COLD FUSION: TRAINING SEQ2SEQ MODELS TOGETHER WITH LANGUAGE MODELS
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.
 The paper proposes a new way of integrating a language model into a seq2seq network: instead of adding the language model only during decoding, the model has access to a pretrained language model during training too. This makes the training and testing conditions more similar. Moreover, only the logits of the pretrained language model are used, making it possible to swap language models post-training. The experiments show that the proposed language model fusion is effective, and works well even when different, domain-dependent language models are used during training and testing. Further experiments indicate that through the integration of a language model at training time the seq2seq*s decoder can be smaller as it is relieved of language modeling. Quality: The paper is well executed, the experiments do basic validation of the model (ablation plus a specially designed task to show model effectiveness) Clarity: Well written, easy to understand. Originality: The main idea is new. Significance: Better language model integration and easier adaptation to new domains of seq2seq models is important. Pros and cons: pros : see above cons: My problem with the paper is lack of experiments on public datasets. The efficacy of the method is shown on only one task on a proprietary corpus engineered for domain mismatch and the method may be not so efficient under other circumstances. Besides presenting results on publicly available data, the paper would also be improved by adding a baseline in which the logits of the language model are added to the logits of the seq2seq decoder at training time. Similarly to cold-fusion, this baseline also allows swapping of language models at test time. In contrast, the baselines presented in the paper are weaker because they don*t use a language model during training time.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
COLD FUSION: TRAINING SEQ2SEQ MODELS TOGETHER WITH LANGUAGE MODELS
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.
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 proposes a new way of integrating a language model into a seq2seq network: instead of adding the language model only during decoding, the model has access to a pretrained language model during training too. This makes the training and testing conditions more similar. Moreover, only the logits of the pretrained language model are used, making it possible to swap language models post-training. The experiments show that the proposed language model fusion is effective, and works well even when different, domain-dependent language models are used during training and testing. Further experiments indicate that through the integration of a language model at training time the seq2seq*s decoder can be smaller as it is relieved of language modeling. Quality: The paper is well executed, the experiments do basic validation of the model (ablation plus a specially designed task to show model effectiveness) Clarity: Well written, easy to understand. Originality: The main idea is new. Significance: Better language model integration and easier adaptation to new domains of seq2seq models is important. Pros and cons: pros : see above cons: My problem with the paper is lack of experiments on public datasets. The efficacy of the method is shown on only one task on a proprietary corpus engineered for domain mismatch and the method may be not so efficient under other circumstances. Besides presenting results on publicly available data, the paper would also be improved by adding a baseline in which the logits of the language model are added to the logits of the seq2seq decoder at training time. Similarly to cold-fusion, this baseline also allows swapping of language models at test time. In contrast, the baselines presented in the paper are weaker because they don*t use a language model during training time.
assistant
 The paper proposes a new way of integrating a language model into a seq2seq network: instead of adding the language model only during decoding, the model has access to a pretrained language model during training too. This makes the training and testing conditions more similar. Moreover, only the logits of the pretrained language model are used, making it possible to swap language models post-training. The experiments show that the proposed language model fusion is effective, and works well even when different, domain-dependent language models are used during training and testing. Further experiments indicate that through the integration of a language model at training time the seq2seq*s decoder can be smaller as it is relieved of language modeling. Quality: The paper is well executed, the experiments do basic validation of the model (ablation plus a specially designed task to show model effectiveness) Clarity: Well written, easy to understand. Originality: The main idea is new. Significance: Better language model integration and easier adaptation to new domains of seq2seq models is important. Pros and cons: pros : see above cons: My problem with the paper is lack of experiments on public datasets. The efficacy of the method is shown on only one task on a proprietary corpus engineered for domain mismatch and the method may be not so efficient under other circumstances. Besides presenting results on publicly available data, the paper would also be improved by adding a baseline in which the logits of the language model are added to the logits of the seq2seq decoder at training time. Similarly to cold-fusion, this baseline also allows swapping of language models at test time. In contrast, the baselines presented in the paper are weaker because they don*t use a language model during training time.