Extractor-Attention Network: A New Attention Network with Hybrid Encoders for Chinese Text Classification
Chinese text classification has received more and more attention today. However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media. To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN). Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder. It improves the capability of Chinese text representation. Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures. Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues. The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.
[Summary] This paper proposes an attention-based model, Extractor-attention network(EAN), for the Chinese text classification task. The proposed model consists of two encoders: word encoder and Pinyin encoder. The main contribution of this paper is extending architecture for Pinyin character encoder. In experiments, the authors tested and reported the results on 5 Chinese text datasets. [Comments] I cannot agree with the contributions claimed in this paper for the following reasons: - First, the model proposed in the paper is not intuitive and novel. It seems a structure that simply combines existing models. If the proposed model is not simply combined by existing models, the authors need a clear explanation of how each part of the model is intuitively designed. - Second, the description and definition of the task are not clear. (The authors need to add a detailed explanation and a clear definition of tasks, including examples.) Also, it would be better to be clear about what problems exist in this task and what the authors want to tackle. - Third, (I don*t know much Chinese but in general), it doesn*t seem natural to be given a Pinyin character. It is considered desirable to learn to infer the meaning of text through context without a pinyin character. In addition, the additional use of the Pinyin character seems to be very equivalent to multi-language tasks and extending seems very straightforward. (I think the multi-language task is more general and natural setting, so I wonder why the authors restrict the problem to Chinese text classification.) - In the experiment, only the test error rate is reported, but it is not enough to explain how well the models perform in practical problems. In addition to the test error rate, it would be better to show how practical improvements have been made in real examples.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Extractor-Attention Network: A New Attention Network with Hybrid Encoders for Chinese Text Classification
Chinese text classification has received more and more attention today. However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media. To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN). Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder. It improves the capability of Chinese text representation. Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures. Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues. The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.
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>



[Summary] This paper proposes an attention-based model, Extractor-attention network(EAN), for the Chinese text classification task. The proposed model consists of two encoders: word encoder and Pinyin encoder. The main contribution of this paper is extending architecture for Pinyin character encoder. In experiments, the authors tested and reported the results on 5 Chinese text datasets. [Comments] I cannot agree with the contributions claimed in this paper for the following reasons: - First, the model proposed in the paper is not intuitive and novel. It seems a structure that simply combines existing models. If the proposed model is not simply combined by existing models, the authors need a clear explanation of how each part of the model is intuitively designed. - Second, the description and definition of the task are not clear. (The authors need to add a detailed explanation and a clear definition of tasks, including examples.) Also, it would be better to be clear about what problems exist in this task and what the authors want to tackle. - Third, (I don*t know much Chinese but in general), it doesn*t seem natural to be given a Pinyin character. It is considered desirable to learn to infer the meaning of text through context without a pinyin character. In addition, the additional use of the Pinyin character seems to be very equivalent to multi-language tasks and extending seems very straightforward. (I think the multi-language task is more general and natural setting, so I wonder why the authors restrict the problem to Chinese text classification.) - In the experiment, only the test error rate is reported, but it is not enough to explain how well the models perform in practical problems. In addition to the test error rate, it would be better to show how practical improvements have been made in real examples.
assistant
[Summary] This paper proposes an attention-based model, Extractor-attention network(EAN), for the Chinese text classification task. The proposed model consists of two encoders: word encoder and Pinyin encoder. The main contribution of this paper is extending architecture for Pinyin character encoder. In experiments, the authors tested and reported the results on 5 Chinese text datasets. [Comments] I cannot agree with the contributions claimed in this paper for the following reasons: - First, the model proposed in the paper is not intuitive and novel. It seems a structure that simply combines existing models. If the proposed model is not simply combined by existing models, the authors need a clear explanation of how each part of the model is intuitively designed. - Second, the description and definition of the task are not clear. (The authors need to add a detailed explanation and a clear definition of tasks, including examples.) Also, it would be better to be clear about what problems exist in this task and what the authors want to tackle. - Third, (I don*t know much Chinese but in general), it doesn*t seem natural to be given a Pinyin character. It is considered desirable to learn to infer the meaning of text through context without a pinyin character. In addition, the additional use of the Pinyin character seems to be very equivalent to multi-language tasks and extending seems very straightforward. (I think the multi-language task is more general and natural setting, so I wonder why the authors restrict the problem to Chinese text classification.) - In the experiment, only the test error rate is reported, but it is not enough to explain how well the models perform in practical problems. In addition to the test error rate, it would be better to show how practical improvements have been made in real examples.