Generating Long and Diverse Responses with Neural Conversation Models
Building general-purpose conversation agents is a very challenging task, but necessary on the road toward intelligent agents that can interact with humans in natural language. Neural conversation models -- purely data-driven systems trained end-to-end on dialogue corpora -- have shown great promise recently, yet they often produce short and generic responses. This work presents new training and decoding methods that improve the quality, coherence, and diversity of long responses generated using sequence-to-sequence models. Our approach adds self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.
This paper considers the problem of generating long and diverse responses in dialog systems. Two techniques are proposed to the seq-to-seq framework: (1) glimpse model that trains on fixed-length segments of the target side at a time, and (2) a segment-based stochastic decoding technique which injects diversity earlier in the generated responses. The large scale experiments on 2.3B conversation messages are quite impressive. Experiments on human evaluation should also be encouraged. With all these said, I am still not 100% convinced that machine generated long sequence is the right direction for dialog systems. As shown in Figure 3 (a), human evaluation shows that the proposed system is not significantly better than the baselines. I think more analysis and user preference mining should be done in the future to help us understand the nature of this problem.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generating Long and Diverse Responses with Neural Conversation Models
Building general-purpose conversation agents is a very challenging task, but necessary on the road toward intelligent agents that can interact with humans in natural language. Neural conversation models -- purely data-driven systems trained end-to-end on dialogue corpora -- have shown great promise recently, yet they often produce short and generic responses. This work presents new training and decoding methods that improve the quality, coherence, and diversity of long responses generated using sequence-to-sequence models. Our approach adds self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.
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 considers the problem of generating long and diverse responses in dialog systems. Two techniques are proposed to the seq-to-seq framework: (1) glimpse model that trains on fixed-length segments of the target side at a time, and (2) a segment-based stochastic decoding technique which injects diversity earlier in the generated responses. The large scale experiments on 2.3B conversation messages are quite impressive. Experiments on human evaluation should also be encouraged. With all these said, I am still not 100% convinced that machine generated long sequence is the right direction for dialog systems. As shown in Figure 3 (a), human evaluation shows that the proposed system is not significantly better than the baselines. I think more analysis and user preference mining should be done in the future to help us understand the nature of this problem.
assistant
This paper considers the problem of generating long and diverse responses in dialog systems. Two techniques are proposed to the seq-to-seq framework: (1) glimpse model that trains on fixed-length segments of the target side at a time, and (2) a segment-based stochastic decoding technique which injects diversity earlier in the generated responses. The large scale experiments on 2.3B conversation messages are quite impressive. Experiments on human evaluation should also be encouraged. With all these said, I am still not 100% convinced that machine generated long sequence is the right direction for dialog systems. As shown in Figure 3 (a), human evaluation shows that the proposed system is not significantly better than the baselines. I think more analysis and user preference mining should be done in the future to help us understand the nature of this problem.