Amortized Context Vector Inference for Sequence-to-Sequence Networks
Neural attention (NA) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. Inspired from recent work in the field of amortized variational inference (AVI), in this work we consider treating the context vectors generated by soft-attention (SA) models as latent variables, with approximate finite mixture model posteriors inferred via AVI. We posit that this formulation may yield stronger generalization capacity, in line with the outcomes of existing applications of AVI to deep networks. To illustrate our method, we implement it and experimentally evaluate it considering challenging ADS, VC, and MT benchmarks. This way, we exhibit its improved effectiveness over state-of-the-art alternatives.
 The authors made a method that consider latent binary representation instead of continuous representations in Seq2seq models, and showed that it could help regularization in multiple sequence prediction tasks. To be effective, the authors considered a simple approximation of the average embedding that prevent them from using REINFORCE. The intuition is that hard latent binary units might be better at representing vocabulary choices and leads to a better inductive bias of the model. However, no inspection of the model to understand in which sentences the averaging of binary variables makes a real difference in prediction. There is no analysis of the condition under which this approximation is correct. What is the impact of this approximation? Are the results the same if reinforce is used? Overall, the article is clearly written and conclusions sufficiently relevant for this article to be published in ICLR. Minor comments: - the term Amortized Variational Inference is not the most intuitive and Variational Auto-Encoder seems to be the de-facto standard term for these techniques. - For the MT results, why the authors did not consider more common language pairs so that the results are readily comparable?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Amortized Context Vector Inference for Sequence-to-Sequence Networks
Neural attention (NA) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS), machine translation (MT), and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. Inspired from recent work in the field of amortized variational inference (AVI), in this work we consider treating the context vectors generated by soft-attention (SA) models as latent variables, with approximate finite mixture model posteriors inferred via AVI. We posit that this formulation may yield stronger generalization capacity, in line with the outcomes of existing applications of AVI to deep networks. To illustrate our method, we implement it and experimentally evaluate it considering challenging ADS, VC, and MT benchmarks. This way, we exhibit its improved effectiveness over state-of-the-art alternatives.
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>



 The authors made a method that consider latent binary representation instead of continuous representations in Seq2seq models, and showed that it could help regularization in multiple sequence prediction tasks. To be effective, the authors considered a simple approximation of the average embedding that prevent them from using REINFORCE. The intuition is that hard latent binary units might be better at representing vocabulary choices and leads to a better inductive bias of the model. However, no inspection of the model to understand in which sentences the averaging of binary variables makes a real difference in prediction. There is no analysis of the condition under which this approximation is correct. What is the impact of this approximation? Are the results the same if reinforce is used? Overall, the article is clearly written and conclusions sufficiently relevant for this article to be published in ICLR. Minor comments: - the term Amortized Variational Inference is not the most intuitive and Variational Auto-Encoder seems to be the de-facto standard term for these techniques. - For the MT results, why the authors did not consider more common language pairs so that the results are readily comparable?
assistant
 The authors made a method that consider latent binary representation instead of continuous representations in Seq2seq models, and showed that it could help regularization in multiple sequence prediction tasks. To be effective, the authors considered a simple approximation of the average embedding that prevent them from using REINFORCE. The intuition is that hard latent binary units might be better at representing vocabulary choices and leads to a better inductive bias of the model. However, no inspection of the model to understand in which sentences the averaging of binary variables makes a real difference in prediction. There is no analysis of the condition under which this approximation is correct. What is the impact of this approximation? Are the results the same if reinforce is used? Overall, the article is clearly written and conclusions sufficiently relevant for this article to be published in ICLR. Minor comments: - the term Amortized Variational Inference is not the most intuitive and Variational Auto-Encoder seems to be the de-facto standard term for these techniques. - For the MT results, why the authors did not consider more common language pairs so that the results are readily comparable?