automatic evaluation is prone to errors in the associated analyzers.
That is, the metrics that rely on automatic analyzers could become artificially high if our generation models learn to produce outputs that match the biases of the analyzers.
We could instead consider manual evaluation of control accuracy. However, we were more interested in devoting our manual evaluation to the question of whether users would find the system outputs useful for a particular goal.

\begin{table}[t]
\centering
\small
\begin{tabular}{l|c|c|c|}
\cline{2-4}
& \multicolumn{3}{c|}{\bf{Generated Continuations}}
\\ \cline{2-4} {\bf{Target Sentiment}}
& negative & neutral & positive  \\\hline
\multicolumn{1}{|l|}{negative} & \bf{68.5} & 19.6  & 12.0  \\
\multicolumn{1}{|l|}{neutral} & \phantom{0}7.0 & \bf{73.9}  & 19.2    \\
\multicolumn{1}{|l|}{positive} & \phantom{0}0.8 & \phantom{0}3.1 & \bf{96.2}  \\ \hline
\end{tabular}
\caption{Sentiment match percentages of generated continuations and target sentiment values.}
\label{table:sentiment_analysis}
\end{table}

\begin{table}[t]
\centering
\small
\begin{tabular}{c|c|c|c|c|}
\cline{2-5}
& $\dif=0$ & $ \dif \leq 1$  & $ \dif \leq 2$ & $\dif \leq 3$ \\ \hline
\multicolumn{1}{|c|}{3 bins} & 95.8  & 100 & - & -\\
\multicolumn{1}{|c|}{30 bins} & 70.35  & 94.8 & 99.25 & 99.9 \\ \hline
\end{tabular}
\caption{Frequency (\%) of the generated continuations in the range of $\dif=|l -l_p|$ where $l$ is the continuation length and $l_p$ is the desired length.}
\label{table:length_result}
\end{table}

\paragraph{Sentiment.}

We generate three continuations for each story in the development set, one for each sentiment label. Using the same sentiment analyzer from \citet{D13-1170} as above,
we obtain predicted sentiment labels for the continuations. Table \ref{table:sentiment_analysis} shows the sentiment distribution for each label. We see that the vast majority of the time, the continuations match the desired values. Matching positive sentiment is easiest for our model, followed by neutral.

\paragraph{Length.}

We quantize the generation lengths into bins, each representing a size range. Below are the two settings we consider:
\begin{itemize}
\item 3 bins: We use three bins with the following length ranges:
[1,7],
[8,13], and
[14, $\infty$).

\item 30 bins: We use a bin for each length. No sentence is longer than 30.
\end{itemize}

During training, we do not update the representations of the length control variable. After training, we treat the length of the continuation in the development set as the target control variable and generate continuations for each length. The results are shown in Table~\ref{table:length_result} and demonstrate that our model can generate continuations with the desired length with only small differences.

\begin{table}[t]
\centering
\small
\begin{tabular}{|l|r|l|r|}
\hline \bf Predicate & \bf M\% & \bf Predicate &   \bf M\%  \\ \hline
was &  100 & got & 100 \\
had & 100 & decided & 94.4 \\
went & 99.9 & is & 100 \\
 made  & 99.25 & were & 100 \\
 found & 100& get & 99.95\\
felt &  99.55 & go & 100 \\
took & 99.2& ended & 98.25 \\
be  & 99.95 & told & 99.9  \\
gave   & 99.95 & left & 99.85 \\
said & 100& bought & 100\\
\hline
\end{tabular}
\caption{\label{srl} Match percentages (M\%) showing fraction of stories for which generated continuations contain the desired predicate.
The 20 most frequent predicates are shown; additional results are in the Appendix.}
\end{table}

\paragraph{Verbal Predicates.}

We select the top 100 most frequent verbal predicates in the training data. Then for all the stories in the development set, we generate a continuation for each of the 100 predicates. We check whether the predicate appears in the generated continuations. As the results in Table \ref{srl} show, the framework can nearly always generate outputs with the desired predicates.

\begin{table}[t]
\setlength{\tabcolsep}{3pt}
\centering
\small
\begin{tabular}{|l|l|l|l|}
\hline \bf Frame & \bf M\% & \bf Frame &   \bf M\%   \\ \hline
Calendric\_unit & 99.5& Locative\_relation & 87.4 \\
 Arriving & 98.9 & Quantity & 92.5 \\
Temporal\_collocation & 99.9 & Becoming& 99.6 \\
Cardinal\_numbers& 89.1 &Being\_obligated & 97.7 \\
Kinship& 94.4& Intentionally\_act& 99.7 \\
Statement& 98.0 & Causation& 98.6 \\
Emotion\_directed & 98.7& Buildings& 93.0 \\
Personal\_relationship& 92.7 & Food& 79.2 \\
Self\_motion& 86.4&Capability & 99.9 \\
Desirability & 98.1& Observable\_body\_parts& 74.2\\
\hline
\end{tabular}
\caption{\label{frame} Match percentages (M\%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix.}
\end{table}

\begin{table}[t]
\centering
\small
\begin{tabular}{c|c|c|c|c|c|}
\cline{2-6}
& \multicolumn{5}{c|}{\bf{Generated Continuations}}
\\ \cline{2-6} {\bf{Target Value}}
& 0 & 1 & 2 & 3 & 4  \\\hline
\multicolumn{1}{|c|}{0} & \bf{79.9} & \phantom{0}2.8  & \phantom{0}3.1  & \phantom{0}0.9 & 13.4\\
\multicolumn{1}{|c|}{1} & \phantom{0}5.1 & \bf{63.1}  & 26.4 & \phantom{0}1.4 & \phantom{0}4.2  \\
\multicolumn{1}{|c|}{2} & \phantom{0}2.6 & \phantom{0}2.0 & \bf{90.6} & \phantom{0}0.3 & \phantom{0}4.7\\
\multicolumn{1}{|c|}{3} & 20.9 & 20.1 & 24.6 & \bf{31.0} & \phantom{0}3.5\\
\multicolumn{1}{|c|}{4} & \phantom{0}0.9 & \phantom{0}0.3 & \phantom{0}0.5 & \phantom{0}0.1 & \bf{98.3}\\ \hline
\end{tabular}
\caption{Cluster match percentages (\%) for each value of the cluster control variable.
}
\label{table:knn_analysis}
\end{table}

\begin{table*}[ht]
\centering
\small
\begin{tabular}{l|c||c|c||c||c|c|c|}
\cline{2-8}
& \multicolumn{1}{c||}{\multirow{2}{*}{PPL ($\downarrow$)}}
& \multicolumn{2}{c||}{ROUGE}
& \multicolumn{1}{c||}{\multirow{2}{*}{BLEU ($\uparrow$)}}
& \multicolumn{3}{c|}{Story Scorer}
\\

& & \multicolumn{1}{c}{ROUGE-1 ($\uparrow$)} & ROUGE-L ($\uparrow$) & & \multicolumn{1}{c}{O ($\uparrow$)} & \multicolumn{1}{c}{R ($\uparrow$)}  & I ($\uparrow$)  \\ \hline
\multicolumn{1}{|l|}{seq2seq} & 25.8  & 27.0
& 23.5 & 17.7 & 5.5 & 5.5 & 4.8\\
\hline
\multicolumn{1}{|l|}{sentiment} & 25.0 &  26.7
& 23.5 & 17.7  & 5.5  & 5.6 & 4.8  \\
\multicolumn{1}{|l|}{length} & 23.1 & 27.3
& 24.6 & 20.3 & 5.7 & 5.8 & 5.0\\
\multicolumn{1}{|l|}{predicates} & 17.1 &  42.9
& 35.1 & 26.4  & 6.0  & 6.2 & 5.2\\
\multicolumn{1}{|l|}{frames} &  15.0 & 41.1
& 35.0  & 27.2 & 5.9  & 6.1 & 5.2 \\
\multicolumn{1}{|l|}{clusters} & 24.3  & 28.6
& 25.0 & 18.4  & 5.5 & 6.1 & 5.1 \\
\hline
\multicolumn{1}{|l|}{BOW} & \phantom{0}5.7 & 64.5
& 54.5 & 45.4 & 6.2 & 6.2 & 5.2\\

 \hline
\multicolumn{1}{|l|}{gold standard} &  - & -   & - & - &6.5  & 6.7 & 5.7  \\
\hline

\end{tabular}
\caption{Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics).}
\label{table:oracle_result}
\end{table*}

\paragraph{Frame Semantics.}

In order to check how frequently the generated output matches the desired frames, we generate continuations for the top 100 frames (one frame for each continuation) for all stories in the development set. We check whether the frame appears in the specific continuation using SEMAFOR. The results are shown in Table \ref{frame}. Most frames have very high match accuracies, but there are a few frames with much lower accuracy, such as ``Food'' and ``Observable\_body\_parts''. These are more concrete frames that may be difficult to reasonably incorporate in certain story contexts.

\paragraph{Automatically-Induced Clusters.}

Given the cluster, the model generates a continuation. Then, we represent the continuation as a bag-of-words sentence embedding (using the same method as when performing the initial clustering) and find the cluster that has the nearest cluster embedding. Then we check whether the two clusters match.

In analyzing the clusters, we observed that cluster 0 corresponds to simple but reasonable continuations. Cluster 2 corresponds to continuations with positive sentiment. Cluster 4 contains continuations with more actions.  Some of the generated outputs are shown in Table \ref{tab:system-outputs-all}.
From the results in Table \ref{table:knn_analysis}, we still see controllability for most clusters; however, for target cluster 3, which is rather generic based on our observations, the generated output seems flat.

\subsection{Evaluation with Oracle Attributes}
\label{sec:oracle}

Table \ref{table:oracle_result} shows automatic metric scores with
oracle attribute values, i.e., using the attribute values of the gold standard continuations. Unsurprisingly, compared with the seq2seq baseline, the perplexity decreases and the ROUGE and BLEU scores increase with oracle attributes
We also find that the scores from the story scorer, which does not use the gold standard while scoring, also show improvements over the baseline.
We note that frame semantics form one of the most useful control attributes, aside from those that use words directly.

The oracle BOW representation of the gold standard continuation yields the lowest perplexity and highest ROUGE and BLEU scores. It is not surprising that using this attribute
would be useful according to metrics that favor matching the gold standard. However, these results do show that our simple modeling framework can make use of the information in the control variable with a high degree of  effectiveness. In addition, while the scores from the story scorer are generally higher than for other control attributes, they are roughly on par with those when using predicates and frames.

\section{Control Attributes}

\input{bigtable-examples.tex}

In this section, we describe the control attributes we explored using our framework. Table~\ref{tab:system-outputs-all} shows examples of generated continuations for a single story context with several values for the control attributes described below. Given our simple modeling framework, it would be natural to experiment with combining control attributes via summation or averaging of the attribute representations, but we leave an investigation of this to future work, focusing here on using one attribute at a time.

\paragraph{Sentiment.}
Stories may express sentiment regarding their characters or circumstances. We acquire sentiment labels by running the pretrained analyzer from \citet{D13-1170} on the continuations in the training data. The analyzer produces three labels: ``negative'', ``neutral'', or ``positive''.

During training, $z_{\mathbf{enc}_{\Psi}}$ and $z_{\dec}$ are fixed one-hot vectors for each value.

\paragraph{Length.}
Some prior work has generated summaries with a desired length~\citep{kikuchi-EtAl:2016:EMNLP2016,W18-2706}. We similarly use length of the continuation as a control attribute. Instead of using an embedding for each integer length value, we group the lengths into a small number of bins (details are provided below). $z_{\mathbf{enc}_{\Psi}}$ and $z_{\dec}$ are fixed one-hot vectors for each bin.

\paragraph{Verbal Predicates.}
Semantic role labeling (SRL) is a form of shallow semantic parsing that annotates predicates and their arguments in sentences. We consider predicates from a semantic role labeling as control attributes. We use the SRL system from AllenNLP~\citep{Gardner2017AllenNLP} to automatically obtain predicates for the continuations in our training set. Then, a predicate vector is obtained by first summing up 100-dimensional GloVe embeddings~\citep{pennington2014glove} of the predicted predicates (if there is more than one), then reducing the dimension to 64 using principal component analysis.\footnote{The reason we use PCA here is to make all attribute embeddings have comparable embedding size, though we did not systematically evaluate the effect of this choice.} We wish to clarify that we do not use the argument structure from the SRL system. We restrict our focus to simply the set of verbal predicates in the SRL structure; this would presumably be simpler to use in interactive settings where users would specify attribute values for generating continuations.

\paragraph{Frame Semantics.}
A story is composed of a sequence of meaningful events~\citep{story}, often following particular patterns described in various terms such as scripts~\citep{schank+abelson77} and frames. FrameNet~\citep{BakerFillmoreLowe:98} is an inventory of semantic frames, which are semantic abstractions describing universal categories of events, concepts, and relationships.

We consider frame semantics as another control attribute in our framework. In order to get a frame semantic representation for a continuation, we use SEMAFOR~\citep{frameparsing}. SEMAFOR automatically produces a frame-semantic parse for a sentence, which consists of spans that evoke particular frames in FrameNet as well as annotations of textual spans that correspond to frame-specific arguments. For our purposes, we drop the arguments and only use the set containing all frames that are evoked in the sentence. A sentence may contain multiple frames. For example, in the sentence ``Roa's advice made Emma a lot happier in her life!'', ``a lot'' evokes the \emph{Quantity} frame while ``Emma a lot happier'' evokes the \emph{Effect} frame.

The frame set variable $z$ is computed by summing embeddings for the frames in the set:
\begin{equation}
z = R(l)=\sum_{j \in l}R_j \label{table:sum_vec}
\end{equation}
where $l$ is the frame set and $R_j$ is the representation of frame $j$. The frame embeddings are learned during training.\footnote{The dimension of the frame vector is 64 in our experiments.} For modeling purposes, we restrict our attention to the 100 most frequent frames in the training data. The rest of the frames are pooled together to form a single additional ``catch-all'' frame.

\paragraph{Automatically-Induced Clusters.} We also experiment with running $k$-means clustering on the bag-of-words sentence representations of the continuations in the training set.
We treat these automatically-induced cluster labels as control attribute values. Below we describe experiments with different cluster labels and analyze the characteristics of the generated outputs.

\paragraph{Oracle Bag-of-Words Sentence Representations.}
We also consider the use of a bag-of-words (BOW) sentence representation as a control attribute. Naturally, the sentence representation of the continuation is not available before generating the continuation in practice. However, we can use this attribute to verify the capability of our model to reconstruct the continuation from its bag-of-words representation.

\section{Experimental Setup}

\subsection{Datasets}

We experiment with the publicly available ROC story corpus developed by \citet{mostafazadeh-EtAl:2016}.
It consists of approximately 100K five-sentence stories of everyday events. We sample 2000 stories as a development set and 2000 as our test set. The remaining stories form our training set. Our goal is to generate the fifth sentence (the ``continuation'') given the previous four sentences. We use the 10k most frequent words in the training set as our vocabulary. A special token $\langle \mathit{unk}\rangle$ is introduced for unknown words.

\subsection{Evaluation}
Previous work evaluates generation tasks with automatic metrics, such as perplexity (\textbf{PPL}), \textbf{BLEU}~\citep{bleu},\footnote{In this paper, all BLEU scores are BLEU-2 scores (i.e., using unigrams and bigrams).}

and \textbf{ROUGE}~\citep{Lin04rouge}.
We adopt these in our evaluation and add three more metrics using the pretrained story scorer from \citet{sagarkar-18}. The scorer rates a generated continuation given its context along three dimensions: \textbf{relevance (R)}, \textbf{interestingness (I)}, and \textbf{overall quality (O)}. The story scorer does not use a gold standard continuation.

In addition, to evaluate the diversity of the generation, we use \textbf{Max-BLEU}\footnote{While max metric scores are not solely measuring diversity, they do provide a sense of the potential of the list. If all entries on the list are the same, the max metric scores would equal the average metric scores. The difference between the max and average metric scores therefore can be viewed as providing a bound on the diversity.} and \textbf{Max-ROUGE}.

First, we compute BLEU and ROUGE scores over a set of outputs $(y_1, y_2, ..., y_n)$ with different attribute values given the same story context, then we compute the max scores:
\begin{align*}
\maxbleu &= \max_{i} \bleu (y_i, r) \\
\maxrouge &= \max_{i} \rouge (y_i, r)
\end{align*}
where $r$ is the gold standard continuation.

We also use \textbf{Self-BLEU}~\citep{zhu2018texygen} to evaluate the diversity of a set of outputs. It is calculated by averaging the BLEU scores computed between all pairs of generated continuations for a given context, then averaging this quantity over all contexts.

The smaller the Self-BLEU score is, the more diverse are the generated outputs.

\subsection{Training Details}
Our seq2seq model has a 2-layer biLSTM~\citep{Hochreiter:1997:LSM:1246443.1246450} encoder and a 1-layer LSTM decoder. The hidden dimension of all layers is 512. The word embedding dimension is also 512. For optimization, we use Adam~\cite{kingma2014adam} with learning rate 0.001. We use early stopping based on perplexity on the development set.

\section{Human Evaluation}
Our previous results demonstrate that our frame control system has strong controllability and diversity in generation. In this section, we conduct a human evaluation to assess the utility of providing multiple suggestions from our models in a creative writing setting. We consider four different systems: \textbf{BS} with beam size 3; \textbf{TS} with 3 continuations using $\tau=0.6$, which we found to produce outputs with more diversity than $0.5$; \textbf{reranking} the 100 most frequent frame sets and using the top 3; and using continuations from the top-3 \textbf{predicted} frames under our frame prediction model.\footnote{The BS and TS baselines do not use control variables.}

To assess which set of generations from these four systems are most helpful in a collaborative writing setting, we collect annotations using Amazon Mechanical Turk. We randomly select 100 stories. For each story, we generate three outputs as a set of suggestions for each system, so there are 600 comparision pairs in total. We show workers two sets of outputs from different systems and ask them to select which suggestion is more helpful for writing the next line in the story. We also provide a choice of ``neither one is helpful at all''. We ask them explicitly to imagine they are writing the next line of the given story (see the appendix for more details).

Table \ref{table:human_analysis1} shows the results.\footnote{We remove results from 10-question sets where more than half of the questions were answered with the ``neither'' option, as we were concerned that these annotators did not fully understand the task or did not spend enough time studying the continuations. This occurred in roughly one third of question sets.}
We observe that workers prefer the BS baseline over TS, although TS yields higher diversity. This could be because the continuations from BS are shorter, simpler, and more fluent. In addition, we observe that workers prefer the outputs from the reranking system over BS more often than not. Although the predicted frame system yields more diverse outputs, workers still prefer BS, likely due to the difficulty in predicting frames.
The reranking and predicted frame systems are both preferred to TS, though the gap is smaller with the predicted system. We also see that generating helpful suggestions is a difficult task: in many cases workers thought neither system was helpful, especially when given the outputs from BS/TS or TS/predicted.

One may ask why workers do not show a stronger preference for the more diverse sets of outputs. From our own preliminary annotations, we believe this is because diverse outputs tend to be longer and harder to understand, and also because greater diversity increases the chance of producing disfluent or nonsensical outputs. The BS outputs, by comparison, are sensical and mostly on-topic. Even if the suggestions are not creative, they may still help a worker to think about a new direction for the story to take. Nonsensical or disfluent suggestions, however, are rarely helpful.

\begin{table}[t]
\centering
\small
\begin{tabular}{|cc|ccc|}
\cline{3-5}
\multicolumn{2}{c}{} & \multicolumn{3}{|c|}{\bf{Human Preference}}\\
\multicolumn{1}{c}{system 1} & system 2
& 1 & 2  & neither \\ \hline
BS & TS
& 43 & 16 & 29\\
BS & reranking

& 18 & 30 & 15\\
BS & predicted

& 38 & 29 & 19\\
TS & reranking

& 18 & 38 & 16\\
TS & predicted

& 18 & 27 & 34\\
reranking & predicted

& 27 & 24 & 21\\
\hline
\end{tabular}
\caption{Human preferences when given three continuations from each pair of systems.}
\label{table:human_analysis1}
\end{table}
\section{Introduction}
\label{intro}

We consider the problem of automatic story continuation generation, i.e., how to generate story continuations conditioned on the story context.
Inspired by recent work in controllable generation \citep{DBLP:conf/icml/HuYLSX17,DBLP:journals/corr/FiclerG17aa}, we propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs based on interpretable control variables.
Each control variable corresponds to an attribute of a sentence. Compared to previous work that only seeks to control the values of sentiment \citep{DBLP:conf/icml/HuYLSX17} and length \citep{kikuchi-EtAl:2016:EMNLP2016},
we further explore neural text generation with particular verbal predicates,
semantic frames, and automatically-induced clusters.

We compare the diversity of story continuations controlled by different sentence attributes and find that using frames yields
a stronger combination of diversity and quality than other control variables as measured by automatic metrics.
Unlike certain other attributes, frames have hundreds of possible values. Some frame values can help to get a natural continuation, while others are not applicable when considering the story context.

\begin{table}[t]
\small
\centering
\begin{tabular}{|ll|}
	\hline
	\textbf{Context:} & \\ \hline
	\multicolumn{2}{|p{0.9\columnwidth}|}{
    sandra needed a new phone . her old one had broken . she walked into the store and bought a new one . she was very excited .}\\
	\hline
	\multicolumn{2}{|l|}{\textbf{Control Attributes and Generated Continuations:}} \\ \hline
	\multicolumn{2}{|l|}{\textbf{Sentiment}:} \\
	\multicolumn{2}{|l|}{positive \quad\quad sandra was happy to have a new phone .} \\
	\hline
	\multicolumn{2}{|l|}{\textbf{Predicates}:} \\
	loved & sandra loved the new one . \\
	gave & sandra 's mom gave sandra a refund . \\
	\hline
   \textbf{Frames}:&\\
   Calendric\_unit & it was the perfect day of her life .\\
	Cardinal\_numbers & it was a perfect one . \\
	Activity\_ongoing & she kept it on the couch . \\
    \hline
    \end{tabular}
\caption{Story continuations conditioned on various control attributes generated from our framework.}
\label{tab:task}
\end{table}

We quantitatively evaluate both controllability and diversity.
Our empirical results show that: (1) our framework is accurate in terms of generating outputs that match the target attribute values; (2) our framework increases maximum metrics scores compared to $n$-best list generation with beam search; (3) controlling generation with frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics.

We also explore methods to rerank continuations to choose attribute values automatically and obtain a small number of high-quality outputs.
We consider two frame ranking methods: one reranks the generated continuations using a reverse scoring model~\citep{LiGBGD15} and returns the $k$-best generations; the second first predicts the $k$ most likely frames based on the context, and uses these frames to generate continuations. One potential use case of controllable, diverse story generation is collaborative writing applications~\citep{ClarkRTJS18}.

We conduct a human evaluation to assess the utility of providing multiple suggestions from our models in this
setting, demonstrating promising results for the potential of controllable generation for collaborative writing.

\section{Model}

Our controllable generation framework is a variation of a sequence-to-sequence (seq2seq) model with attention~\cite{NIPS2014_5346,BahdanauCB14,LuongPM15}.
To represent the control attribute values, we define an attribute embedding function $R$ that maps a given attribute value $l$ to a vector $z$: $z = R(l)$.
Here $l$ can be a single discrete number or a set of discrete numbers, depending on what attributes are being used. The control variable $z$ contains two parts: $z = [z_{\mathbf{enc}_{\Psi}}; z_{\dec}]$
where semicolon (;) denotes vertical concatenation and  $z_{\mathbf{enc}_{\Psi}}$  and $z_{\dec}$ are additional inputs for the encoder and decoder respectively.

\paragraph{Encoder.} For our encoder, we use a standard bidirectional recurrent neural network (RNN):
\begin{align*}
\overrightarrow{s_{i}}&=f_{e1}([v_{i}; z_{\mathbf{enc}_{\Psi}}], \overrightarrow{s_{i-1}}) \\
\overleftarrow{s_{i}}&=f_{e2}([v_{i}; z_{\mathbf{enc}_{\Psi}}], \overleftarrow{s_{i+1}}) \\
s_{i} &= [\overrightarrow{s_{i}};\overleftarrow{s_{i}}]
\end{align*}
where $v_i$ is the vector representation of word $x_i$, $s_{i} \in \mathbb{R}^d$ is the hidden state at time $i$, and $f_{e1}$ and $f_{e2}$ are the forward and backward RNN functions.

\paragraph{Decoder.} Our decoder uses an RNN with the general global attention scheme from \citet{LuongPM15}.
An additional input $z_{\dec}$ is fed to the decoder at each time step to reflect the characteristics of the control variable:
\begin{align}
h_j=f_d([y_{j-1}; z_{\dec}], h_{j-1}) \nonumber
\end{align}
\noindent where $h_j$ is the decoder RNN hidden vector at time step $j$ and $f_d$ is the decoder RNN function. Then, the conditional probability with controllable generation can be decomposed as follows:
\begin{align}
\log p_{\Theta}(y \mid x,l) = \sum_{j=1}^{|y|} \log p_{\Theta}(y_j \mid y_{<j},s, l)\nonumber
\end{align}
Here $s$ represents the hidden states of the source sequence and $\Theta$ are the parameters of the seq2seq attention model.

\paragraph{Training.}

Our training objective is:
\begin{align}
\min_{\Theta,  R} \sum_{\langle x, l, y\rangle \in \mathcal{D}}-\log p_{\Theta}(y \mid x, l)\label{eq:updateloss}
\end{align}
\noindent where $\mathcal{D}$ is the training data, i.e., we assume attribute values $l$ are provided during training. In practice, these will be predicted automatically using a linguistic analyzer.

With certain attributes, we do not update the attribute value embeddings $R$, i.e., we fix $z_{\mathbf{enc}_{\Psi}}$ and $z_{\dec}$ to one-hot vectors.

\paragraph{Inference.} We can specify the value of the control variable and generate specific outputs. By changing the variable values, we obtain multiple continuations for the same context. Beam search is used for decoding.

\section{Related Work}

Automatic story generation has a long history, with early work based primarily on hand-written rules \citep{klein-73, meehan1977tale, dehn1981story, turner1993minstrel}. Subsequent methods were based on planning from artificial intelligence \citep{theune2003virtual, oinonen-2006, riedl2010narrative} and,  more recently, data-driven methods have been developed
\citep{mcintyre-lapata:2010:ACL,elson-12, daza-calvo-figueroanazuno:2016:CLfL2016,roemmele2015creative,N18-1204,martin2018event,fan-etal-2018-hierarchical,yao2019plan,fan-etal-2019-strategies}. In concurrent work, \citet{gupta-etal-2019-writerforcing} also propose methods to generate
more diverse and interesting story endings, albeit without control variables.

In stronger relevance to our work, \citet{ClarkRTJS18} explore a creative writing setting with a machine in the loop, albeit with mixed results in terms of the quality of system suggestions.
Predicting and controlling with frame values suggests a new way of interacting with collaborative writing systems, as long as frames can be communicated to users in ways they can easily understand. Recently, \citet{N18-1204} proposed a neural text generation method that explicitly represents and tracks entities.
In addition, event sequences~\citep{D17-1168,DBLP:journals/corr/abs-1805-06122} are important elements in narrative texts but under-explored for story generation. These and related characteristics of creative writing could be incorporated into our framework as control attributes in future work.

The broader neural text generation community has also recently been interested in controllable text generation, i.e., generating text with specified characteristics reflected by control variables.
In some previous work, the variables to be
controlled are embedded into vectors which are
then fed into models to reflect the characteristics
of the variables. \citet{kikuchi-EtAl:2016:EMNLP2016} and \citet{W18-2706} developed methods for controllable summarization, for example permitting users to control the length of the generated summary.
Related work has controlled style, topic, and sentiment polarity~\citep{DBLP:conf/icml/HuYLSX17,DBLP:journals/corr/abs-1709-03010,shen-1,DBLP:journals/corr/abs-1805-11749}.

Despite the widespread usage of beam search for neural text generation, it has long been observed that its outputs are lacking in diversity. Several efforts have been made to provide diverse outputs for generation tasks, such as dialogue~\citep{LiGBGD15} and machine translation~\citep{N12-1059,gimpel-EtAl:2013:EMNLP,Li2016MutualIA}. Diverse beam search~\citep{VijayakumarCSSL16} produces a list of outputs with a diversity-augmented objective. \citet{ippolito-etal-2019-comparison} compare several methods for producing a set of diverse outputs from conditional language models. We leave a careful comparison to such algorithms to future work.

\section{Task Description and Definitions}
Given a story context and a control attribute value, our goal is to generate a story continuation that: (1) conforms to the given attribute value, (2) is relevant to the
story context, and (3) is complementary to continuations with other control attribute values, thereby providing a diverse set of continuations when used with multiple attribute values.

We use $x = \langle x_1, x_2,..., x_{|x|}\rangle$ to denote a story context and $y = \langle y_1, y_2,..., y_{|y|}\rangle$ to denote a story continuation. The last token $y_{|y|}$ is assumed to be $\langle \mathit{eos}\rangle$. We develop a framework to model tuples $(x, l, y)$, where $l$ is a control attribute.
The control attribute represents a characteristic of the continuation, such as its length, sentiment, automatically-induced cluster, verbal predicate, or set of semantic frames. Table \ref{tab:task} shows several examples of control attributes and generated continuations corresponding to them from our model.

redpajama_set_name: RedPajamaArXiv


text:
Husband murdered wife in chip shop by throwing boiling fat over her in row over burnt fish, court hears
Mavis Bran Credit: Media Wales
11 November 2019 • 11:51pm
A husband burned his wife of 30 years to death at their chip shop by pouring scalding hot cooking oil over her, a court has heard.
Geoffrey Bran, 71, allegedly murdered his wife Mavis Bran, 69, with a deep fat fryer at the Chipoteria in Hermon, Carmarthenshire, following arguments between the pair.
On Monday Swansea Crown Court was told Mrs Bran suffered severe burns to her body after her husband allegedly pushed or threw one of three fryers on top of her.
Mrs Bran died in hospital six days after the alleged incident in October last year.
Prosecutor Paul Lewis QC said: "It is the prosecution case in a nutshell that the terrible burns Mrs Bran suffered were not the result of an accident, but were caused by her husband deliberately pushing or throwing over her a deep fat fryer which contained scalding oil."
Geoffrey Bran Credit: Wales News Service
The couple, who had no children together, owned a small number of commercial proprieties and businesses around South Wales, and opened the Chipoteria together in January 2018 in a cabin situated next to their home in the small West Wales village.
The court heard that financial pressures of their various businesses would sometimes put strain on their marriage leading to arguments about money.
People who knew the couple were said to have described them as having "short tempers" and "always argued, swearing and shouting at each other".
Mr Lewis said in the months leading up to her death a friend of Mrs Bran had noticed their relationship had been "deteriorating", with Mrs Bran phoning the friend in tears saying "he's getting nasty".
Gareth Davies, another friend of the couple who lived with them, said on the afternoon of October 23 last year Mrs Bran returned home naked from the waist up shouting "I've been burned" and "I've had boiling hot fat all over me".
He described her upper body as "crimson" and that her skin was peeling off her wrists.
Chipoteria chip shop in Carmarthenshire
Despite her injuries Mrs Bran told Mr Davies to fetch more fish for customers and declined an ambulance.
Bran, who was sporting a cut above his eye, told Mr Davies his wife had been cooking fish when she slipped and grabbed the fryer, causing it to come out of the holder and spill over her.
But Mrs Bran's friend, Caroline Morgan, said she received a call from her around 1.15pm in which Mrs Bran screamed: "Geoff has thrown boiling oil over me. Please get here, I need you now, help."
Miss Morgan said she found Mrs Bran "shocked" and "shaking" and told Mr Davies to call for an ambulance, before telling Bran, who was still serving customers, to shut the chip shop.
Mrs Bran allegedly told Miss Morgan of her husband: "I was nagging him and he flipped."
She was taken by air ambulance to the burns unit at Morriston Hospital in Swansea, where doctors found she has suffered burns to 46% of her body.
Mrs Bran later developed sepsis and hypothermia and underwent surgery to remove some of her burned skin but died from multi-organ failure at the hospital six days after the alleged attack.
Flowers left at the gate of the Chipoteria fish and chips shop Credit: Athena Picture Agency
Bran was initially arrested on suspicion of assault and denied attacking his wife, telling police: "She got burned with the chip fryer. She slipped and it came off the top and went over her. Don't ask."
A nurse found he had a superficial cut to the front of his head and to the front of his neck, as well as a scuff abrasion to his right forearm.
He later said in interview his wife had become "upset and agitated" because a fryer in their van was dirty and she became angry at him because four fish had been spoiled, before adding: "She can lose all reason."
The accused said his wife threw the spoiled fish in a temper, causing hot fat to fly through the air and lead to her pulling the fryer unit down on top of herself.
Bran, from Hermon, denies murder.

redpajama_set_name: RedPajamaCommonCrawl


text:
2015 Cadillac Escalade Platinum Revealed, Priced From $90,270
Viknesh Vijayenthiran August 12, 2014 Comment Now!
The range-topping Platinum version of the latest Cadillac Escalade has been revealed, and will join the rest of the 2015 Escalade range in showrooms in the fourth quarter of the year, priced from $90,270 with a $995 destination charge tacked on. The regular Escalade starts at $73,965, including the same destination charge.
WATCH: Mercedes-Benz G63 AMG 6x6 Terrorizes Supercars At Zandvoort: Video
The premium paid for the Escalade Platinum gets you a number of niceties, including hand-crafted luxury items for both the exterior and cabin. On the outside, Escalade Platinum models ride on 22-inch wheels and the model is distinguished by chrome accents, Platinum exterior badging, and a unique mesh pattern for the front grille.
Inside, the first- and second-row seats are wrapped in Nappa semi-aniline leather, while the dashboard, upper door panels and center console are clad in hand-stitched and hand-wrapped leather. The headliner features a sueded microfiber lining, and customers can further personalize the cabin with exotic wood accents.
Heated and cooled 18-way power front seats are standard, and include a massaging function on the driver's seat. There are a number entertainment screens, too, and a cooled center console compartment to keep your drinks chilly. Finally, Escalade Platinum models also include premium floor mats and Platinum-branded door sill plates.
MUST SEE: KiraVan Is The World's Most Insane RV
All Escalades are powered by the same 6.2-liter V-8 engine, which for 2015 is rated at 420 horsepower and 460 pound-feet of torque. The engine is paired with a new eight-speed automatic transmission and either spins the rear wheels or all four (four-wheel-drive models also get a two-speed transfer case). All Escalades also come with high-speed 4G LTE connectivity, a surround view camera and Cadillac's new crest design which does away with the classic laurel wreath.
High-Res Gallery: 2015 Cadillac Escalade Platinum
2015 Cadillac Escalade News Cadillac News Car Prices Luxury Cars SUVs
Volvo Presents New XC90's Modular SPA Platform
Volvo Reveals 'Face' Of New XC90 In Latest Teaser
Navdy Head-Up Display Is Like Driving In The Future
Cadillac Drops Plans For Crossover With Third-Row Seats: Report
Used 2023 Cadillac Escalade
Used 2023 BMW X7
Used 2023 GMC Yukon
2024 Ford Edge for China spied Spy Shots January 27, 2023


text:
St. Patrick's Cathedral, Dublin
by David Iliff
St Patrick's Cathedral, Dublin was established in 1191 and the building you can visit today originates from c. 1220.
Based on Wikipedia content that has been reviewed, edited, and republished. Original image by David Iliff. Uploaded by Wanda Marcussen, published on 22 June 2021 under the following license: Creative Commons Attribution-NonCommercial-ShareAlike. This license lets others remix, tweak, and build upon this content non-commercially, as long as they credit the author and license their new creations under the identical terms. When republishing on the web a hyperlink back to the original content source URL must be included. Please note that content linked from this page may have different licensing terms.
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Iliff, D. (2021, June 22). St. Patrick's Cathedral, Dublin. World History Encyclopedia. Retrieved from https://www.worldhistory.org/image/14333/st-patricks-cathedral-dublin/
Iliff, David. "St. Patrick's Cathedral, Dublin." World History Encyclopedia. Last modified June 22, 2021. https://www.worldhistory.org/image/14333/st-patricks-cathedral-dublin/.
Iliff, David. "St. Patrick's Cathedral, Dublin." World History Encyclopedia. World History Encyclopedia, 22 Jun 2021. Web. 20 Jan 2022.
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ARE YOU THE NEXT KING & QUEEN OF USHAKA FOR 2019/2020?
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You heard it through the grapevine – the TOPS at SPAR Wine Show is back in Durban next week
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Splashy Fen Goes Sustainable
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WATERSHED LIVE IN BALLITO
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Eight Larger-Than-Life Monster Jam® Trucks head to SA
Outdoor Cinema on the Beach – Cimema on the Sand
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5 Things To Do Before You Go To Splashy Fen
JEREMY LOOPS BRINGS THE BEAT WITH ANOTHER SOLD OUT SHOW IN BALLITO
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PJ POWERS HEADS UP THE LEGENDS STAGE AT SPLASHY FEN 2019
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Diarise the dates for Decorex Durban now!
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Save the date for Africa's largest Easter Egg Hunt only at uShaka!
Splashy Fen 2019 Line-Up Announced
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A Family Night Out Under the Stars
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WIN tickets to Music at Makaranga
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I Heart Market Dates to Diarise
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Durban Cheese & Wine Festival
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Salon Prive: Suncoast Casino Durban
Enjoy the vibe at Umhlanga's very own festival!
Mother's Day Spoils at Brigit Filmer Spa
Fitch & Leedes Gin and Tonic Festival 2018
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Camelot at Elizabeth Sneddon Theatre
Durban: Bootcamp for Ladies with Gi Jane
Bubbly Fest at Litchi Orchard 2018
Beverly Hills Launch their new GORGEOUS Sparkling MCC and the bespoke 1701Nougat
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Granada Square outdoor movie night this April 30th
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WIN Tickets to the La Lucia Mall Fine Wine Prive
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REVIEW: Splashy Fen Festival 2018
WIN! Tops at SPAR Wine Show
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Taste Away Craft Festival
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Jeremy Loops Brings the Waves to Marriot Ballito Beats
Jeremy Loops LIVE at Marriott Beats Music Series
Santana Performs in Durban for the First Time
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Talk Sign 2018 Launch a Success
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Valentine's Day Holiday Express Train Trip
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Spoil your other half with a romantic experience at Karkloof Safari Villas
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Muddy Princess 2018 at Giba Gorge
Festive Fun on The Holiday Express
SUMMER'S ON AT THE OYSTER BOX
Giving Back with Toyota 86/BRZ Car Club
REVIEW: Sinbad the Sailor at Elizabeth Sneddon
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WIN Tickets to the Litchi Orchard Rum Fest
Dolphins by Starlight 2017 at uShaka Marine World
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All Aboard for Salt Rock on The Holiday Express
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Starbucks prepares for its Durban opening!
Cornubia Mall Organic Market 19 November
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YUSUF / CAT STEVENS RELEASES HIS NEW ALBUM 'THE LAUGHING APPLE' BEFORE TOURING SA
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Movies in The Park Coming to Umhlanga
Fashion is going off the rails this November at Musgrave Centre
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Catch a Train Ride to Salt Rock for Your Year End Event
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Gallery: Nitro Circus Live – Durban 2017
October Beer Fest at Granada Square
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Tsogo Sun Amashova 2017 Results
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Gateway's Celebrity Chef Showdown is Back!
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Get ready for the Spirit of Tsogo Sun Amashova
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BOUNCE: Indoor Trampoline Park Opens at Cornubia
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MTN donates R500,000 to flood relief
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Vega Durban: FREE Doodle Evening
NOW OPEN: Mindgame Escape Rooms, Umhlanga
Who will bring the bras?
Last chance to see Bastille live in SA
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FREE movies at Cornubia Mall on Friday 29 September
The Countdown Begins: NITRO CIRCUS LIVE
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The Color Run Durban 2017
The Holiday Express Train Ride Experience
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Mrs Vietnam is crowned Mrs Universe in a glitzy event in Durban
Dance Away at Secret Sunrise
WIN Tickets to Muddy Princess at Giba Gorge
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Mercury Wine Week 2017
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The Holiday Express – Train to Shongweni or Scottburgh
A FRESH APPROACH TO FUN – BUBBLES, BEERS AND BURGERS
The Lion King comes to the Botanic Gardens
The Oyster Box Upcoming Events
ECR House & Garden Show
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The Wonderful World of Disney On Ice now on in SA, skating into Durban for the first time!
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NOW OPEN: Escape Hunt Experience Umhlanga
Gateway's Vodacom Durban July Preview Fashion Show
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Splashy Fen 2017: Recap & Photos
NEW OPENING: Nu Metro at Ballito Junction
Splashy Fen 13 – 16 April 2017
LOCAL BAND, THE REMEDY PROJECT TO PERFORM AT SUNCOAST'S SUNDAY BEATS!
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Bubbly Fest 2017 at The Litchi Orchard
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NEW STORE OPENING: Krispy Kreme Gateway
Talk Sign Day 2017
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Summer Fitness with Adventure Boot Camp
Float Away with Lube and Tube 2017
New Year's Eve at The Oyster Box Hotel
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7 Christmas Markets to Visit in Durban
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Brands Taking Part in Black Friday, South Africa
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WIN Tickets to Carols by Candlelight
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The Keyboard Killers Show
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WICKEDLY ODD – The Night Garden
Kinky Summer Festival for New Year's
IT'S ORCHID SHOWTIME IN NOVEMBER
Gateway Gives Back – Charity Dinner & Auction
SUNCOAST IS CALLING ON SPRINGBOK SUPPORTERS TO GET BEHIND THE BOKKE AT BOKTOWN
Goodluck, Rubber Duc and Pascal & Pearce to ignite the Durban Botanical Gardens
FROM GUS BROWN ROAD TO GUS BROWN BAND
A MASTERCHEF CELEBRATION AT THIS YEAR'S DURBAN GOOD FOOD & WINE SHOW
Roar for the cause at Sisters with Blisters 2016
The Monster Ball at uShaka Marine World
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Which Oktoberfest is Oktoberbest?
HIGH ADVENTURE, HIGH TEAS AND NON-STOP ENTERTAINMENT AT THE OYSTER BOX
Mi Casa bring their soulful sound back to the Old Mutual Music at the Lake stage
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A Fashion Feast at 2016 Fashion Recognition Awards
Kwazulu Natal choirs ready to compete for choral music's biggest prize
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8 top SA artists for Huawei Durban Day with East Coast Radio
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INVESTING IN LOCAL TALENT
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Bubble Soccer Comes to Durban
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GRASSY SPARK CONFIRMED AS OPENING BAND
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White Mountain Festival 2016
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BEYOND THREADS AT THE DURBAN FASHION FAIR!
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VESPA Rides with Spiga and Dante
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GROOVE TO THE JAZZY SOUNDS OF THE RENAE SOOBIAH TRIO AT SUNCOAST
In celebration of Superwomen: heroines will rise!
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The big blues set to rock the Durban Botanic Gardens
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SA on Tap Craft Beer & Music Festival
BIG CONCERTS: UB40 with Ali, Astro & Mickey announce SA Tour
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FOOD FAMILY FUN at Shongweni Market
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Durban Port Festival Back By Popular Demand
9 Things to do in Durban that are FREE
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Wine-Pairing Dinner at Big Easy
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International selling artists to exhibit at upcoming Art in the Park
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TI Concert – Review
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The Parlotones break new ground with Orchestrated tour at the Old Mutual Music at the Lake
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HIT ALL-HUMAN AUSSIE CIRCUS SPECTACULAR IS COMING TO DURBAN FOR THE SCHOOL HOLIDAYS
20 Things to do in Durban
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Locals raise funds for Children with Cancer while attempting Guinness World Record
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WIN: NWJ Bridal and Events Show
WIN: Open air cinema your ticket to romance this Valentines
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DINOSAUR'S ARE COMING TO DURBAN THIS NOVEMBER
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DUT Annual Fashion Show
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SUCCESSFUL WOMEN EMPOWERMENT EVENT HOSTED BY VARSITY COLLEGE
Gateway's Celebrity Chef Showdown 2015
SA's top artists all set to rock Durban
Rodriguez Back By Popular Demand
Taste of Durban is Back and Better Than Ever
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Inaugural DBN X FST (Durban X Fest) Starts 27 June
Local Beyrick De Vries Wins 2nd Quiksilver Surf Event in Durban.
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City of Durban Ready for 90th Comrades Marathon
PHARRELL WILLIAMS SOUTH AFRICAN TOUR DATES ANNOUNCED
Thousands walk Durban together at the Discovery East Coast Radio Big Walk!
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Quiksilver Surf Series Set For Umhlanga Rocks!!
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Legacy of LEGiT continues with Punk & Ivy for Winter 2015!
8 expert tips to make this your best walk yet!
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uShaka crowns their winners for 2015!
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Concerts SA Music Mobility Fund 2015 winners announced
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Royal Riot 2015 to rock The Pavilion Shopping Centre this Saturday!
Festival the Market
10 WAYS TO PREP FOR THE DISCOVERY EAST COAST RADIO BIG WALK
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Whisky Live Showrooms announced for Durban, Pretoria and Cape Town
Set Sail for a Unique Mountain Biking Experience on Lake Jozini
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Madame Zingara Extended Dates
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We Survived Valentine's Day… Did You?
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Hirsch's Springfield Women of the Year Networking
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The Script tells KZN fans: It's going to be amazing!
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Movember Retro Surf Event Promises Seventies Fun Day Out
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Global music icons Pet Shop Boys are coming to Durban!
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Local Leg of Banff Mountain Film Festival World Tour Celebrates Spirit of Adventure
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Michael Bublé is coming to Durban!
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Project Enza commemorates World Tourism Day
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Tops at Spar Bierfest Durban 2014
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South Africa's Super-rich Attempts to Break a World-Record for Charity
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MESSAGE FROM THE MAYOR (OF BIERFEST)
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National Geocacher Gathering to take in place in Kwazulu Natal in October 2014
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Beach Bums Spring Break Party
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SA Bridal Fashion Week
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What to expect at the 11th Durban International Boat & Lifestyle Show
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Durban July Preview Fashion Show
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It's baaaack – the 35th Durban International Film Festival
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Support the Rhino Rehabilitation Centre
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Just Jinjer at the Botanical Gardens
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Summer Heat Model & Talent Search
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SA's biggest action sports athletes take to the ramp with Mountain Dew
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Splashy Fen #25 the 'must-do' event of the year
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NITRO CIRCUS LIVE WOWS DURBAN!
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Durban Social Paintball Tournament
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SUNDAY BEAT ENTERTAINMENT – Taxi Violence
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NWJ Bridal and Events Show
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Nova Music Festival
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Durban Dirt Challenge
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Nitro Circus Live – Interviews
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5FM "Love Sucks" Valentine's Day celebrations
Buzzart is Back & it's All Green
The KZNSA Gallery's biggest local-only Christmas fair is open… and ru..
Save the Dates for The Oyster Box 2020
2020 is almost here and The Oyster Box Hotel has oodles of romantic dinners..
Unique Magic Show at Seabrooke's Theatre
A unique magic show, Mysteries, is opening at Seabrooke's Theatre in Durban..
Celebrate a South African Christmas at uShaka Marine World
Celebrate Christmas this year with Durban's most festive family – the dolph..
"I'm disappointed that I wasn't disappointed." These were the words spoken ..
Affordable Family Vaycay: Your Gooderson Adventure Awaits
We all know money is tight and holidays aren't something many of us can spl..
CALLING ALL KZN SURFERS TO ENTER!
It is nearly Christmas, and time for the annual LIZZARD HURRICANE KZN SUR..
MATTHEW MOLE LIGHTS UP OUR FRIDAY!
An excitable crowd gathered at Sugar Rush Park this Friday evening for the ..
The thing about trains… it doesn't matter where they're going. What matters..
Gallery: Tops at Spar Bierfest 2015!
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Durban Hit By Massive Waves
Review: Goldfish Live in Durban
Durban has a history of attracting rain when big live events happen, and th..
TOURIST IN OUR OWN CITY: HILTON DURBAN
Durbanite recently had the pleasure of staying at the Hilton Durban. We sub..
WIN WIN WIN: Body20 – Glenashley
Are you tired of attending a regular gym? Feel like you are not making any ..
Durbanite
27, 28 June & 2 July
Bay Of Plenty Beach, Durban
Introducing DBN X FST, a core skate and surf lifestyle festival established in the birthplace of South African surfing; the Durban beachfront. Presented with the support of eThekwini Municipality, this event helps mark 50 years of surfing for Durban. DBN X FST is a collaborative effort with local brands to showcase the power of the Durban surf, skate and related industries, with events on the 27, 28 June and 2 July at the Bay of Plenty.
The festival will play host to a range of events including: Kimberley Diamond Cup Qualifiers, Vans Skateboarding for Hope, Roxy Yoga and Learn to Surf, DBN X TAG event, O'Neill and G-Force Jordy Smith Grom Invitational, Volcom Airstrike, Channel Island surfboard demos, Surf Afrika comp, Gunslinger long-board skate clinic, Fox BMX Jam, I HEART Market, music entertainment, films and more.
Partner brands include: Quiksilver, Roxy, DC, Skullcandy, Rip Curl, Holmes Brothers, Gunslinger, Vans, Fox, Volcom, O'Neill and Red Bull.
Surfing takes centre stage at the historic Bay of Plenty beach as DBN X FST hosts a number of epic surfing events.
Saturday 27 June:
O'Neill and Gee Star Surfboards are proud to announce the Jordy Smith Super Grom Invitational event. World Surf League competitor Jordy Smith will be inviting 28 of KZN's best up and coming surfers from 6 – 14 years of age, to compete for a once in a lifetime prize, including an air ticket to watch the J-Bay Open, O'Neill wetsuit and clothing package, as well as a G-Star Surfboard! The winner will also get a VIP Surfers Guest Pass to the J-Bay Open and will be able to rub shoulders with the best surfers in the world at Super Tubes, J-Bay, while he supports Jordy in the event. The 28 surfers have been announced and the event will take place at The Bay of Plenty on the 27 June from7:30am – 12pm as part of the DBN X FST. Jordy will be on the beach to judge the final and spend time with the surfers.
The DBN X TAG surfing event will see teams of 4, made up of professional and development surfers, who go head to head in an "old skool" tag team surf competition format. The winning team will walk away with R10 000 in prize money.
The Volcom Airstrike will invite top aerial surfers to take to the air for a chance to win R5000 in prize money.
The Quiksilver Whipped Session will see some of South Africa's best and most innovative surfers teaming up with the Red Bull jet-ski team where man and machine meet for an explosion of aerial maneuvers and high impact surfing. Surfers will be whipped in to waves at pace by Red Bull jet-skis to see who can who can boost the highest and shred the hardest.
Sunday 28 June:
The Holmes Brothers present the Modern Retro Surf Jam where style points are everything. Surfers are invited to bring along their fish/twin-fin/single-fin/log/ bonzers/inverted rail boards and stand a chance to win a year's supply of Holmes Brothers gear. To enter email holmesbros3@telkomsa.net
Roxy Learn 2 Surf will be holding free surf lessons for ladies on Sunday the 28th June from 9am-1pm.
Thursday 2 July:
Surf Afrika is a development surf competition that will be run by the Surfers Not Street Children organisation. This event will give up-and-coming surfers from disadvantaged backgrounds a chance to showcase their talents for all to see.
The main attraction in the skateboarding segment of DBN X FST is the Kimberley Diamond Cup (KDC) grand slam qualifying rounds. On Saturday 27 June the stage will be set for a showdown of Durban's best skateboarders to see who will take a share of R50 000 prize money for this event, and also to determine which skaters will go on to compete in the main KDC event later in the year with a chance to win a staggering US$100 000 in prize money.
Sunday 28 June will see Skateboarding for Hope presented by Vans where top local skaters will hold demos, teach skateboarding and hand out skateboards to disadvantaged youth.
Gunslinger will host long-board skate clinics daily from 10:00am – 11:00am, allowing the public a chance to get a taste of the adrenalin-fuelled action. Beachgoers are invited to test ride new boards and get tips on how to better one's skate skills.
The Durban Beach skate park will be alive with action on Sunday the 28th June for the Fox BMX Jam brought to you by BMX Direct. SA's best BMX athletes will be competing in a "Jam style" format from 11am to 3pm to see who can throw down the biggest, most technical and stylish tricks on their BMX bikes. The winner walks away with R2000. Freestyle BMX riding is more of an art form than a sport and the rider who creatively makes the best use of the park, whilst performing innovative tricks that are flawlessly linked together with style and finesse will be deemed the winner on the day.
Roxy presents a free yoga session from 8am – 9am on Saturday 27th. All are welcome; interested members of the public should bring a towel or a yoga mat.
I HEART MARKET
The ever-exciting I HEART Market will be serving up some tasty treats and awesome crafts on the lawns on Saturday from 9am – 2pm.
Each day from 18:00 – 20:00 will feature a selection of local and international adventure and surf films. Films include: Now Now featuring Jordy Smith, Sampler from Dane Reynolds, On Any Sunday courtesy of Skullcandy, A selection of short surf films from local videographer Mathew Gardner, a Faces of Africadoccie about Ntando Msibi directed by Michael Cross, a preview of the film Uncharted featuring Twiggy Baker and the short film Lost4Word by Benji Brand.
Saturday will be capped off with local young-gun groove rock band Black Math at 8pm, followed by JHB surf-rock band The Moths at 9pm to drive it all home.
Sunday's sounds will be made by local soul-rock group Nje from 4pm and followed by the natural grooves of the Hinds Brothers at 5pm to cool it all off.
For more information check out www.facebook.com/dbnxfst or www.dbnxfst.co.za or mail letsbelocal@gmail.com
DBN x FST is organised by letsbelocal with the support of Ethekwini Municipality, Durban Tourism and in partnership with Ethekwini Surfriders Association, Surfers Not Street Children, Rip Curl, O'Neill, Quiksilver, Skullcandy, Holmes Brothers, Red Bull, Gunslinger, Roxy, Vans, Fox, Volcom and el Jimador.
EntertainmentEvents
2015Black MathDBX x FSTDC ShoesdurbandurbaniteExtremeFestivalGee Star SurfboardsHinds BrothersHolmes BrothersI Heat MarketKimberley Diamond CupLets Be LocalLetsbelocalNjeO'NeillQuiksilverRed BullRoxyskateboardingSurfingThe MothsX FestYoga
Durbanite is a company established to highlight everything Durban has to offer. By creating an online platform, we serve to act as a Durban Guide, providing not only Durbanites but also other visitors all the information they need to know about the city.
6 Week Challenge Giveaway: GETFIT Umhlanga
The Challenge - Week One
Review: The Royal Palm Hotel in Umhlanga, Durban 9.6
Review: The Canelands – de Charmoy Estate 9
REVIEW: Endless Horizons Boutique Hotel 8.8
Review: Riboville, Waterfall 9.2
Review: Big Easy Wine Bar & Grill Durban by Ernie Els 10
PROPERTY: Umhlanga Arch presents KZN's first European-inspired high street - The Multiply Group on 09 Oct
Umhlanga Arch presents KZN's first European-inspired high street
[…] Source: Durbanite […]
Mohsinah Pieterse on 14 Mar
My daughter is crazy over chocolates and my son is adventurous, they dad is coming home after 3 months these tickets would be perfect bonding
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Heat 1 tbsp of the oil in a flameproof dish over a medium heat. Add the celery and cook for 10 mins until softened, then stir in the free flour and cook for a further 2 mins.
Add the carrots, pumpkin, garlic infused oil and rosemary, and cook for 5 mins, stirring regularly, until they begin to soften.
Tip the tomatoes into the vegetables along with a can full of water.
Cover with a lid and simmer for 10 mins, then remove the lid and cook for 10-15 mins more, until the sauce has thickened and the vegetables are cooked.
Meanwhile, boil the potatoes for 10-15 mins until tender.
Drain, then place back in the saucepan and mash. Stir through enough lactose free milk to reach a fairly soft consistency, then add the lactose free cream, parsley and season.
Heat the grill. Spoon the hot vegetable mix into a pie dish, top with the mash and drag a fork lightly over the surface.
Place under the grill for a few mins until the top is crisp golden brown.


text:
Boris Johnson leads Conservative resistance against Clegg's mansion taxes
We need strategic clarity. What poses the greater danger to Britain – Iranian aggression abroad or Sunni extremism here?
As 2020 begins, we look back on ten years in which Tories first led the movement for austerity…and then against it
The year in which the British people forced the pro-Remain Ascendancy to "come to heel"
Brexit. The story of 2019 in a single paragraph.
By Matthew Barrett
Follow Matthew on Twitter.
"Boris isn't shy about lobbing grenades across the political landscape", as my colleague Peter Hoskin noted earlier this week. Sometimes he seems to want to cause controversy, sometimes he seems to want to grab attention – and sometimes, as Tory activists admire him for, he says what senior Conservatives cannot.
The Daily Telegraph reports that the Mayor of London has called Nick Clegg's mansion tax plans "a non-starter [and] he knows it". That's not particularly incendiary by his standards, but it is the first big intervention on the mansion tax after an oddly mute few days on the issue from senior Tories, including David Cameron, and it will annoy the Treasury, who the Telegraph say are working on plans for the new tax. Mr Johnson's full quote is:
"I like Nick Clegg but he can't be serious. These proposals are a non-starter. He knows it. I know it. The idea of a mansion tax is crazy. The idea of a mansion tax by the back door through vastly inflated council tax bills is nonsense. These taxes will disproportionately hit London and Londoners, penalising people simply because of circumstance, trapping people who in many cases are cash poor. London is the motor of the UK economy; kicking it hard makes no sense at all."
Mr Johnson is likely to have four factors playing on his mind when he made those comments. Firstly, he will know the strength of feeling amongst some senior Tories. The Telegraph names Chris Grayling, the Justice Secretary, Eric Pickles, the Secretary of State for Communities and Local Government (who will presumably have some responsibility for administering the policy), and Grant Shapps, the new Chairman, as opponents of the policy so far.
Mr Johnson will also be doing his duty as he sees it, and standing up for London. As he suggests, many of the homes affected by the policy would be in London and the South East, where, the Telegraph notes, the average detached home is worth over £500,000, suggesting most detached homes in London will need to be inspected if Clegg's planned threshold is £1million.
Mr Johnson's third and fourth calculations will be pleasing to the grassroots: standing up for those likely to be personally affected by the policy, and attacking Clegg in general. Internal Conservative research suggests that a million home owners will have to have their properties reassessed under the plans, including many pensioners who don't have a great deal of disposable income, but who have seen the price of their homes rise over the years. Many of the aforementioned Southern homeowners and pensioners, will be core Conservative voters, who will take heart from Mr Johnson's comments.
On a side-note, the Financial Times (£) today details the extent of the "grey vote"'s presence in Conservative seats – after Nick Clegg also suggested means-testing winter fuel payments, free bus passes and free TV licences for pensioners. The FT reports:
"On a regional level, almost 60 per cent of the taxpaying elderly with pension income over £50,000 live in London, the south-east, the south-west and East Anglia, a much higher proportion than the 45 per cent of taxpaying pensioners that live in regions."
Whether right or wrong, policies like these which hit pensioners hardest will not make life easy for many Conservative MPs.
Boris Johnson (Mayor) Economic policy Liberal Democrats Tax and Spending Welfare Reform


text:
"A Historic Supermajority": New York Democrats Declare Sweeping Victory in State Senate
By Morgan McKay New York State
PUBLISHED 9:14 PM ET Nov. 23, 2020 PUBLISHED 9:14 PM EST Nov. 23, 2020
It's a story this election cycle that has already played out numerous times across the country.
Preliminary election results on November 4 showed New York state Senate Democrats trailing behind in numerous key races.
However, absentee ballots have weighed heavily in Democrats' favor this year, helping them to slowly gain ground across the state when results were finally tallied.
So on Monday, state Senate Democrats triumphantly declared that they will be walking into Senate chambers next year with a supermajority for the first time in history.
"I'm announcing that in 2021, we will begin our session with a historic supermajority," Senate Majority Leader Andrea Stewart-Cousins said. "And it will also be the biggest Senate majority conference in the history of New York state."
BREAKING: NYS Senate Majority Leader @AndreaSCousins announces the Senate Democrats will start the 2021 session with a supermajority of 42 members @CapitalTonight pic.twitter.com/MLKOX0ToFz
— Morgan Mckay (@morganfmckay) November 23, 2020
At this point, Senate Democrats have definitively secured 41 seats and feel confident enough in their lead in the 40th Senate District race to put them over the edge to 42 members.
This race, along with two other races, Senate District 42 and 50, have not yet been officially called.
But Democrats said that Senator Pete Harckham will be declared the winner over Republican challenger Rob Astorino on Tuesday at the latest.
"On election night, many wrote us off," Stewart-Cousins commented. "They said it was a big red wave and they were already predicting our shrinking majority. And as we know, the red wave turned out to really be more of a red mirage."
The day after the election, Senate Republican Minority Leader Rob Ortt held a press conference touting early Republican leads, saying at the time, "What you saw last night was a repudiation of one-party rule. It was repudiation. There's no other way to slice it. In a blue state where President Biden won, where you have record turnout, Republicans picked up seats last night."
A Senate Democratic supermajority tips the scales of power in several ways.
Democrats will now have the ability to override any vetoes that Governor Andrew Cuomo issues and be in charge of redrawing district lines.
Deputy Senate Majority Leader @SenGianaris reading quotes from Senate Minority Leader @SenatorOrtt who held a press conference the day after the election saying that Republicans were going to win heavy after early results showed preliminary leads
A few more moderate Democrats could have the option to not always vote on party lines.
And this also means the Democratic Party is expanding geographically as well, allowing upstate members to hold more influence.
However, Governor Cuomo cast doubt on how much power this supermajority will wield.
"The way the state government really works is through the budget," Governor Cuomo said. "All of the main things are done in the budget. Supermajority or not, it doesn't really make a difference."
When asked if the Senate Democrats will challenge Cuomo on any of his policies, Stewart-Cousins said "we generally agree, so I don't see that this would be an issue. But it certainly is a potential."
.@AndreaSCousins says they are "considering" holding session next month. "Nothing is off the table"
Cuomo and state Democratic Party Chairman Jay Jacobs both offered up warnings to the state Democrats after the election, saying they needed to tone down their "progressive rhetoric."
Deputy Senate Majority Leader Mike Gianaris took a moment on Monday to address these comments, saying "a message was sent by the voters. Let's be clear about that. Let's be clear about something else. The Republicans did not beat the Democrats on the messaging as some have said. Obviously, they took their best shot."
Senate Minority Leader Rob Ortt sent a statement in response to this press conference, writing "our sitting members and candidates competed in every area of the state. They ran on a message that resonated with voters who chose to reject the radical agenda of Albany Democrats. They will be an even louder voice. If New Yorkers thought One-Party Control was bad, more Democrats in the New York State Senate will usher in a new era of radical, increasingly socialist policies, unlike anything before seen in this state."
State Democrats still have an uphill battle before them this next legislative session, with a looming budget deficit that has been pushed down the road in hopes of another federal stimulus package.
Stewart-Cousins acknowledged that they will not be able to wait forever on this, not ruling out any potential revenue raisers such as legalizing recreational marijuana, increased taxes, and mobile sports betting.