Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Author:
Affiliation:
1. Tsinghua University, Beijing, China
2. Amazon, Seattle, WA, USA
3. The Chinese University of Hong Kong, Hong Kong, Hong Kong
4. University of Illinois at Chicago, Chicago, IL, USA
Funder
National Key Research and Development Program of China
NSF (National Science Foundation)
Research Grants Council of the Hong Kong Special Administrative Region, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3539618.3592072
Reference36 articles.
1. TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
2. Interpretable Neural Predictions with Differentiable Binary Variables
3. UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
4. Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
5. Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
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1. Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
2. DoreBer: Document-Level Relation Extraction Method Based on BernNet;IEEE Access;2023
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