Papers by Tetsuji Ogawa
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification (2020.coling-main)
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| Challenge: | Existing methods for recognizing the mental state of characters in text are limited by their use of character-specific contexts. |
| Approach: | They propose a method that encodes the preceding context of the target sentence along with the target phrase using a BERT-based text encoder. |
| Outcome: | The proposed method improves the accuracy of emotion classification by encoding the preceding context of the target sentence along with the target phrase using a BERT encoder. |
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model (2022.findings-emnlp)
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| Challenge: | Existing approaches to connectionist temporal classification (CTC) are based on pre-trained language models (LMs) |
| Approach: | They propose a formulation of connectionist temporal classification that relaxes the conditional independence assumptions used in conventional CTC and incorporates linguistic knowledge through explicit output dependency. |
| Outcome: | The proposed model improves over conventional approaches across variations in speaking styles and languages while maintaining CTC’s training efficiency. |