Time-Aware Representation Learning for Time-Sensitive Question Answering (2023.findings-emnlp)
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| Challenge: | Existing QA datasets do not include sufficient time expressions, and language models have difficulty understanding the relationships between time specifiers and numerical values. |
| Approach: | They propose a Time-Context-dependent Span Extraction task and build a time-context dependent data generation framework for model training. |
| Outcome: | The proposed model outperforms baseline models up to 8.5 of the F1-score in the TimeQA dataset. |
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| Challenge: | Existing solutions for temporal knowledge graph question answering lack sufficient temporal constraints in retrieval process. |
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Question Answering as Programming for Solving Time-Sensitive Questions (2023.emnlp-main)
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| Challenge: | Recent studies show that Large Language Models (LLMs) have shown remarkable intelligence in question answering. |
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