Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Neural sequence models are widely used to model time-series data. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top B candidates. This tends to result in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose Diverse Beam Search (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing a diversity-augmented objective. We observe that our method not only improved diversity but also finds better top 1 solutions by controlling for the exploration and exploitation of the search space. Moreover, these gains are achieved with minimal computational or memory overhead com- pared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation, conversation and visual question generation using both standard quantitative metrics and qualitative human studies. We find that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
The paper addresses an important problem - namely on how to improve diversity in responses. It is applaudable that the authors show results on several tasks showing the applicability across different problems. In my view there are two weaknesses at this point 1) the improvements (for essentially all tasks) seem rather minor and do not really fit the overall claim of the paper 2) the approach seems quite ad hoc and it unclear to me if this is something that will and should be widely adopted. Having said this the gist of the proposed solution seems interesting but somewhat premature.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Neural sequence models are widely used to model time-series data. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top B candidates. This tends to result in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose Diverse Beam Search (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing a diversity-augmented objective. We observe that our method not only improved diversity but also finds better top 1 solutions by controlling for the exploration and exploitation of the search space. Moreover, these gains are achieved with minimal computational or memory overhead com- pared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation, conversation and visual question generation using both standard quantitative metrics and qualitative human studies. We find that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
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 paper addresses an important problem - namely on how to improve diversity in responses. It is applaudable that the authors show results on several tasks showing the applicability across different problems. In my view there are two weaknesses at this point 1) the improvements (for essentially all tasks) seem rather minor and do not really fit the overall claim of the paper 2) the approach seems quite ad hoc and it unclear to me if this is something that will and should be widely adopted. Having said this the gist of the proposed solution seems interesting but somewhat premature.
assistant
The paper addresses an important problem - namely on how to improve diversity in responses. It is applaudable that the authors show results on several tasks showing the applicability across different problems. In my view there are two weaknesses at this point 1) the improvements (for essentially all tasks) seem rather minor and do not really fit the overall claim of the paper 2) the approach seems quite ad hoc and it unclear to me if this is something that will and should be widely adopted. Having said this the gist of the proposed solution seems interesting but somewhat premature.