Papers by Taiki Watanabe
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (D19-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is one of the important basic technologies for Natural Language Processing (NLP) . |
| Approach: | They propose to use long short-term memory (LSTM) of NER model to capture chemical com- pound paraphrases by sharing parameters of LSTM and character embeddings be- tween the two models. |
| Outcome: | The proposed method improves chemi- cal NER and achieves state-of-the-art performance on the BioCreative IV’s CHEMDNER task. |
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites. |
| Approach: | They propose a large-scale document-based QA dataset that requires both visual and textual information to answer questions. |
| Outcome: | The proposed dataset incorporates multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. |