Enhance Word Representation for Out-of-Vocabulary on Ubuntu Dialogue Corpus
Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines the general pre-trained word embedding vectors with those generated on the task-specific training set to address this issue. We integrated character embedding into Chen et al*s Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.
 The paper considers a setting (Ubuntu Dialogue Corpus and Douban Conversation Corpus) where most word types in the data are not covered by pretrained representations. The proposed solution is to combine (1) external pretrained word embeddings and (2) pretrained word embeddings on the training data by keeping them as two views: use the view if it*s available, otherwise use a zero vector. This scheme is shown to perform well compared to other methods, specifically combinations of pretraining vs not pretraining embeddings on the training data, updating vs not updating embeddings during training, and others. Quality: Low. The research is not very well modularized: the addressed problem has nothing specifically to do with ESIM and dialogue response classification, but it*s all tangled up. The proposed solution is reasonable but rather minor. Given that the model will learn task-specific word representations on the training set anyway, it*s not clear how important it is to follow this procedure, though minor improvement is reported (Table 5). Clarity: The writing is clear. But the point of the paper is not immediately obvious because of its failure to modularize its contributions (see above). Originality: Low to minor. Significance: It*s not convincing that an incremental improvement in the pretraining phase is so significant, for instance compared to developing a novel better architecture actually tailored to the dialogue task.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Enhance Word Representation for Out-of-Vocabulary on Ubuntu Dialogue Corpus
Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines the general pre-trained word embedding vectors with those generated on the task-specific training set to address this issue. We integrated character embedding into Chen et al*s Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.
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 considers a setting (Ubuntu Dialogue Corpus and Douban Conversation Corpus) where most word types in the data are not covered by pretrained representations. The proposed solution is to combine (1) external pretrained word embeddings and (2) pretrained word embeddings on the training data by keeping them as two views: use the view if it*s available, otherwise use a zero vector. This scheme is shown to perform well compared to other methods, specifically combinations of pretraining vs not pretraining embeddings on the training data, updating vs not updating embeddings during training, and others. Quality: Low. The research is not very well modularized: the addressed problem has nothing specifically to do with ESIM and dialogue response classification, but it*s all tangled up. The proposed solution is reasonable but rather minor. Given that the model will learn task-specific word representations on the training set anyway, it*s not clear how important it is to follow this procedure, though minor improvement is reported (Table 5). Clarity: The writing is clear. But the point of the paper is not immediately obvious because of its failure to modularize its contributions (see above). Originality: Low to minor. Significance: It*s not convincing that an incremental improvement in the pretraining phase is so significant, for instance compared to developing a novel better architecture actually tailored to the dialogue task.
assistant
 The paper considers a setting (Ubuntu Dialogue Corpus and Douban Conversation Corpus) where most word types in the data are not covered by pretrained representations. The proposed solution is to combine (1) external pretrained word embeddings and (2) pretrained word embeddings on the training data by keeping them as two views: use the view if it*s available, otherwise use a zero vector. This scheme is shown to perform well compared to other methods, specifically combinations of pretraining vs not pretraining embeddings on the training data, updating vs not updating embeddings during training, and others. Quality: Low. The research is not very well modularized: the addressed problem has nothing specifically to do with ESIM and dialogue response classification, but it*s all tangled up. The proposed solution is reasonable but rather minor. Given that the model will learn task-specific word representations on the training set anyway, it*s not clear how important it is to follow this procedure, though minor improvement is reported (Table 5). Clarity: The writing is clear. But the point of the paper is not immediately obvious because of its failure to modularize its contributions (see above). Originality: Low to minor. Significance: It*s not convincing that an incremental improvement in the pretraining phase is so significant, for instance compared to developing a novel better architecture actually tailored to the dialogue task.