The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Decomposable Attention Model for Natural Language Inference
Open accessAbstract
We propose a simple neural architecture for natural language inference.Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable.On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information.Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Order-Embeddings of Images and Language
Vendrov, I., Kiros, R., Fidler, S., & Urtasun, R. (2015) · arXiv · doi:10.48550/arxiv.1511.06361 · Cited by 296
Cited by
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
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A., Nangia, N., & Bowman, S. (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/n18-1101 · Cited by 3,342
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865
Metadata
- Authors
- Parikh, A., Täckström, O., Das, D., & Uszkoreit, J.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 2249-2255
- OpenAlex
- W2413794162
- Cited by
- 1,296
- References
- 2
- FWCI
- 83.68
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Parikh, A., Täckström, O., Das, D., & Uszkoreit, J. (2016). A Decomposable Attention Model for Natural Language Inference. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d16-1244
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