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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.20708 |
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| _version_ | 1866909213962272768 |
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| author | Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke |
| author_facet | Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke |
| contents | Thinking about the future is one of the important activities that people do in daily life. Futurists also pay a lot of effort into figuring out possible scenarios for the future. We argue that the exploration of this direction is still in an early stage in the NLP research. To this end, we propose three argument generation tasks in the financial application scenario. Our experimental results show these tasks are still big challenges for representative generation models. Based on our empirical results, we further point out several unresolved issues and challenges in this research direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FinGen: A Dataset for Argument Generation in Finance Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke Computation and Language Artificial Intelligence Thinking about the future is one of the important activities that people do in daily life. Futurists also pay a lot of effort into figuring out possible scenarios for the future. We argue that the exploration of this direction is still in an early stage in the NLP research. To this end, we propose three argument generation tasks in the financial application scenario. Our experimental results show these tasks are still big challenges for representative generation models. Based on our empirical results, we further point out several unresolved issues and challenges in this research direction. |
| title | FinGen: A Dataset for Argument Generation in Finance |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.20708 |