The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning Natural Language Inference with LSTM
Open accessAbstract
Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications.The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI).In this paper, we propose a special long short-term memory (LSTM) architecture for NLI.Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea.Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution uses a match-LSTM to perform wordby-word matching of the hypothesis with the premise.This LSTM is able to place more emphasis on important word-level matching results.In particular, we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label.On the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the state of the art.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293
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
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
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,620
NewsQA: A Machine Comprehension Dataset
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K. (2017) · Proceedings of the 2nd Workshop on Representation Learning for NLP · doi:10.18653/v1/w17-2623 · Cited by 706
“The first neural model is the match-LSTM (mLSTM) system of Wang & Jiang (2016b).”
MethodInfluentialGated Self-Matching Networks for Reading Comprehension and Question Answering
Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1018 · Cited by 666
R3: Reinforced Ranker-Reader for Open-Domain Question Answering
Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12053 · Cited by 351
Self-citation
Metadata
- Authors
- Wang, S., & Jiang, J.
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Pages
- 1442-1451
- OpenAlex
- W2963542836
- Cited by
- 408
- References
- 4
- FWCI
- 33.40
- Type
- conference paper
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning Natural Language Inference with LSTM. https://app.cochat.ai/papers/10.18653/v1/n16-1170
- The paper
- Wang, S., & Jiang, J. (2016). Learning Natural Language Inference with LSTM. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. https://doi.org/10.18653/v1/n16-1170
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
