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 proposes a method for representing sentences as a 2d matrix by utilizing a self-attentive mechanism on the hidden states of a bi-directional LSTM encoder. This work differs from prior work mainly in the 2d structure of embedding, which the authors use to produce heat-map visualizations of input sentences and to generate good performance on several downstream tasks. There is a substantial amount of prior work which the authors do not appropriately address, some of which is listed in previous comments. The main novelty of this work is in the 2d structure of embeddings, and as such, I would have liked to see this structure investigated in much more depth. Specifically, a couple important relevant experiments would have been: * How do the performance and visualizations change as the number of attention vectors (r) varies? * For a fixed parameter budget, how important is using multiple attention vectors versus, say, using a larger hidden state or embedding size? I would recommend changing some of the presentation in the penalization term section. Specifically, the statement that *the best way to evaluate the diversity is definitely the Kullback Leibler divergence between any 2 of the summation weight vectors* runs somewhat counter to the authors* comments about this topic below. In Fig. (2), I did not find the visualizations to provide particularly compelling evidence that the multiple attention vectors were doing much of interest beyond a single attention vector, even with penalization. To me this seems like a necessary component to support the main claims of this paper. Overall, while I found the architecture interesting, I am not convinced that the model*s main innovation -- the 2d structure of the embedding matrix -- is actually doing anything important or meaningful beyond what is being accomplished by similar attentive embedding models already present in the literature. Further experiments demonstrating this effect would be necessary for me to give this paper my full endorsement.
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 proposes a method for representing sentences as a 2d matrix by utilizing a self-attentive mechanism on the hidden states of a bi-directional LSTM encoder. This work differs from prior work mainly in the 2d structure of embedding, which the authors use to produce heat-map visualizations of input sentences and to generate good performance on several downstream tasks. There is a substantial amount of prior work which the authors do not appropriately address, some of which is listed in previous comments. The main novelty of this work is in the 2d structure of embeddings, and as such, I would have liked to see this structure investigated in much more depth. Specifically, a couple important relevant experiments would have been: * How do the performance and visualizations change as the number of attention vectors (r) varies? * For a fixed parameter budget, how important is using multiple attention vectors versus, say, using a larger hidden state or embedding size? I would recommend changing some of the presentation in the penalization term section. Specifically, the statement that *the best way to evaluate the diversity is definitely the Kullback Leibler divergence between any 2 of the summation weight vectors* runs somewhat counter to the authors* comments about this topic below. In Fig. (2), I did not find the visualizations to provide particularly compelling evidence that the multiple attention vectors were doing much of interest beyond a single attention vector, even with penalization. To me this seems like a necessary component to support the main claims of this paper. Overall, while I found the architecture interesting, I am not convinced that the model*s main innovation -- the 2d structure of the embedding matrix -- is actually doing anything important or meaningful beyond what is being accomplished by similar attentive embedding models already present in the literature. Further experiments demonstrating this effect would be necessary for me to give this paper my full endorsement.
assistant
This paper proposes a method for representing sentences as a 2d matrix by utilizing a self-attentive mechanism on the hidden states of a bi-directional LSTM encoder. This work differs from prior work mainly in the 2d structure of embedding, which the authors use to produce heat-map visualizations of input sentences and to generate good performance on several downstream tasks. There is a substantial amount of prior work which the authors do not appropriately address, some of which is listed in previous comments. The main novelty of this work is in the 2d structure of embeddings, and as such, I would have liked to see this structure investigated in much more depth. Specifically, a couple important relevant experiments would have been: * How do the performance and visualizations change as the number of attention vectors (r) varies? * For a fixed parameter budget, how important is using multiple attention vectors versus, say, using a larger hidden state or embedding size? I would recommend changing some of the presentation in the penalization term section. Specifically, the statement that *the best way to evaluate the diversity is definitely the Kullback Leibler divergence between any 2 of the summation weight vectors* runs somewhat counter to the authors* comments about this topic below. In Fig. (2), I did not find the visualizations to provide particularly compelling evidence that the multiple attention vectors were doing much of interest beyond a single attention vector, even with penalization. To me this seems like a necessary component to support the main claims of this paper. Overall, while I found the architecture interesting, I am not convinced that the model*s main innovation -- the 2d structure of the embedding matrix -- is actually doing anything important or meaningful beyond what is being accomplished by similar attentive embedding models already present in the literature. Further experiments demonstrating this effect would be necessary for me to give this paper my full endorsement.