Papers with knowledge-enhanced models
CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding (2022.emnlp-demos)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Yuyang Zhou, Pengfei Cao, Yubo Chen, Zhipeng Xue, Kang Liu, Jun Zhao
| Challenge: | Existing knowledge-enhanced methods are limited to knowledge-intensive tasks. |
| Approach: | They propose a knowledge-enhanced text representation toolkit for natural language understanding . it combines knowledge acquisition, knowledge representation, knowledge injection and knowledge application . |
| Outcome: | The proposed toolkit supports knowledge acquisition, knowledge representation, knowledge injection, and knowledge application. |
It’s not Rocket Science: Interpreting Figurative Language in Narratives (2022.tacl-1)
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| Challenge: | Existing text representations by design rely on compositionality, while figurative language is often non-compositional. |
| Approach: | They propose to use a pre-trained language model to interpret figurative language types to adopt human strategies for interpreting figurativ language types: inferring meaning from context and relying on constituent words’ literal meanings. |
| Outcome: | The proposed models perform significantly worse than humans on discriminative and generative tasks, bridging the gap from human performance. |