The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Entity-Relation Extraction as Multi-Turn Question Answering
Open accessAbstract
In this paper, we propose a new paradigm for the task of entity-relation extraction.We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context.This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models.Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models.We are able to obtain the stateof-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively.Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets.The proposed multi-turn QA model also achieves the best performance on the RESUME dataset. 1
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Adam: A Method for Stochastic Optimization
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
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“For the RESUME dataset, we use the strategy of curriculum learning (Bengio et al., 2009), i.e., we gradually increase the number of turns from 2 to 4 at training.”
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys · doi:10.1145/3605943 · Cited by 1,387
A Unified MRC Framework for Named Entity Recognition
Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.519 · Cited by 697
Self-citationA Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531
Large language models are few-shot clinical information extractors
Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., & Sontag, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.130 · Cited by 358
Metadata
- Authors
- Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J.
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 1340-1350
- OpenAlex
- W2949922292
- Cited by
- 336
- References
- 62
- FWCI
- 18.03
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J. (2019). Entity-Relation Extraction as Multi-Turn Question Answering. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1129
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