Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in neural Sequence-to-Sequence (Seq2Seq) models reveal a purely data-driven approach to the response generation task. Despite its diverse variants and applications, the existing Seq2Seq models are prone to producing short and generic replies, which blocks such neural network architectures from being utilized in practical open-domain response generation tasks. In this research, we analyze this critical issue from the perspective of the optimization goal of models and the specific characteristics of human-to-human conversational corpora. Our analysis is conducted by decomposing the goal of Neural Response Generation (NRG) into the optimizations of word selection and ordering. It can be derived from the decomposing that Seq2Seq based NRG models naturally tend to select common words to compose responses, and ignore the semantic of queries in word ordering. On the basis of the analysis, we propose a max-marginal ranking regularization term to avoid Seq2Seq models from producing the generic and uninformative responses. The empirical experiments on benchmarks with several metrics have validated our analysis and proposed methodology.
 The paper looks into improving the neural response generation task by deemphasizing the common responses using modification of the loss function and presentation the common/universal responses during the training phase. The authors show that the approach yields better results in the dataset considered using various measures and human evaluation. Improvement Points - the explanation for low ROGUE measure due to the method favoring non-repetitive words sounds like it can be supported using numerical statistics, than hand-waiving argument - for the timing, how much time was taken to tune the additional parameters (how the # of negative responses sampled for each positive response was chosen as four via uniform sampling) - some description about a) how many users are there, what type of conversation/active users/topics etc. b) what time frame was used during data collection (this may have implications for lemma asserting zipf) - it would be interesting to know for the trivial questions if the performance was impacted by the deemphasizing (one that do result in universal replies)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in neural Sequence-to-Sequence (Seq2Seq) models reveal a purely data-driven approach to the response generation task. Despite its diverse variants and applications, the existing Seq2Seq models are prone to producing short and generic replies, which blocks such neural network architectures from being utilized in practical open-domain response generation tasks. In this research, we analyze this critical issue from the perspective of the optimization goal of models and the specific characteristics of human-to-human conversational corpora. Our analysis is conducted by decomposing the goal of Neural Response Generation (NRG) into the optimizations of word selection and ordering. It can be derived from the decomposing that Seq2Seq based NRG models naturally tend to select common words to compose responses, and ignore the semantic of queries in word ordering. On the basis of the analysis, we propose a max-marginal ranking regularization term to avoid Seq2Seq models from producing the generic and uninformative responses. The empirical experiments on benchmarks with several metrics have validated our analysis and proposed methodology.
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 looks into improving the neural response generation task by deemphasizing the common responses using modification of the loss function and presentation the common/universal responses during the training phase. The authors show that the approach yields better results in the dataset considered using various measures and human evaluation. Improvement Points - the explanation for low ROGUE measure due to the method favoring non-repetitive words sounds like it can be supported using numerical statistics, than hand-waiving argument - for the timing, how much time was taken to tune the additional parameters (how the # of negative responses sampled for each positive response was chosen as four via uniform sampling) - some description about a) how many users are there, what type of conversation/active users/topics etc. b) what time frame was used during data collection (this may have implications for lemma asserting zipf) - it would be interesting to know for the trivial questions if the performance was impacted by the deemphasizing (one that do result in universal replies)
assistant
 The paper looks into improving the neural response generation task by deemphasizing the common responses using modification of the loss function and presentation the common/universal responses during the training phase. The authors show that the approach yields better results in the dataset considered using various measures and human evaluation. Improvement Points - the explanation for low ROGUE measure due to the method favoring non-repetitive words sounds like it can be supported using numerical statistics, than hand-waiving argument - for the timing, how much time was taken to tune the additional parameters (how the # of negative responses sampled for each positive response was chosen as four via uniform sampling) - some description about a) how many users are there, what type of conversation/active users/topics etc. b) what time frame was used during data collection (this may have implications for lemma asserting zipf) - it would be interesting to know for the trivial questions if the performance was impacted by the deemphasizing (one that do result in universal replies)