A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
This paper introduces a sentence encoding model (for use within larger text understanding models) that can extract a matrix-valued sentence representation by way of within-sentence attention. The new model lends itself to (slightly) more informative visualizations than could be gotten otherwise, and beats reasonable baselines on three datasets. The paper is reasonably clear, I see no major technical issues, and the proposed model is novel and effective. It could plausibly be relevant to sequence modeling tasks beyond NLP. I recommend acceptance. There is one fairly serious writing issue that I*d like to see fixed, though: The abstract, introduction, and related work sections are all heavily skewed towards unsupervised learning. The paper doesn*t appear to be doing unsupervised learning, and the ideas are no more nor less suited to unsupervised learning than any other mainstream ideas in the sentence encoding literature. Details: - You should be clearer about how you expect these embeddings to be used, since that will be of certain interest to anyone attempting to use the results of this work. In particular, how you should convert the matrix representation into a vector for downstream tasks that require one. Some of the content of your reply to my comment could be reasonably added to the paper. - A graphical representation of the structure of the model would be helpful. - The LSTMN (Cheng et al., EMNLP *16) is similar enough to this work that an explicit comparison would be helpful. Again, incorporating your reply to my comment into the paper would be more than adequate. - Jiwei Li et al. (Visualizing and Understanding Neural Models in NLP, NAACL *15) present an alternative way of visualizing the influence of words on sentence encodings without using cross-sentence attention. A brief explicit comparison would be nice here.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper introduces a sentence encoding model (for use within larger text understanding models) that can extract a matrix-valued sentence representation by way of within-sentence attention. The new model lends itself to (slightly) more informative visualizations than could be gotten otherwise, and beats reasonable baselines on three datasets. The paper is reasonably clear, I see no major technical issues, and the proposed model is novel and effective. It could plausibly be relevant to sequence modeling tasks beyond NLP. I recommend acceptance. There is one fairly serious writing issue that I*d like to see fixed, though: The abstract, introduction, and related work sections are all heavily skewed towards unsupervised learning. The paper doesn*t appear to be doing unsupervised learning, and the ideas are no more nor less suited to unsupervised learning than any other mainstream ideas in the sentence encoding literature. Details: - You should be clearer about how you expect these embeddings to be used, since that will be of certain interest to anyone attempting to use the results of this work. In particular, how you should convert the matrix representation into a vector for downstream tasks that require one. Some of the content of your reply to my comment could be reasonably added to the paper. - A graphical representation of the structure of the model would be helpful. - The LSTMN (Cheng et al., EMNLP *16) is similar enough to this work that an explicit comparison would be helpful. Again, incorporating your reply to my comment into the paper would be more than adequate. - Jiwei Li et al. (Visualizing and Understanding Neural Models in NLP, NAACL *15) present an alternative way of visualizing the influence of words on sentence encodings without using cross-sentence attention. A brief explicit comparison would be nice here.
assistant
This paper introduces a sentence encoding model (for use within larger text understanding models) that can extract a matrix-valued sentence representation by way of within-sentence attention. The new model lends itself to (slightly) more informative visualizations than could be gotten otherwise, and beats reasonable baselines on three datasets. The paper is reasonably clear, I see no major technical issues, and the proposed model is novel and effective. It could plausibly be relevant to sequence modeling tasks beyond NLP. I recommend acceptance. There is one fairly serious writing issue that I*d like to see fixed, though: The abstract, introduction, and related work sections are all heavily skewed towards unsupervised learning. The paper doesn*t appear to be doing unsupervised learning, and the ideas are no more nor less suited to unsupervised learning than any other mainstream ideas in the sentence encoding literature. Details: - You should be clearer about how you expect these embeddings to be used, since that will be of certain interest to anyone attempting to use the results of this work. In particular, how you should convert the matrix representation into a vector for downstream tasks that require one. Some of the content of your reply to my comment could be reasonably added to the paper. - A graphical representation of the structure of the model would be helpful. - The LSTMN (Cheng et al., EMNLP *16) is similar enough to this work that an explicit comparison would be helpful. Again, incorporating your reply to my comment into the paper would be more than adequate. - Jiwei Li et al. (Visualizing and Understanding Neural Models in NLP, NAACL *15) present an alternative way of visualizing the influence of words on sentence encodings without using cross-sentence attention. A brief explicit comparison would be nice here.