Papers with bottom-up strategy
Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)
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| Challenge: | Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks. |
| Approach: | They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks . |
| Outcome: | The proposed model performs better than existing models on three benchmark datasets. |
From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base (2024.lrec-main)
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| Challenge: | Traditional Ecological Knowledge (TEK) is a shared cultural heritage and crucial instrument to tackle environmental challenges. |
| Approach: | They propose to build a language resource based on Shanhai Jing (the classic of mountains and seas) written 2000 years ago and uses a stylized narrative and juxtaposition of knowledge from multiple domains to build the knowledge base. |
| Outcome: | The proposed knowledge base contains 1432 systematically classified entities and 3294 relationships. |