DSReg: Using Distant Supervision as a Regularizer
In this paper, we aim at tackling a general issue in NLP tasks where some of the negative examples are highly similar to the positive examples, i.e., hard-negative examples). We propose the distant supervision as a regularizer (DSReg) approach to tackle this issue. We convert the original task to a multi-task learning problem, in which we first utilize the idea of distant supervision to retrieve hard-negative examples. The obtained hard-negative examples are then used as a regularizer, and we jointly optimize the original target objective of distinguishing positive examples from negative examples along with the auxiliary task objective of distinguishing soften positive examples (comprised of positive examples and hard-negative examples) from easy-negative examples. In the neural context, this can be done by feeding the final token representations to different output layers. Using this unbelievably simple strategy, we improve the performance of a range of different NLP tasks, including text classification, sequence labeling and reading comprehension.
The authors propose a novel approach to leverage Distant Supervision for discriminating between positive examples and *negative examples that share salient features with the positive ones.* In spite of its simplicity, the method appears to be quite promising. The main feedback for the authors is to describe *early & in detail* the distant supervision techniques used in the experiments. The paper would be greatly improved by adding: - an intuitive paragraph in the intro that explains a concrete example of DS high level, but with enough details for the reader to grasp the idea) - adding a new section right after related work (and before the current *3. Models*) in which you present in great detail (and with concrete & complete examples) the two main DS techniques that are used in the experiments; with that solid understanding Other comments: - for sake of simplicity & understand-ability, you should avoid the use of the term *soften positive examples* in the abstract - avoid using the term *unbelievable* (one in abstract & twice in intro* - the last paragraph before *Conclusion* seems to refer to an earlier version of Figure 4, which, in its current form, does NOT have S(pos,E) or the word *interesting*
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DSReg: Using Distant Supervision as a Regularizer
In this paper, we aim at tackling a general issue in NLP tasks where some of the negative examples are highly similar to the positive examples, i.e., hard-negative examples). We propose the distant supervision as a regularizer (DSReg) approach to tackle this issue. We convert the original task to a multi-task learning problem, in which we first utilize the idea of distant supervision to retrieve hard-negative examples. The obtained hard-negative examples are then used as a regularizer, and we jointly optimize the original target objective of distinguishing positive examples from negative examples along with the auxiliary task objective of distinguishing soften positive examples (comprised of positive examples and hard-negative examples) from easy-negative examples. In the neural context, this can be done by feeding the final token representations to different output layers. Using this unbelievably simple strategy, we improve the performance of a range of different NLP tasks, including text classification, sequence labeling and reading comprehension.
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 approach to leverage Distant Supervision for discriminating between positive examples and *negative examples that share salient features with the positive ones.* In spite of its simplicity, the method appears to be quite promising. The main feedback for the authors is to describe *early & in detail* the distant supervision techniques used in the experiments. The paper would be greatly improved by adding: - an intuitive paragraph in the intro that explains a concrete example of DS high level, but with enough details for the reader to grasp the idea) - adding a new section right after related work (and before the current *3. Models*) in which you present in great detail (and with concrete & complete examples) the two main DS techniques that are used in the experiments; with that solid understanding Other comments: - for sake of simplicity & understand-ability, you should avoid the use of the term *soften positive examples* in the abstract - avoid using the term *unbelievable* (one in abstract & twice in intro* - the last paragraph before *Conclusion* seems to refer to an earlier version of Figure 4, which, in its current form, does NOT have S(pos,E) or the word *interesting*
assistant
The authors propose a novel approach to leverage Distant Supervision for discriminating between positive examples and *negative examples that share salient features with the positive ones. * In spite of its simplicity, the method appears to be quite promising. The main feedback for the authors is to describe *early & in detail* the distant supervision techniques used in the experiments. The paper would be greatly improved by adding: - an intuitive paragraph in the intro that explains a concrete example of DS high level, but with enough details for the reader to grasp the idea) - adding a new section right after related work (and before the current *3. Models*) in which you present in great detail (and with concrete & complete examples) the two main DS techniques that are used in the experiments; with that solid understanding Other comments: - for sake of simplicity & understand-ability, you should avoid the use of the term *soften positive examples* in the abstract - avoid using the term *unbelievable* (one in abstract & twice in intro* - the last paragraph before *Conclusion* seems to refer to an earlier version of Figure 4, which, in its current form, does NOT have S(pos,E) or the word *interesting*