---
title: "Bilateral Multi-Perspective Matching for Natural — Wang 2017"
description: "The paper addresses natural language sentence matching (NLSM): deciding how two sentences relate, a problem underlying tasks such as recognizing…"
authors: ["Zhiguo Wang", "Wael Hamza", "Radu Florian"]
year: 2017
venue: "arXiv:1702.03814"
source: "https://arxiv.org/abs/1702.03814"
doi: "10.48550/arxiv.1702.03814"
arxiv: "1702.03814"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2017-bilateral-multi-perspective-matching-natural/"
---
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# Bilateral Multi-Perspective Matching for Natural Language Sentences

**Authors:** [Zhiguo Wang](https://md.research.elamur.ai/free/en/authors/zhiguo-wang/), [Wael Hamza](https://md.research.elamur.ai/free/en/authors/wael-hamza/), [Radu Florian](https://md.research.elamur.ai/free/en/authors/radu-florian/) · **Year:** 2017 · **Venue:** arXiv:1702.03814 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1702.03814) · [PDF](https://arxiv.org/pdf/1702.03814) · [DOI 10.48550/arxiv.1702.03814](https://doi.org/10.48550/arxiv.1702.03814) · [arXiv 1702.03814](https://arxiv.org/abs/1702.03814) · [OpenAlex](https://openalex.org/W2593833795)

**Citations:** 739 citations (openalex-snapshot · 2026-10-14) · [184](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses natural language sentence matching (NLSM): deciding how two sentences relate, a problem underlying tasks such as recognizing paraphrases, judging the inferential relationship between a premise and a hypothesis, and choosing the sentence that answers a question. The authors argue that earlier "matching-aggregation" systems had two weaknesses. They compared sentences in only one direction, or they used only one level of granularity, such as word-to-word. Their BiMPM model addresses both. Each word is represented by combining a pretrained vector (GloVe or word2vec) with a character-level LSTM embedding. A shared BiLSTM then adds context. The core layer compares every time-step of one sentence with the entire other sentence, in both directions. It does this through a multi-perspective cosine function, where each perspective is a learned weighting of the dimensions, and four matching strategies. A second BiLSTM condenses the results into a fixed-length vector, which a two-layer feed-forward network with softmax classifies. The authors report state-of-the-art or competitive results on all three tasks, which shows the architecture works as a general tool for sentence-pair problems.

## Key points
- The matching operation uses l learned perspectives, each a row of a weight matrix W ∈ ℝ^{l×d}. Its four strategies are Full-Matching, Maxpooling-Matching, Attentive-Matching and Max-Attentive-Matching.
- On SNLI, matching the hypothesis against the premise (P ← Q) clearly beat the opposite direction. The full bilateral model improved further. An ensemble of four BiMPM models with different random seeds outperformed the ensemble of Chen et al. (2016).
- For paraphrase identification, BiMPM beat a re-implemented L.D.C. model by more than 2 points. Multi-Perspective-CNN/LSTM baselines also clearly beat their Siamese-CNN/LSTM counterparts.
- For answer selection on TREC-QA (clean version), BiMPM reached 0.802 MAP and 0.875 MRR, roughly tied with the best prior system (0.801 / 0.877).
- On WikiQA, BiMPM reached 0.718 MAP and 0.731 MRR, below the best listed baseline (Wang and Jiang, 2016: 0.743 / 0.755). The authors nonetheless describe this as on par.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

184 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 176 not yet.

- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://md.research.elamur.ai/free/en/papers/chistova-2024-bilingual-rhetorical-structure-parsing-large/)
- [Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/guo-2020-incorporating-syntax-frame-semantics-neural/)
- [End-to-End Bias Mitigation by Modelling Biases in Corpora](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/)
- [Extractive Summarization as Text Matching](https://md.research.elamur.ai/free/en/papers/zhong-2020-extractive-summarization-as-text-matching/)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wang2017bilateral,
  title = {{Bilateral Multi-Perspective Matching for Natural Language Sentences}},
  author = {Wang, Zhiguo and Hamza, Wael and Florian, Radu},
  year = {2017},
  doi = {10.48550/arxiv.1702.03814},
  eprint = {1702.03814},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1702.03814}
}
```

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## References

37 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326, 2015. [find](https://scholar.google.com/scholar?q=Samuel+R+Bowman%2C+Gabor+Angeli%2C+Christopher+Potts%2C+and+Christopher+D+Manning%2E+A+large+annotated+corpus+for+learning+natural+language+inference%2E+arXiv+preprint+arXiv%3A1508%2E05326%2C+2015%2E)
2. Jane Bromley, James W. Bentz, Léon Bottou, Isabelle Guyon, Yann LeCun, Cliff Moore, Eduard Säckinger, and Roopak Shah. Signature verification using a ”siamese” time delay neural network. IJPRAI, 7(4):669– 688, 1993. [find](https://scholar.google.com/scholar?q=Jane+Bromley%2C+James+W%2E+Bentz%2C+Le%CC%81on+Bottou%2C+Isabelle+Guyon%2C+Yann+LeCun%2C+Cliff+Moore%2C+Eduard+Sa%CC%88ckinger%2C+and+Roopak+Shah%2E+Signature+verification+using+a+%E2%80%9Dsiamese%E2%80%9D+time+delay+neural+network%2E+IJPRAI%2C+7%284)
3. Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. Enhancing and combining sequential and tree lstm for natural language inference. arXiv preprint arXiv:1609.06038, 2016. [find](https://scholar.google.com/scholar?q=Qian+Chen%2C+Xiaodan+Zhu%2C+Zhenhua+Ling%2C+Si+Wei%2C+and+Hui+Jiang%2E+Enhancing+and+combining+sequential+and+tree+lstm+for+natural+language+inference%2E+arXiv+preprint+arXiv%3A1609%2E06038%2C+2016%2E)
4. Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733, 2016. [find](https://scholar.google.com/scholar?q=Jianpeng+Cheng%2C+Li+Dong%2C+and+Mirella+Lapata%2E+Long+short%2Dterm+memory%2Dnetworks+for+machine+reading%2E+arXiv+preprint+arXiv%3A1601%2E06733%2C+2016%2E) [our card](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/)
5. Hua He and Jimmy Lin. Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In NAACL, 2016. [find](https://scholar.google.com/scholar?q=Hua+He+and+Jimmy+Lin%2E+Pairwise+word+interaction+modeling+with+deep+neural+networks+for+semantic+similarity+measurement%2E+In+NAACL%2C+2016%2E)
6. Michael Heilman and Noah A Smith. Tree edit models for recognizing textual entailments, paraphrases, and answers to questions. In NAACL, 2010. [find](https://scholar.google.com/scholar?q=Michael+Heilman+and+Noah+A+Smith%2E+Tree+edit+models+for+recognizing+textual+entailments%2C+paraphrases%2C+and+answers+to+questions%2E+In+NAACL%2C+2010%2E) [our card](https://md.research.elamur.ai/free/en/papers/heilman-2010-tree-edit-models-recognizing-textual/)
7. Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%3A1735%E2%80%931780%2C+1997%2E)
8. Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. [find](https://scholar.google.com/scholar?q=Diederik+Kingma+and+Jimmy+Ba%2E+Adam%3A+A+method+for+stochastic+optimization%2E+arXiv+preprint+arXiv%3A1412%2E6980%2C+2014%2E)
9. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436– 444, 2015. [find](https://scholar.google.com/scholar?q=Yann+LeCun%2C+Yoshua+Bengio%2C+and+Geoffrey+Hinton%2E+Deep+learning%2E+Nature%2C+521%287553%29%3A436%E2%80%93+444%2C+2015%2E)
10. Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Modelling interaction of sentence pair with coupled-lstms. arXiv preprint arXiv:1605.05573, 2016. [find](https://scholar.google.com/scholar?q=Pengfei+Liu%2C+Xipeng+Qiu%2C+and+Xuanjing+Huang%2E+Modelling+interaction+of+sentence+pair+with+coupled%2Dlstms%2E+arXiv+preprint+arXiv%3A1605%2E05573%2C+2016%2E)
11. Yang Liu, Chengjie Sun, Lei Lin, and Xiaolong Wang. Learning natural language inference using bidirectional lstm model and inner-attention. arXiv preprint arXiv:1605.09090, 2016. — [arXiv:1605.09090](https://arxiv.org/abs/1605.09090) [find](https://scholar.google.com/scholar?q=Yang+Liu%2C+Chengjie+Sun%2C+Lei+Lin%2C+and+Xiaolong+Wang%2E+Learning+natural+language+inference+using+bidirectional+lstm+model+and+inner%2Dattention%2E+arXiv+preprint+arXiv%3A1605%2E09090%2C+2016%2E+%E2%80%94+%5BarXiv%3A1605%2E09090%5D%28)
12. Yishu Miao, Lei Yu, and Phil Blunsom. Neural variational inference for text processing. In ICML, 2016. [find](https://scholar.google.com/scholar?q=Yishu+Miao%2C+Lei+Yu%2C+and+Phil+Blunsom%2E+Neural+variational+inference+for+text+processing%2E+In+ICML%2C+2016%2E)
13. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119, 2013. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Ilya+Sutskever%2C+Kai+Chen%2C+Greg+S+Corrado%2C+and+Jeff+Dean%2E+Distributed+representations+of+words+and+phrases+and+their+compositionality%2E+In+Advances+in+neural+information+processing+system)
14. Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. Natural language inference by tree-based convolution and heuristic matching. arXiv preprint arXiv:1512.08422, 2015. [find](https://scholar.google.com/scholar?q=Lili+Mou%2C+Rui+Men%2C+Ge+Li%2C+Yan+Xu%2C+Lu+Zhang%2C+Rui+Yan%2C+and+Zhi+Jin%2E+Natural+language+inference+by+tree%2Dbased+convolution+and+heuristic+matching%2E+arXiv+preprint+arXiv%3A1512%2E08422%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/)
15. Tsendsuren Munkhdalai and Hong Yu. Neural tree indexers for text understanding. arXiv preprint arXiv:1607.04492, 2016. [find](https://scholar.google.com/scholar?q=Tsendsuren+Munkhdalai+and+Hong+Yu%2E+Neural+tree+indexers+for+text+understanding%2E+arXiv+preprint+arXiv%3A1607%2E04492%2C+2016%2E)
16. Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933, 2016. [find](https://scholar.google.com/scholar?q=Ankur+P+Parikh%2C+Oscar+Ta%CC%88ckstro%CC%88m%2C+Dipanjan+Das%2C+and+Jakob+Uszkoreit%2E+A+decomposable+attention+model+for+natural+language+inference%2E+arXiv+preprint+arXiv%3A1606%2E01933%2C+2016%2E) [our card](https://md.research.elamur.ai/free/en/papers/parikh-2016-decomposable-attention-model-natural-language/)
17. Xi Peng, Junzhou Huang, Qiong Hu, Shaoting Zhang, Ahmed Elgammal, and Dimitris Metaxas. From circle to 3-sphere: Head pose estimation by instance parameterization. Computer Vision and Image Understanding, 136:92–102, 2015. [find](https://scholar.google.com/scholar?q=Xi+Peng%2C+Junzhou+Huang%2C+Qiong+Hu%2C+Shaoting+Zhang%2C+Ahmed+Elgammal%2C+and+Dimitris+Metaxas%2E+From+circle+to+3%2Dsphere%3A+Head+pose+estimation+by+instance+parameterization%2E+Computer+Vision+and+Image+Understand)
18. Xi Peng, Shaoting Zhang, Yu Yang, and Dimitris N Metaxas. Piefa: Personalized incremental and ensemble face alignment. In Proceedings of the IEEE International Conference on Computer Vision, pages 3880– 3888, 2015. [find](https://scholar.google.com/scholar?q=Xi+Peng%2C+Shaoting+Zhang%2C+Yu+Yang%2C+and+Dimitris+N+Metaxas%2E+Piefa%3A+Personalized+incremental+and+ensemble+face+alignment%2E+In+Proceedings+of+the+IEEE+International+Conference+on+Computer+Vision%2C+pages+388)
19. Xi Peng, Rogerio S Feris, Xiaoyu Wang, and Dimitris N Metaxas. A recurrent encoder-decoder network for sequential face alignment. In European Conference on Computer Vision, pages 38–56. Springer International Publishing, 2016. [find](https://scholar.google.com/scholar?q=Xi+Peng%2C+Rogerio+S+Feris%2C+Xiaoyu+Wang%2C+and+Dimitris+N+Metaxas%2E+A+recurrent+encoder%2Ddecoder+network+for+sequential+face+alignment%2E+In+European+Conference+on+Computer+Vision%2C+pages+38%E2%80%9356%2E+Springer+Inter)
20. Xi Peng, Shaoting Zhang, Yang Yu, and Dimitris N Metaxas. Toward personalized modeling: Incremental and ensemble alignment for sequential faces in the wild. International Journal of Computer Vision, pages 1–14, 2017. [find](https://scholar.google.com/scholar?q=Xi+Peng%2C+Shaoting+Zhang%2C+Yang+Yu%2C+and+Dimitris+N+Metaxas%2E+Toward+personalized+modeling%3A+Incremental+and+ensemble+alignment+for+sequential+faces+in+the+wild%2E+International+Journal+of+Computer+Vision%2C+p)
21. Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In EMNLP, 2014. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+D+Manning%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+EMNLP%2C+2014%2E)
22. Jinfeng Rao, Hua He, and Jimmy Lin. Noise-contrastive estimation for answer selection with deep neural networks. In CIKM, 2016. [find](https://scholar.google.com/scholar?q=Jinfeng+Rao%2C+Hua+He%2C+and+Jimmy+Lin%2E+Noise%2Dcontrastive+estimation+for+answer+selection+with+deep+neural+networks%2E+In+CIKM%2C+2016%2E)
23. Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom. Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664, 2015. [find](https://scholar.google.com/scholar?q=Tim+Rockta%CC%88schel%2C+Edward+Grefenstette%2C+Karl+Moritz+Hermann%2C+Toma%CC%81s%CC%8C+Koc%CC%8Cisky%CC%80%2C+and+Phil+Blunsom%2E+Reasoning+about+entailment+with+neural+attention%2E+arXiv+preprint+arXiv%3A1509%2E06664%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/rocktaschel-2015-reasoning-about-entailment-neural-attention/)
24. Cicero dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. Attentive pooling networks. arXiv preprint arXiv:1602.03609, 2016. — [arXiv:1602.03609](https://arxiv.org/abs/1602.03609) [find](https://scholar.google.com/scholar?q=Cicero+dos+Santos%2C+Ming+Tan%2C+Bing+Xiang%2C+and+Bowen+Zhou%2E+Attentive+pooling+networks%2E+arXiv+preprint+arXiv%3A1602%2E03609%2C+2016%2E+%E2%80%94+%5BarXiv%3A1602%2E03609%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1602%2E03609%29)
25. Lei Sha, Baobao Chang, Zhifang Sui, and Sujian Li. Reading and thinking: Re-read lstm unit for textual entailment recognition. In COLING, 2016. [find](https://scholar.google.com/scholar?q=Lei+Sha%2C+Baobao+Chang%2C+Zhifang+Sui%2C+and+Sujian+Li%2E+Reading+and+thinking%3A+Re%2Dread+lstm+unit+for+textual+entailment+recognition%2E+In+COLING%2C+2016%2E)
26. Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. Lstm-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108, 2015. — [arXiv:1511.04108](https://arxiv.org/abs/1511.04108) [find](https://scholar.google.com/scholar?q=Ming+Tan%2C+Cicero+dos+Santos%2C+Bing+Xiang%2C+and+Bowen+Zhou%2E+Lstm%2Dbased+deep+learning+models+for+non%2Dfactoid+answer+selection%2E+arXiv+preprint+arXiv%3A1511%2E04108%2C+2015%2E+%E2%80%94+%5BarXiv%3A1511%2E04108%5D%28https%3A%2F%2Farxiv%2Eorg)
27. Ivan Vendrov, Ryan Kiros, Sanja Fidler, and Raquel Urtasun. Order-embeddings of images and language. arXiv preprint arXiv:1511.06361, 2015. [find](https://scholar.google.com/scholar?q=Ivan+Vendrov%2C+Ryan+Kiros%2C+Sanja+Fidler%2C+and+Raquel+Urtasun%2E+Order%2Dembeddings+of+images+and+language%2E+arXiv+preprint+arXiv%3A1511%2E06361%2C+2015%2E)
28. Zhiguo Wang and Abraham Ittycheriah. Faq-based question answering via word alignment. arXiv preprint arXiv:1507.02628, 2015. — [arXiv:1507.02628](https://arxiv.org/abs/1507.02628) [find](https://scholar.google.com/scholar?q=Zhiguo+Wang+and+Abraham+Ittycheriah%2E+Faq%2Dbased+question+answering+via+word+alignment%2E+arXiv+preprint+arXiv%3A1507%2E02628%2C+2015%2E+%E2%80%94+%5BarXiv%3A1507%2E02628%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1507%2E02628%29)
29. Shuohang Wang and Jing Jiang. Learning natural language inference with lstm. arXiv preprint arXiv:1512.08849, 2015. [find](https://scholar.google.com/scholar?q=Shuohang+Wang+and+Jing+Jiang%2E+Learning+natural+language+inference+with+lstm%2E+arXiv+preprint+arXiv%3A1512%2E08849%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/)
30. Shuohang Wang and Jing Jiang. A compare-aggregate model for matching text sequences. arXiv preprint arXiv:1611.01747, 2016. — [arXiv:1611.01747](https://arxiv.org/abs/1611.01747) [find](https://scholar.google.com/scholar?q=Shuohang+Wang+and+Jing+Jiang%2E+A+compare%2Daggregate+model+for+matching+text+sequences%2E+arXiv+preprint+arXiv%3A1611%2E01747%2C+2016%2E+%E2%80%94+%5BarXiv%3A1611%2E01747%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1611%2E01747%29)
31. Mengqiu Wang, Noah A Smith, and Teruko Mitamura. What is the jeopardy model? a quasi-synchronous grammar for qa. In EMNLP, 2007. [find](https://scholar.google.com/scholar?q=Mengqiu+Wang%2C+Noah+A+Smith%2C+and+Teruko+Mitamura%2E+What+is+the+jeopardy+model%3F+a+quasi%2Dsynchronous+grammar+for+qa%2E+In+EMNLP%2C+2007%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/)
32. Bingning Wang, Kang Liu, and Jun Zhao. Inner attention based recurrent neural networks for answer selection. In ACL, 2016. [find](https://scholar.google.com/scholar?q=Bingning+Wang%2C+Kang+Liu%2C+and+Jun+Zhao%2E+Inner+attention+based+recurrent+neural+networks+for+answer+selection%2E+In+ACL%2C+2016%2E)
33. Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. Multi-perspective context matching for machine comprehension. arXiv preprint arXiv:1612.04211, 2016. — [arXiv:1612.04211](https://arxiv.org/abs/1612.04211) [find](https://scholar.google.com/scholar?q=Zhiguo+Wang%2C+Haitao+Mi%2C+Wael+Hamza%2C+and+Radu+Florian%2E+Multi%2Dperspective+context+matching+for+machine+comprehension%2E+arXiv+preprint+arXiv%3A1612%2E04211%2C+2016%2E+%E2%80%94+%5BarXiv%3A1612%2E04211%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F16)
34. Zhiguo Wang, Haitao Mi, and Abraham Ittycheriah. Semi-supervised clustering for short text via deep representation learning. In CoNLL, 2016. [find](https://scholar.google.com/scholar?q=Zhiguo+Wang%2C+Haitao+Mi%2C+and+Abraham+Ittycheriah%2E+Semi%2Dsupervised+clustering+for+short+text+via+deep+representation+learning%2E+In+CoNLL%2C+2016%2E)
35. Zhiguo Wang, Haitao Mi, and Abraham Ittycheriah. Sentence similarity learning by lexical decomposition and composition. In COLING, 2016. [find](https://scholar.google.com/scholar?q=Zhiguo+Wang%2C+Haitao+Mi%2C+and+Abraham+Ittycheriah%2E+Sentence+similarity+learning+by+lexical+decomposition+and+composition%2E+In+COLING%2C+2016%2E)
36. Yi Yang, Wen-tau Yih, and Christopher Meek. Wikiqa: A challenge dataset for open-domain question answering. In EMNLP, 2015. [find](https://scholar.google.com/scholar?q=Yi+Yang%2C+Wen%2Dtau+Yih%2C+and+Christopher+Meek%2E+Wikiqa%3A+A+challenge+dataset+for+open%2Ddomain+question+answering%2E+In+EMNLP%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/yang-2015-wikiqa-challenge-dataset-open-domain/)
37. Wenpeng Yin, Hinrich Schütze, Bing Xiang, and Bowen Zhou. Abcnn: Attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:1512.05193, 2015. [find](https://scholar.google.com/scholar?q=Wenpeng+Yin%2C+Hinrich+Schu%CC%88tze%2C+Bing+Xiang%2C+and+Bowen+Zhou%2E+Abcnn%3A+Attention%2Dbased+convolutional+neural+network+for+modeling+sentence+pairs%2E+arXiv+preprint+arXiv%3A1512%2E05193%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)