Joint text classification on multiple levels with multiple labels
Natural language uses words in an associative way to construct sentences: it is not words in isolation, but the appropriate use of hierarchical structures that makes communication successful. We propose a deep learning framework for explicitly tying together the representations between single words and full sentences, resulting in a fluid transfer of knowledge between these two levels of granularity. We construct a multi-head attention mechanism for sentence classification, where the individual attention heads simultaneously learn to perform multi-class sequence labeling. Supervision on individual tokens explicitly teaches the classifier which areas it needs to focus on in each sentence, while the sentence-level objective regularizes the token-level predictions and even enables sequence labeling without token-level training data. Our experiments show that the proposed architecture systematically outperforms its single-task counterparts and exhibits strong transfer capabilities, while also achieving reasonable performance as a zero-shot sequence labeler.
This paper addresses the problem of text classification by incorporating classification at both token and sentence levels, with the aim of better learning composition. The paper finds that introducing losses at multiple levels, along with additional auxiliary objectives, generally improves performance over simpler baselines. The paper is clearly written, and I like the combination of token-level and sentence-level objectives with the aim of better learning compositional properties of language. I think the architecture described is interesting, with reasonable motivation, so overall I think the paper has potential. However, I don*t think the paper in its current form has clear enough takeaways or strong enough results to make a publishable contribution -- in particular, it*s not clear that the model in fact advances our ability to capture composition, nor do the results clearly advance the state of the art, and the contextualization with respect to related work is too minimal. The main claim of the paper is that this multi-level labeling scheme incentivizes the network to learn better composition functions. However, there is no compelling reason to conclude that composition has necessarily been improved here. The majority of tasks being tested on are not related to composition -- it seems that the token/sentence relationship in the error detection and entity detection tasks simply involves identifying whether one token in the sentence has a positive label, which really has nothing to do with composition. The SST task can plausibly require non-trivial composition to relate between the token-level sentiment and the sentence-level sentiment, but that would typically involve incorporating sentiment at the phrase level along the way, rather than jumping from the token to the sentence level, which seems more likely to focus on individual lexical cues rather than composition. Beyond the choice of tasks, there is also no further analysis/discussion to help in determining whether anything about composition has been improved. If the results were strong enough to advance the state of the art in these tasks, the composition takeaways might be less critical, but since the only baselines compared against are simpler versions of this model, it*s not clear that the results here advance the state of the art in any way. As another point: the contextualization with respect to related literature should also be improved. Currently the related work is described extremely briefly and fairly obliquely, and it is not clear how exactly this work differs from other work that has involved prediction at multiple linguistic levels (or work on composition). Overall, I think the paper has potential, but it needs a clearer contribution and better contextualization.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Joint text classification on multiple levels with multiple labels
Natural language uses words in an associative way to construct sentences: it is not words in isolation, but the appropriate use of hierarchical structures that makes communication successful. We propose a deep learning framework for explicitly tying together the representations between single words and full sentences, resulting in a fluid transfer of knowledge between these two levels of granularity. We construct a multi-head attention mechanism for sentence classification, where the individual attention heads simultaneously learn to perform multi-class sequence labeling. Supervision on individual tokens explicitly teaches the classifier which areas it needs to focus on in each sentence, while the sentence-level objective regularizes the token-level predictions and even enables sequence labeling without token-level training data. Our experiments show that the proposed architecture systematically outperforms its single-task counterparts and exhibits strong transfer capabilities, while also achieving reasonable performance as a zero-shot sequence labeler.
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 addresses the problem of text classification by incorporating classification at both token and sentence levels, with the aim of better learning composition. The paper finds that introducing losses at multiple levels, along with additional auxiliary objectives, generally improves performance over simpler baselines. The paper is clearly written, and I like the combination of token-level and sentence-level objectives with the aim of better learning compositional properties of language. I think the architecture described is interesting, with reasonable motivation, so overall I think the paper has potential. However, I don*t think the paper in its current form has clear enough takeaways or strong enough results to make a publishable contribution -- in particular, it*s not clear that the model in fact advances our ability to capture composition, nor do the results clearly advance the state of the art, and the contextualization with respect to related work is too minimal. The main claim of the paper is that this multi-level labeling scheme incentivizes the network to learn better composition functions. However, there is no compelling reason to conclude that composition has necessarily been improved here. The majority of tasks being tested on are not related to composition -- it seems that the token/sentence relationship in the error detection and entity detection tasks simply involves identifying whether one token in the sentence has a positive label, which really has nothing to do with composition. The SST task can plausibly require non-trivial composition to relate between the token-level sentiment and the sentence-level sentiment, but that would typically involve incorporating sentiment at the phrase level along the way, rather than jumping from the token to the sentence level, which seems more likely to focus on individual lexical cues rather than composition. Beyond the choice of tasks, there is also no further analysis/discussion to help in determining whether anything about composition has been improved. If the results were strong enough to advance the state of the art in these tasks, the composition takeaways might be less critical, but since the only baselines compared against are simpler versions of this model, it*s not clear that the results here advance the state of the art in any way. As another point: the contextualization with respect to related literature should also be improved. Currently the related work is described extremely briefly and fairly obliquely, and it is not clear how exactly this work differs from other work that has involved prediction at multiple linguistic levels (or work on composition). Overall, I think the paper has potential, but it needs a clearer contribution and better contextualization.
assistant
This paper addresses the problem of text classification by incorporating classification at both token and sentence levels, with the aim of better learning composition. The paper finds that introducing losses at multiple levels, along with additional auxiliary objectives, generally improves performance over simpler baselines. The paper is clearly written, and I like the combination of token-level and sentence-level objectives with the aim of better learning compositional properties of language. I think the architecture described is interesting, with reasonable motivation, so overall I think the paper has potential. However, I don*t think the paper in its current form has clear enough takeaways or strong enough results to make a publishable contribution -- in particular, it*s not clear that the model in fact advances our ability to capture composition, nor do the results clearly advance the state of the art, and the contextualization with respect to related work is too minimal. The main claim of the paper is that this multi-level labeling scheme incentivizes the network to learn better composition functions. However, there is no compelling reason to conclude that composition has necessarily been improved here. The majority of tasks being tested on are not related to composition -- it seems that the token/sentence relationship in the error detection and entity detection tasks simply involves identifying whether one token in the sentence has a positive label, which really has nothing to do with composition. The SST task can plausibly require non-trivial composition to relate between the token-level sentiment and the sentence-level sentiment, but that would typically involve incorporating sentiment at the phrase level along the way, rather than jumping from the token to the sentence level, which seems more likely to focus on individual lexical cues rather than composition. Beyond the choice of tasks, there is also no further analysis/discussion to help in determining whether anything about composition has been improved. If the results were strong enough to advance the state of the art in these tasks, the composition takeaways might be less critical, but since the only baselines compared against are simpler versions of this model, it*s not clear that the results here advance the state of the art in any way. As another point: the contextualization with respect to related literature should also be improved. Currently the related work is described extremely briefly and fairly obliquely, and it is not clear how exactly this work differs from other work that has involved prediction at multiple linguistic levels (or work on composition). Overall, I think the paper has potential, but it needs a clearer contribution and better contextualization.