Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples (2023)

Abstract: In this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. Feedback comment generation is a task in which a system generates feedback comments such as hints or explanatory notes for language learners, given input text and position showing where to comment. In the conventional study, the retrieve-and-edit method for retrieving feedback comments in the data pool and editing the comments has been thought effective for this task. However, the performance of this method does not perform as well as other conventional methods because its model learns to edit tokens that do not need to be rewritten in the retrieved comments. To mitigate this problem, we propose a method for combining retrieval, masking, and generation based on the retrieve-and-edit method. Specifically, tokens of feedback comments retrieved from the data pool are masked, and this masked feedback comment is used as a template to generate feedback comments. The proposed method should prevent unnecessary conversion by using not retrieved feedback comments directly but masking them. Our experiments on feedback comment generation demonstrate that the proposed method outperforms conventional methods.

## introduction feedback comment generation is a task in which, given an input text and position that shows where to comment, a system generates feedback comments such as hints or explanatory notes for non-native language learners @xcite . in this task, it is not enough to simply point out or correct errors; the system should also explain why they are wrong. such a system would be extremely beneficial for language learners, but there is currently no effective method for generating comments. conventionally, retrieval-based @xcite , simple generation @xcite , and retrieveand-edit @xcite methods have been used for the feedback comment generation task. with the retrieval-based method, the method retrieves a feedback comment from the data pool with maximum cosine similarity. although the modeling for this method is simple, the output is not flexible; for example, this method often retrieves feedback comments that were correct as hints or explanatory notes but focused on tokens that are not in the input text. the simple generation method generates feedback comments directly given the input text and position using an encoderdecoder model. since this method can generate flexible feedback comments, it mitigates the problem with the retrieval-based method. however, generating comments with simple generation is more difficult than with the retrieval-based method because this method should generate comments from scratch. thus, @xcite used the retrieve-and-edit method that combines these two methods for better performance. with this method, the feedback comments retrieved with the retrieval-based method are edited using the simple generation method. however, the experimental results indicated that the retrieve-and-edit method did not perform well because its model learned excessive conversions and converted unnecessary tokens @xcite . to mitigate this problem, we consider extending the retrieve-and-edit method with which unnecessary tokens in the retrieved feedback comments are actively edited. our idea to specify where to edit the retrieved feedback comment is masking tokens that should be edited. by masking tokens that should be edited, the method should be able to predict only masked tokens and not change the other tokens in the retrieved feedback comments. specifically, we mask tokens in retrieved feedback comments obtained with the retrieval-based method, and the method outputs a feedback comment given this masked feedback comment and the input text. we can create a template for comment generation, as the tokens of the retrieved feedback comment that are irrelevant to the input text are eliminated. in this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. it consists of three modules: retrieval, masking, and generation. first, the retrieval module retrieves a feedback comment from the data pool, as with the conventional retrieval-based method. next, the masking module executes binary classification, i.e., masking or not, for each token in the retrieved feedback comment. the masking module learns to mask tokens of the retrieved comment that are not in the reference feedback comment. finally, the generation module generates a feedback comment given the input text and masked feedback comment. to generate feedback comments, the proposed method cascades the results of each module, which means that the performance of each module depends on the subsequent performance of the module output. to mitigate this potential problem, we introduce a multi-decoding operation that uses not only the top result but also the top k results. our experiments on feedback comment generation demonstrate that the proposed method performs better than the above three conventional methods. ## feedback comment generation in feedback comment generation, given the input text that has grammatical errors and position, a system outputs a feedback comment. we define the input text as @xmath0, • • • , x m } and feedback comment as @xmath1, • • • , y n }, where x m and y n are tokens, and m and n are the number of tokens in the input text and feedback comment, respectively. the position represents the range of the feedback target in character units and consists of integers. we make x emphasize the target characters by adding brackets to the input text on the basis based of the given position. for example, when x is "he agrees the opinion." and the position is 3:13, x is "he «agrees the» opinion." note that in x, the input text and position are not treated separately, but the position is also treated as a token. ## retrieval, masking, and generation modules the proposed method consists of three modules: retrieval, masking, and generation. figure 1 shows an overview of the proposed method. ## conclusion in this paper, we proposed a novel method, retrieval, masking, and generation, for feedback comment generation. the proposed method has three modules, retrieval, masking, and generation, and generates feedback comments by cascading each module output. first, the retrieval module extracts an example of feedback comments appropriate for the input text from the data pool. next, the masking module masks tokens of the retrieved example that are not in the reference feedback comment. finally, given a concatenated sequence of the input text and masked feedback comment, the generation module outputs the feedback comment in the generation module. with this three-module method, the performance of each module depends on the subsequent performance of the module output. since it is difficult to guarantee the perfect performance of each module, we use a multi-decoding operation that uses not only the top result but also the top k results for each decoding. our experimental results indicate that the proposed method outperforms the three conventional methods. input text reference retrieved ... please keep consider about that. @xmath2. since @xmath3, @xmath4. masked generation predicted masking when @xmath5, @xmath6. correct masking