We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals. Pre-trained word representations (Mikolov et al., 2013; Pennington et al., 2014) are a key component in many neural language understanding models. However, learning high quality representations can be challenging. They should ideally model both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). In this paper, we introduce a new type of deep contextualized word representation that directly addresses both challenges, can be easily integrated into existing models, and significantly improves the state of the art in every considered case across a range of challenging language understanding problems. Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence. We use vectors derived from a bidirectional LSTM that is trained with a coupled language model (LM) objective on a large text corpus. For this reason, we call them ELMo (Em-beddings from Language Models) representations. Unlike previous approaches for learning contextualized word vectors (Peters et al., 2017; McCann et al., 2017), ELMo representations are deep, in the sense that they are a function of all of the internal layers of the biLM. More specifically, we learn a linear combination of the vectors stacked above each input word for each end task, which markedly improves performance over just using the top LSTM layer. Combining the internal states in this manner allows for very rich word representations. Using intrinsic evaluations, we show that the higher-level LSTM states capture context-dependent aspects of word meaning (e.g., they can be used without modification to perform well on supervised word sense disambiguation tasks) while lower-level states model aspects of syntax (e.g., they can be used to do part-of-speech tagging). Simultaneously exposing all of these signals is highly beneficial, allowing the learned models select the types of semi-supervision that are most useful for each end task. Extensive experiments demonstrate that ELMo representations work extremely well in practice. We first show that they can be easily added to existing models for six diverse and challenging language understanding problems, including textual entailment, question answering and sentiment analysis. The addition of ELMo representations alone significantly improves the state of the art in every case, including up to 20% relative error reductions. For tasks where direct comparisons are possible, ELMo outperforms CoVe (McCann et al., 2017), which computes contextualized representations using a neural machine translation encoder. Finally, an analysis of both ELMo and CoVe reveals that deep representations outperform 2227 those derived from just the top layer of an LSTM. Our trained models and code are publicly available, and we expect that ELMo will provide similar gains for many other NLP problems. 1 2 Related work Due to their ability to capture syntactic and semantic information of words from large scale unlabeled text, pretrained word vectors (Turian et al., 2010; Mikolov et al., 2013; Pennington et al., 2014) are a standard component of most state-of-the-art NLP architectures, including for question answering (Liu et al., 2017), textual entailment (Chen et al., 2017) and semantic role labeling (He et al., 2017). However, these approaches for learning word vectors only allow a single context-independent representation for each word. Previously proposed methods overcome some of the shortcomings of traditional word vectors by either enriching them with subword information (e.g., Wieting et al., 2016; Bojanowski et al., 2017) or learning separate vectors for each word sense (e.g., Neelakantan et al., 2014). Our approach also benefits from subword units through the use of character convolutions, and we seamlessly incorporate multi-sense information into downstream tasks without explicitly training to predict predefined sense classes. Other recent work has also focused on learning context-dependent representations. context2vec (Melamud et al., 2016) uses a bidirectional Long Short Term Memory (LSTM; Hochreiter and Schmidhuber, 1997) to encode the context around a pivot word. Other approaches for learning contextual embeddings include the pivot word itself in the representation and are computed with the encoder of either a supervised neural machine translation (MT) system (CoVe; McCann et al., 2017) or an unsupervised language model (Peters et al., 2017). Both of these approaches benefit from large datasets, although the MT approach is limited by the size of parallel corpora. In this paper, we take full advantage of access to plentiful monolingual data, and train our biLM on a corpus with approximately 30 million sentences (Chelba et al., 2014). We also generalize these approaches to deep contextual representations, which we show work well across a broad range of diverse NLP tasks. Previous work has also shown that different layers of deep biRNNs encode different types of information. For example, introducing multi-task syntactic supervision (e.g., part-of-speech tags) at the lower levels of a deep LSTM can improve overall performance of higher level tasks such as dependency parsing (Hashimoto et al., 2017) or CCG super tagging (Sgaard and Goldberg, 2016). In an RNN-based encoder-decoder machine translation system, Belinkov et al. (2017) showed that the representations learned at the first layer in a 2-layer LSTM encoder are better at predicting POS tags then second layer. Finally, the top layer of an LSTM for encoding word context (Melamud et al., 2016) has been shown to learn representations of word sense. We show that similar signals are also induced by the modified language model objective of our ELMo representations, and it can be very beneficial to learn models for downstream tasks that mix these different types of semi-supervision. Dai and Le (2015) and Ramachandran et al. (2017) pretrain encoder-decoder pairs using language models and sequence autoencoders and then fine tune with task specific supervision. In contrast, after pretraining the biLM with unlabeled data, we fix the weights and add additional task-specific model capacity, allowing us to leverage large, rich and universal biLM representations for cases where downstream training data size dictates a smaller supervised model. Unlike most widely used word embeddings (Pen-nington et al., 2014), ELMo word representations are functions of the entire input sentence, as described in this section. They are computed on top of two-layer biLMs with character convolutions (Sec. 3.1), as a linear function of the internal network states (Sec. 3.2). This setup allows us to do semi-supervised learning, where the biLM is pretrained at a large scale (Sec. 3.4) and easily incorporated into a wide range of existing neural NLP architectures (Sec. 3.3). Given a sequence of N tokens, ( t 1 , t 2 , ..., t N ) , forward language model computes the probability of the sequence by modeling the probability of to-2228 ken t k given the history ( t 1 , ..., t k  1 ) : Recent state-of-the-art neural language models (Jozefowicz et al., 2016; Melis et al., 2017; Mer-ity et al., 2017) compute a context-independent token representation x LMk (via token embeddings or a CNN over characters) then pass it through L layers of forward LSTMs. At each position k , each LSTM layer outputs a context-dependent representation ! h LMk,j where j = 1 , . . . , L . The top layer LSTM output, ! h LMk,L , is used to predict the next token t k +1 with a Softmax layer. A backward LM is similar to a forward LM, except it runs over the sequence in reverse, predicting the previous token given the future context: p ( t 1 , t 2 , . . . , t N ) = NY k =1 p ( t k | t k +1 , t k +2 , . . . , t N ) . It can be implemented in an analogous way to a forward LM, with each backward LSTM layer j in a L layer deep model producing representations  h LMk,j of t k given ( t k +1 , . . . , t N ) . NX k =1 ( log p ( t k | t 1 , . . . , t k  1 ; x , !  LSTM ,  s ) + log p ( t k | t k +1 , . . . , t N ; x ,   LSTM ,  s ) ) We tie the parameters for both the token representation (  x ) and Softmax layer (  s ) in the forward and backward direction while maintaining separate parameters for the LSTMs in each direction. Overall, this formulation is similar to the approach of Peters et al. (2017), with the exception that we share some weights between directions instead of using completely independent parameters. In the next section, we depart from previous work by introducing a new approach for learning word representations that are a linear combination of the biLM layers. ELMo is a task specific combination of the intermediate layer representations in the biLM. For each token t k , a L -layer biLM computes a set of 2 L + 1 representations R k = { x LMk , ! h LMk,j ,  h LMk,j | j = 1 , . . . , L } = { h LMk,j | j = 0 , . . . , L } , where h LMk, 0 is the token layer and h LMk,j = [ ! h LMk,j ;  h LMk,j ] , for each biLSTM layer. For inclusion in a downstream model, ELMo collapses all layers in R into a single vector, ELMo k = E ( R k ;  e ) . In the simplest case, ELMo just selects the top layer, E ( R k ) = h LMk,L , as in TagLM (Peters et al., 2017) and CoVe (Mc-Cann et al., 2017). More generally, we compute a task specific weighting of all biLM layers: ELMo taskk = E ( R k ; task ) =  task LX j =0 s taskj h LMk,j . (1) In (1), s task are softmax-normalized weights and the scalar parameter  task allows the task model to scale the entire ELMo vector.  is of practical importance to aid the optimization process (see supplemental material for details). Considering that the activations of each biLM layer have a different distribution, in some cases it also helped to apply layer normalization (Ba et al., 2016) to each biLM layer before weighting. Given a pre-trained biLM and a supervised architecture for a target NLP task, it is a simple process to use the biLM to improve the task model. We simply run the biLM and record all of the layer representations for each word. Then, we let the end task model learn a linear combination of these representations, as described below. First consider the lowest layers of the supervised model without the biLM. Most supervised NLP models share a common architecture at the lowest layers, allowing us to add ELMo in a consistent, unified manner. Given a sequence of tokens ( t 1 , . . . , t N ) , it is standard to form a context-independent token representation x k for each token position using pre-trained word embeddings and optionally character-based representations. Then, the model forms a context-sensitive representation h k , typically using either bidirectional RNNs, CNNs, or feed forward networks. To add ELMo to the supervised model, we first freeze the weights of the biLM and then 2229 concatenate the ELMo vector ELMo taskk with x k and pass the ELMo enhanced representation [ x k ; ELMo taskk ] into the task RNN. For some tasks (e.g., SNLI, SQuAD), we observe further improvements by also including ELMo at the output of the task RNN by introducing another set of output specific linear weights and replacing h k with [ h k ; ELMo taskk ] . As the remainder of the supervised model remains unchanged, these additions can happen within the context of more complex neural models. For example, see the SNLI experiments in Sec. 4 where a bi-attention layer follows the biLSTMs, or the coreference resolution experiments where a clustering model is layered on top of the biLSTMs. Finally, we found it beneficial to add a moderate amount of dropout to ELMo (Srivastava et al., 2014) and in some cases to regularize the ELMo weights by adding  k w k 2 2 to the loss. This imposes an inductive bias on the ELMo weights to stay close to an average of all biLM layers. The pre-trained biLMs in this paper are similar to the architectures in Jozefowicz et al. (2016) and Kim et al. (2015), but modified to support joint training of both directions and add a residual connection between LSTM layers. We focus on large scale biLMs in this work, as Peters et al. (2017) highlighted the importance of using biLMs over forward-only LMs and large scale training. To balance overall language model perplexity with model size and computational requirements for downstream tasks while maintaining a purely character-based input representation, we halved all embedding and hidden dimensions from the single best model CNN-BIG-LSTM in Jozefowicz et al. (2016). The final model uses L = 2 biLSTM layers with 4096 units and 512 dimension projections and a residual connection from the first to second layer. The context insensitive type representation uses 2048 character n-gram convolutional filters followed by two highway layers (Srivastava et al., 2015) and a linear projection down to a 512 representation. As a result, the biLM provides three layers of representations for each input token, including those outside the training set due to the purely character input. In contrast, traditional word embedding methods only provide one layer of representation for tokens in a fixed vocabulary. After training for 10 epochs on the 1B Word Benchmark (Chelba et al., 2014), the average forward and backward perplexities is 39.7, compared to 30.0 for the forward CNN-BIG-LSTM . Generally, we found the forward and backward perplexities to be approximately equal, with the backward value slightly lower. Once pretrained, the biLM can compute representations for any task. In some cases, fine tuning the biLM on domain specific data leads to signifi-cant drops in perplexity and an increase in downstream task performance. This can be seen as a type of domain transfer for the biLM. As a result, in most cases we used a fine-tuned biLM in the downstream task. See supplemental material for details. Table 1 shows the performance of ELMo across a diverse set of six benchmark NLP tasks. In every task considered, simply adding ELMo establishes a new state-of-the-art result, with relative error reductions ranging from 6 20% over strong base models. This is a very general result across a diverse set model architectures and language understanding tasks. In the remainder of this section we provide high-level sketches of the individual task results; see the supplemental material for full experimental details. Question answering The Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016) contains 100K+ crowd sourced question-answer pairs where the answer is a span in a given Wikipedia paragraph. Our baseline model (Clark and Gardner, 2017) is an improved version of the Bidirectional Attention Flow model in Seo et al. (BiDAF; 2017). It adds a self-attention layer after the bidirectional attention component, simpli-fies some of the pooling operations and substitutes the LSTMs for gated recurrent units (GRUs; Cho et al., 2014). After adding ELMo to the baseline model, test set F 1 improved by 4.7% from 81.1% to 85.8%, a 24.9% relative error reduction over the baseline, and improving the overall single model state-of-the-art by 1.4%. A 11 member ensemble pushes F 1 to 87.4, the overall state-of-the-art at time of submission to the leaderboard. 2 The increase of 4.7% with ELMo is also significantly larger then the 1.8% improvement from adding CoVe to a baseline model (McCann et al., 2017). Textual entailment Textual entailment is the task of determining whether a hypothesis is true, given a premise. The Stanford Natural Language Inference (SNLI) corpus (Bowman et al., 2015) provides approximately 550K hypoth-esis/premise pairs. Our baseline, the ESIM sequence model from Chen et al. (2017), uses a biLSTM to encode the premise and hypothesis, followed by a matrix attention layer, a local inference layer, another biLSTM inference composition layer, and finally a pooling operation before the output layer. Overall, adding ELMo to the ESIM model improves accuracy by an average of 0.7% across five random seeds. A five member ensemble pushes the overall accuracy to 89.3%, exceeding the previous ensemble best of 88.9% (Gong et al., 2018). Semantic role labeling A semantic role labeling (SRL) system models the predicate-argument structure of a sentence, and is often described as answering Who did what to whom. He et al. (2017) modeled SRL as a BIO tagging problem and used an 8-layer deep biLSTM with forward and backward directions interleaved, following Zhou and Xu (2015). As shown in Table 1, when adding ELMo to a re-implementation of He et al. (2017) the single model test set F 1 jumped 3.2% from 81.4% to 84.6%  a new state-of-the-art on the OntoNotes benchmark (Pradhan et al., 2013), even improving over the previous best ensemble result by 1.2%. Coreference resolution Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. Our baseline model is the end-to-end span-based neural model of Lee et al. (2017). It uses a biLSTM and attention mechanism to first compute span representations and then applies a softmax mention ranking model to find coreference chains. In our experiments with the OntoNotes coreference annotations from the CoNLL 2012 shared task (Pradhan et al., 2012), adding ELMo improved the average F 1 by 3.2% from 67.2 to 70.4, establishing a new state of the art, again improving over the previous best ensemble result by 1.6% F 1 . Named entity extraction The CoNLL 2003 NER task (Sang and Meulder, 2003) consists of newswire from the Reuters RCV1 corpus tagged with four different entity types ( PER , LOC , ORG , MISC ). Following recent state-of-the-art systems (Lample et al., 2016; Peters et al., 2017), the baseline model uses pre-trained word embeddings, a character-based CNN representation, two biLSTM layers and a conditional random field (CRF) loss (Lafferty et al., 2001), similar to Collobert et al. (2011). As shown in Table 1, our ELMo enhanced biLSTM-CRF achieves 92.22% F 1 averaged over five runs. The key difference between our system and the previous state of the art from Peters et al. (2017) is that we allowed the task model to learn a weighted average of all biLM layers, whereas Peters et al. (2017) only use the top biLM layer. As shown in Sec. 5.1, using all layers instead of just the last layer improves performance across multiple tasks. Sentiment analysis The fine-grained sentiment classification task in the Stanford Sentiment Treebank (SST-5; Socher et al., 2013) involves selecting one of five labels (from very negative to very positive) to describe a sentence from a movie review. The sentences contain diverse linguistic phenomena such as idioms and complex syntac-2231 Task Baseline Last Only All layers  =1  =0.001 SQuAD 80.8 84.7 85.0 85.2 SNLI 88.1 89.1 89.3 89.5 SRL 81.6 84.1 84.6 84.8 Table 2: Development set performance for SQuAD, SNLI and SRL comparing using all layers of the biLM (with different choices of regularization strength  ) to just the top layer. tic constructions such as negations that are diffi-cult for models to learn. Our baseline model is the biattentive classification network (BCN) from McCann et al. (2017), which also held the prior state-of-the-art result when augmented with CoVe embeddings. Replacing CoVe with ELMo in the BCN model results in a 1.0% absolute accuracy improvement over the state of the art. 5 Analysis This section provides an ablation analysis to validate our chief claims and to elucidate some interesting aspects of ELMo representations. Sec. 5.1 shows that using deep contextual representations in downstream tasks improves performance over previous work that uses just the top layer, regardless of whether they are produced from a biLM or MT encoder, and that ELMo representations provide the best overall performance. Sec. 5.3 explores the different types of contextual information captured in biLMs and uses two intrinsic evaluations to show that syntactic information is better represented at lower layers while semantic information is captured a higher layers, consistent with MT encoders. It also shows that our biLM consistently provides richer representations then CoVe. Additionally, we analyze the sensitivity to where ELMo is included in the task model (Sec. 5.2), training set size (Sec. 5.4), and visualize the ELMo learned weights across the tasks (Sec. 5.5). There are many alternatives to Equation 1 for combining the biLM layers. Previous work on contextual representations used only the last layer, whether it be from a biLM (Peters et al., 2017) or an MT encoder (CoVe; McCann et al., 2017). The choice of the regularization parameter  is also important, as large values such as  = 1 effectively reduce the weighting function to a simple average over the layers, while smaller values (e.g.,  = 0 . 001 ) allow the layer weights to vary. Table 2 compares these alternatives for SQuAD, SNLI and SRL. Including representations from all layers improves overall performance over just using the last layer, and including contextual representations from the last layer improves performance over the baseline. For example, in the case of SQuAD, using just the last biLM layer improves development F 1 by 3.9% over the baseline. Averaging all biLM layers instead of using just the last layer improves F 1 another 0.3% (comparing Last Only to  =1 columns), and allowing the task model to learn individual layer weights improves F 1 another 0.2% (  =1 vs.  =0.001). A small  is preferred in most cases with ELMo, although for NER, a task with a smaller training set, the results are insensitive to  (not shown). The overall trend is similar with CoVe but with smaller increases over the baseline. For SNLI, averaging all layers with  =1 improves development accuracy from 88.2 to 88.7% over using just the last layer. SRL F 1 increased a marginal 0.1% to 82.2 for the  =1 case compared to using the last layer only. All of the task architectures in this paper include word embeddings only as input to the lowest layer biRNN. However, we find that including ELMo at the output of the biRNN in task-specific architectures improves overall results for some tasks. As shown in Table 3, including ELMo at both the input and output layers for SNLI and SQuAD improves over just the input layer, but for SRL (and coreference resolution, not shown) performance is highest when it is included at just the input layer. One possible explanation for this result is that both the SNLI and SQuAD architectures use attention layers after the biRNN, so introducing ELMo at this layer allows the model to attend directly to the biLM's internal representations. In the SRL case, 2232 Source Nearest Neighbors GloVe play playing, game, games, played, players, plays, player, Play, football, multiplayer biLM Chico Ruiz made a spectacular play on Alusik 's grounder { ... } Kieffer , the only junior in the group , was commended for his ability to hit in the clutch , as well as his all-round excellent play . the task-specific context representations are likely more important than those from the biLM. Since adding ELMo improves task performance over word vectors alone, the biLM's contextual representations must encode information generally useful for NLP tasks that is not captured in word vectors. Intuitively, the biLM must be disambiguating the meaning of words using their context. Consider play, a highly polysemous word. The top of Table 4 lists nearest neighbors to play using GloVe vectors. They are spread across several parts of speech (e.g., played, playing as verbs, and player, game as nouns) but concentrated in the sports-related senses of play. In contrast, the bottom two rows show nearest neighbor sentences from the SemCor dataset (see below) using the biLM's context representation of play in the source sentence. In these cases, the biLM is able to disambiguate both the part of speech and word sense in the source sentence. intrinsic evaluation of the contextual representations similar to Belinkov et al. (2017). To isolate the information encoded by the biLM, the representations are used to directly make predictions for a fine grained word sense disambiguation (WSD) task and a POS tagging task. Using this approach, it is also possible to compare to CoVe, and across each of the individual layers. Word sense disambiguation Given a sentence, we can use the biLM representations to predict the sense of a target word using a simple 1-nearest neighbor approach, similar to Melamud et al. (2016). To do so, we first use the biLM to compute representations for all words in SemCor 3.0, our training corpus (Miller et al., 1994), and then take the average representation for each sense. At test time, we again use the biLM to compute representations for a given target word and take the nearest neighbor sense from the training set, falling back to the first sense from WordNet for lemmas not observed during training. Table 5 compares WSD results using the evaluation framework from Raganato et al. (2017b) across the same suite of four test sets in Raganato et al. (2017a). Overall, the biLM top layer rep-2233 resentations have F 1 of 69.0 and are better at WSD then the first layer. This is competitive with a state-of-the-art WSD-specific supervised model using hand crafted features (Iacobacci et al., 2016) and a task specific biLSTM that is also trained with auxiliary coarse-grained semantic labels and POS tags (Raganato et al., 2017a). The CoVe biLSTM layers follow a similar pattern to those from the biLM (higher overall performance at the second layer compared to the first); however, our biLM outperforms the CoVe biLSTM, which trails the WordNet first sense baseline. POS tagging To examine whether the biLM captures basic syntax, we used the context representations as input to a linear classifier that predicts POS tags with the Wall Street Journal portion of the Penn Treebank (PTB) (Marcus et al., 1993). As the linear classifier adds only a small amount of model capacity, this is direct test of the biLM's representations. Similar to WSD, the biLM representations are competitive with carefully tuned, task specific biLSTMs (Ling et al., 2015; Ma and Hovy, 2016). However, unlike WSD, accuracies using the first biLM layer are higher than the top layer, consistent with results from deep biLSTMs in multi-task training (Sgaard and Goldberg, 2016; Hashimoto et al., 2017) and MT (Be-linkov et al., 2017). CoVe POS tagging accuracies follow the same pattern as those from the biLM, and just like for WSD, the biLM achieves higher accuracies than the CoVe encoder. Implications for supervised tasks Taken together, these experiments confirm different layers in the biLM represent different types of information and explain why including all biLM layers is important for the highest performance in downstream tasks. In addition, the biLM's representations are more transferable to WSD and POS tagging than those in CoVe, helping to illustrate why ELMo outperforms CoVe in downstream tasks. Adding ELMo to a model increases the sample efficiency considerably, both in terms of number of parameter updates to reach state-of-the-art performance and the overall training set size. For example, the SRL model reaches a maximum development F 1 after 486 epochs of training without ELMo. After adding ELMo, the model exceeds the baseline maximum at epoch 10, a 98% relative decrease in the number of updates needed to reach Figure 1: Comparison of baseline vs. ELMo performance for SNLI and SRL as the training set size is varied from 0.1% to 100%. In addition, ELMo-enhanced models use smaller training sets more efficiently than models without ELMo. Figure 1 compares the performance of baselines models with and without ELMo as the percentage of the full training set is varied from 0.1% to 100%. Improvements with ELMo are largest for smaller training sets and significantly reduce the amount of training data needed to reach a given level of performance. In the SRL case, the ELMo model with 1% of the training set has about the same F 1 as the baseline model with 10% of the training set. Figure 2 visualizes the softmax-normalized learned layer weights. At the input layer, the task model favors the first biLSTM layer. For coreference and SQuAD, the this is strongly favored, but the distribution is less peaked for the other tasks. The output layer weights are relatively balanced, with a slight preference for the lower layers. In addition to the contextual information captured in the biLM's biLSTM layers, ELMo representations also contain sub-word information in the fully character based context insensitive type layer, x LMk . To analyze the relative contribution of the contextual information compared to the sub-word information, we ran an additional ablation that replaced the GloVe vectors with just the biLM character based x LMk layer without the biLM biLSTM layers. Table 7 summarizes the results for SQuAD, SNLI and SNLI. Replacing the GloVe vectors with the biLM character layer gives a slight improvement for all tasks (e.g. from 80.8 to 81.4 F 1 for SQuAD), but overall the improvements are small compared to the full ELMo model. From this, we conclude that most of the gains in the downstream tasks are due to the contextual information and not the sub-word information. All of the results presented in Sec.4 include pretrained word vectors in addition to ELMo representations. However, it is natural to ask whether pre-trained vectors are still necessary with high quality contextualized representations. As shown in the two right hand columns of Table 7, adding GloVe to models with ELMo generally provides a marginal improvement over ELMo only models (e.g. 0.2% F 1 improvement for SRL from 84.5 to 84.7). We have introduced a general approach for learning high-quality deep context-dependent representations from biLMs, and shown large improvements improvements when applying ELMo to a broad range of NLP tasks. Through ablations and other controlled experiments, we have also confirmed that the biLM layers efficiently encode different types of syntactic and semantic information about words-in-context, and that using all layers improves overall task performance.
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