Naoya Ueda


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TMU Feedback Comment Generation System Using Pretrained Sequence-to-Sequence Language Models
Naoya Ueda | Mamoru Komachi
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

In this paper, we introduce our Tokyo Metropolitan University Feedback Comment Generation system submitted to the feedback comment generation task for INLG 2023 Generation Challenge. In this task, a source sentence and offset range of preposition uses are given as the input. Then, a system generates hints or explanatory notes about preposition uses as the output. To tackle this generation task, we finetuned pretrained sequence-to-sequence language models. The models using BART and T5 showed significant improvement in BLEU score, demonstrating the effectiveness of the pretrained sequence-to-sequence language models in this task. We found that using part-of-speech tag information as an auxiliary input improves the generation quality of feedback comments. Furthermore, we adopt a simple postprocessing method that can enhance the reliability of the generation. As a result, our system achieved the F1 score of 47.4 points in BLEU-based evaluation and 60.9 points in manual evaluation, which ranked second and third on the leaderboard.


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Japanese Named Entity Recognition from Automatic Speech Recognition Using Pre-trained Models
Seiichiro Kondo | Naoya Ueda | Teruaki Oka | Masakazu Sugiyama | Asahi Hentona | Mamoru Komachi
Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation