Koji Tanaka


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Constructing a Public Meeting Corpus
Koji Tanaka | Chenhui Chu | Haolin Ren | Benjamin Renoust | Yuta Nakashima | Noriko Takemura | Hajime Nagahara | Takao Fujikawa
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration. The corpus construction method is based on image processing and OCR. We digitize and transcribe texts of the specific topic of public meeting. Experiments show that our proposed method achieves a F-score of 87.8% for corpus construction. As a result, we built a content search tool for temporal and semantic content analysis.


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Dialogue-Act Prediction of Future Responses Based on Conversation History
Koji Tanaka | Junya Takayama | Yuki Arase
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.