Papers by Takuya Ohko
Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models (2024.findings-acl)
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| Challenge: | generative approach to multilingual sentiment classification is based on syntactic and lexical knowledge and requires retraining and tuning. |
| Approach: | They propose to use a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification without retraining LLMs. |
| Outcome: | The proposed approach reduces the multilingual sentiment classification error by 33 points and performs well even for nongenerative tasks such as topic classification and sentiment polarity judgment. |
Incorporating Syntactic Knowledge into Pre-trained Language Model using Optimization for Overcoming Catastrophic Forgetting (2023.findings-emnlp)
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| Challenge: | Pre-trained language models lack syntactic knowledge for many tasks that handle complex or long sentences. |
| Approach: | They propose to use pre-trained language models to incorporate syntactic knowledge into a model by adding additional syntatic knowledge to the model. |
| Outcome: | The proposed model can be easily applied to downstream tasks that require syntactic knowledge. |