Low-Resource Knowledge-Grounded Dialogue Generation
Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning such a response generation model, are difficult to obtain. Motivated by the challenge in practice, we consider knowledge-grounded dialogue generation under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model. By this means, the major part of the model can be learned from a large number of ungrounded dialogues and unstructured documents, while the remaining small parameters can be well fitted using the limited training examples. Evaluation results on two benchmarks indicate that with only training data, our model can achieve the state-of-the-art performance and generalize well on out-of-domain knowledge.
The authors propose a novel framework for training a knowledge-grounded dialogue model. They decouple the three main elements of such a model - a language model, a context processor, and a document reader - and as such can pretrain each component separately. They achieve state-of-the-art results on two benchmark datasets, and can additionally obtain near-state-of-the-art results while training on a fraction of the task data. Overall I think it’s a strong paper with a good set of experiments, baselines, and considers multiple datasets. Disentangling the model elements is a clever way to allow for more robust pre-training, and indeed yields favorable results. The authors show that just the pre-training aspect is not the root cause of their boost in performance, as a pre-trained baseline model fails to replicate their best results. The contribution is broadly applicable to other areas in which data collation is more difficult; the authors additionally do a good job of pointing out that their knowledge encoder is not limited to text but can also use other knowledge grounding including images, videos, or a knowledge-base. Finally, the authors detail thorough ablation studies for their models. One major thing missing from their ablation in Figure 2 is a setting when *no* pretraining is used. That would be much more comparable to the setting used for TMN, since that had *no* pretraining available to it. Alternative, adding pretraining to the baselines would be another good way to do this, which would help disentangle how much the architecture is helpful over the pretraining. Although the authors point out that a major advantage of their architecture is that we can separate the pretraining for each of the components, I would also be interested to see how they find the model doing if a single source of pretraining is used. I.e., only reddit pretrained weights for all 3 components, or only wikipedia etc. I definitely don’t think that holds back this paper, just think it would provide some evidence of the value gained utilizing the disparate sources.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Low-Resource Knowledge-Grounded Dialogue Generation
Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning such a response generation model, are difficult to obtain. Motivated by the challenge in practice, we consider knowledge-grounded dialogue generation under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model. By this means, the major part of the model can be learned from a large number of ungrounded dialogues and unstructured documents, while the remaining small parameters can be well fitted using the limited training examples. Evaluation results on two benchmarks indicate that with only training data, our model can achieve the state-of-the-art performance and generalize well on out-of-domain knowledge.
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 propose a novel framework for training a knowledge-grounded dialogue model. They decouple the three main elements of such a model - a language model, a context processor, and a document reader - and as such can pretrain each component separately. They achieve state-of-the-art results on two benchmark datasets, and can additionally obtain near-state-of-the-art results while training on a fraction of the task data. Overall I think it’s a strong paper with a good set of experiments, baselines, and considers multiple datasets. Disentangling the model elements is a clever way to allow for more robust pre-training, and indeed yields favorable results. The authors show that just the pre-training aspect is not the root cause of their boost in performance, as a pre-trained baseline model fails to replicate their best results. The contribution is broadly applicable to other areas in which data collation is more difficult; the authors additionally do a good job of pointing out that their knowledge encoder is not limited to text but can also use other knowledge grounding including images, videos, or a knowledge-base. Finally, the authors detail thorough ablation studies for their models. One major thing missing from their ablation in Figure 2 is a setting when *no* pretraining is used. That would be much more comparable to the setting used for TMN, since that had *no* pretraining available to it. Alternative, adding pretraining to the baselines would be another good way to do this, which would help disentangle how much the architecture is helpful over the pretraining. Although the authors point out that a major advantage of their architecture is that we can separate the pretraining for each of the components, I would also be interested to see how they find the model doing if a single source of pretraining is used. I.e., only reddit pretrained weights for all 3 components, or only wikipedia etc. I definitely don’t think that holds back this paper, just think it would provide some evidence of the value gained utilizing the disparate sources.
assistant
The authors propose a novel framework for training a knowledge-grounded dialogue model. They decouple the three main elements of such a model - a language model, a context processor, and a document reader - and as such can pretrain each component separately. They achieve state-of-the-art results on two benchmark datasets, and can additionally obtain near-state-of-the-art results while training on a fraction of the task data. Overall I think it’s a strong paper with a good set of experiments, baselines, and considers multiple datasets. Disentangling the model elements is a clever way to allow for more robust pre-training, and indeed yields favorable results. The authors show that just the pre-training aspect is not the root cause of their boost in performance, as a pre-trained baseline model fails to replicate their best results. The contribution is broadly applicable to other areas in which data collation is more difficult; the authors additionally do a good job of pointing out that their knowledge encoder is not limited to text but can also use other knowledge grounding including images, videos, or a knowledge-base. Finally, the authors detail thorough ablation studies for their models. One major thing missing from their ablation in Figure 2 is a setting when *no* pretraining is used. That would be much more comparable to the setting used for TMN, since that had *no* pretraining available to it. Alternative, adding pretraining to the baselines would be another good way to do this, which would help disentangle how much the architecture is helpful over the pretraining. Although the authors point out that a major advantage of their architecture is that we can separate the pretraining for each of the components, I would also be interested to see how they find the model doing if a single source of pretraining is used. I.e., only reddit pretrained weights for all 3 components, or only wikipedia etc. I definitely don’t think that holds back this paper, just think it would provide some evidence of the value gained utilizing the disparate sources.