- 著者
- Kazuaki Hanawa Ryo Nagata Kentaro Inui
- 出版者
- The Association for Natural Language Processing
- 雑誌
- 自然言語処理 (ISSN:13407619)
- 巻号頁・発行日
- vol.29, no.3, pp.901-924, 2022 (Released:2022-09-15)
- 参考文献数
- 26
Feedback comment generation is the task of generating explanatory notes for language learners. Although various generation techniques are available, little is known about which methods are appropriate for this task. Nagata (2019) demonstrates the effectiveness of neural-retrieval-based methods in generating feedback comments for preposition use. Retrieval-based methods have limitations in that they can only output feedback comments existing in the given training data. Besides, feedback comments can be made on other grammatical and writing items other than preposition use, which has not yet been addressed. To shed light on these points, we investigate a wider range of methods for generating various types of feedback comments in this study. Our close analysis of the features of the task leads us to investigate three different architectures for comment generation: (i) a neural-retrieval-based method as a baseline, (ii) a pointer-generator-based generation method as a neural seq2seq method, (iii) a retrieve-and-edit method, a hybrid of (i) and (ii). Intuitively, the pointer-generator should outperform neural-retrieval, and retrieve-and-edit should perform the best. However, in our experiments, this expectation is completely overturned. We closely analyze the results to reveal the major causes of these counter-intuitive results and report on our findings from the experiments, which will lead to further developments of feedback comment generation.
- 著者
- Nozomi Kobayashi Kentaro Inui Yuji Matsumoto
- 出版者
- The Japanese Society for Artificial Intelligence
- 雑誌
- Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)
- 巻号頁・発行日
- vol.22, no.2, pp.227-238, 2007 (Released:2007-01-25)
- 参考文献数
- 19
- 被引用文献数
- 3 17 38
The task of opinion extraction and structurization is the key component of opinion mining, which allow Web users to retrieve and summarize people's opinions scattered over the Internet. Our aim is to develop a method for extracting opinions that represent evaluation of concumer products in a structured form. To achieve the goal, we need to consider some issues that are relevant to the extraction task: How the task of opinion extraction and structurization should be designed, and how to extract the opinions which we defined. We define an opinion unit consisting of a quadruple, that is, the opinion holder, the subject being evaluated, the part or the attribute in which it is evaluated, and the evaluation that expresses positive or negative assessment. In this task, we focus on two subtasks (a) extracting subject/aspect-evaluation relations, and (b) extracting subject/aspect-aspect relations, we approach each extraction task using a machine learning-based method. In this paper, we discuss how customer reviews in web documents can be best structured. We also report on the results of our experiments and discuss future directions.