Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (N19-1)
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| Challenge: | Existing methods to categorize sentiments and emotions in text are limited. |
| Approach: | They propose to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. |
| Outcome: | The proposed method improves on a recently published dataset for categorizing human needs. |
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| Challenge: | Existing QA systems do not have commonsense knowledge or cannot reason with it. |
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| Challenge: | Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs. |
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Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph. |
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Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
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Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
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I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
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