Unifying Question Answering, Text Classification, and Regression via Span Extraction
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks. We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.
This submission proposes a span-extraction approach to unify QA, text classification and regression tasks. The extensive empirical studies presented in this paper demonstrate that the proposed method can improve the performance by a small margin across multiple datasets and tasks. Overall, I find that the idea of unifying QA, text classification and regression is interesting by itself, but the experiments cannot justify their claims well mainly due to the mixed results. I have the following concerns: 0. Compared to decalNLP (https://github.com/salesforce/decaNLP), this new approach seems unable to handle as many types of tasks as decalNLP. It is not clear to me what is the main advantage. As discussed in the Related Work Section, decalNLP needs to fix the classifier a-priori, but this submission*s method needs a natural language description, which seems more difficult to implement in practice. 1. The results are mixed. For example, SpEx-BERT underperforms BERT on RTE and QNLI in Table 2 and Span-extractive multi-task learning results in weaker single-task models as shown in Table 4a. Furthermore, an error analysis would be helpful here.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks. We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.
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 submission proposes a span-extraction approach to unify QA, text classification and regression tasks. The extensive empirical studies presented in this paper demonstrate that the proposed method can improve the performance by a small margin across multiple datasets and tasks. Overall, I find that the idea of unifying QA, text classification and regression is interesting by itself, but the experiments cannot justify their claims well mainly due to the mixed results. I have the following concerns: 0. Compared to decalNLP (https://github.com/salesforce/decaNLP), this new approach seems unable to handle as many types of tasks as decalNLP. It is not clear to me what is the main advantage. As discussed in the Related Work Section, decalNLP needs to fix the classifier a-priori, but this submission*s method needs a natural language description, which seems more difficult to implement in practice. 1. The results are mixed. For example, SpEx-BERT underperforms BERT on RTE and QNLI in Table 2 and Span-extractive multi-task learning results in weaker single-task models as shown in Table 4a. Furthermore, an error analysis would be helpful here.
assistant
This submission proposes a span-extraction approach to unify QA, text classification and regression tasks. The extensive empirical studies presented in this paper demonstrate that the proposed method can improve the performance by a small margin across multiple datasets and tasks. Overall, I find that the idea of unifying QA, text classification and regression is interesting by itself, but the experiments cannot justify their claims well mainly due to the mixed results. I have the following concerns: 0. Compared to decalNLP (https://github.com/salesforce/decaNLP), this new approach seems unable to handle as many types of tasks as decalNLP. It is not clear to me what is the main advantage. As discussed in the Related Work Section, decalNLP needs to fix the classifier a-priori, but this submission*s method needs a natural language description, which seems more difficult to implement in practice. 1. The results are mixed. For example, SpEx-BERT underperforms BERT on RTE and QNLI in Table 2 and Span-extractive multi-task learning results in weaker single-task models as shown in Table 4a. Furthermore, an error analysis would be helpful here.