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We formulate the problem of understanding MSEQs as a semantic labeling task over an open representation that makes minimal assumptions about schema or ontology-specific semantic vocabulary. At the core of our model, we use a BiLSTM (bidirectional LSTM) conditional random field (CRF), and to overcome the challenges of operating with low training data, we supplement it by using BERT embeddings, hand-designed features, as well as hard and soft constraints spanning multiple sentences. We find that this results in a 12\u201315 points gain over a vanilla BiLSTM CRF. We demonstrate the strengths of our work using the novel task of answering real-world entity-seeking questions from the tourism domain. The use of our labels helps answer 36% more questions with 35% more (relative) accuracy as compared to baselines. We also demonstrate how our framework can rapidly enable the parsing of MSEQs in an entirely new domain with small amounts of training data and little change in the semantic representation.<\/jats:p>","DOI":"10.1017\/s1351324920000017","type":"journal-article","created":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T09:48:42Z","timestamp":1581587322000},"page":"65-87","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":10,"title":["Constrained BERT BiLSTM CRF for understanding multi-sentence entity-seeking questions"],"prefix":"10.1017","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6843-1961","authenticated-orcid":false,"given":"Danish","family":"Contractor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barun","family":"Patra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Mausam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parag","family":"Singla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2020,2,13]]},"reference":[{"key":"S1351324920000017_ref60","unstructured":"Wolf, T. , Debut, L. , Sanh, V. , Chaumond, J. , Delangue, C. , Moi, A. , Cistac, P. , Rault, T. , Louf, R. , Funtowicz, M. and Brew, J. 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Pages 4171\u20134186 of: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Volume 1 (long and short papers)."},{"key":"S1351324920000017_ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2076"},{"key":"S1351324920000017_ref11","unstructured":"Chen, D. , Fisch, A. , Weston, J. and Bordes, A. (2017). Reading wikipedia to answer open-domain questions. Pages 1870\u20131879 of: Barzilay, R. and Kan, M.-Y. (eds), ACL (1). Association for Computational Linguistics."},{"key":"S1351324920000017_ref16","unstructured":"Das, R. , Zaheer, M. , Reddy, S. and McCallum, A. (2017). Question answering on knowledge bases and text using universal schema and memory networks. 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Pages 142\u2013148 of: NAACL HLT 2016, the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12\u201317, 2016."},{"key":"S1351324920000017_ref66","unstructured":"Zettlemoyer, L.S. (2009). Learning to Map Sentences to Logical Form. PhD Thesis, Massachusetts Institute of Technology."},{"key":"S1351324920000017_ref2","unstructured":"Baker, C.F. , Fillmore, C.J. and Lowe, J.B. (1998). The Berkeley FrameNet Project. Pages 86\u201390 of: Proceedings of the 17th International Conference on Computational Linguistics\u2014Volume 1, COLING \u201998. Stroudsburg, PA, USA: Association for Computational Linguistics."},{"key":"S1351324920000017_ref39","unstructured":"Palmer, M. , Hwa, R. and Riedel, S. (eds). (2017). Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9\u201311, 2017. Association for Computational Linguistics."},{"key":"S1351324920000017_ref33","unstructured":"Liang, P.S. (2011). Learning Dependency-Based Compositional Semantics. PhD Thesis, University of California, Berkeley."},{"key":"S1351324920000017_ref9","unstructured":"Bordes, A. , Usunier, N. , Chopra, S. and Weston, J. (2015). Large-scale simple question answering with memory networks. arxiv preprint arxiv:1506.02075."},{"key":"S1351324920000017_ref45","unstructured":"Romeo, S. , Da San Martino, G. , Barron-Cedeno, A. , Moschitti, A. , Belinkov, Y. , Hsu, W.-N. , Zhang, Y. , Mohtarami, M. and Glass, J. (2016). Neural attention for learning to rank questions in community question answering. In Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan."},{"key":"S1351324920000017_ref22","unstructured":"Guo, S. , Liu, K. , He, S. , Liu, C. , Zhao, J. and Wei, Z. (2017). IJCNLP-2017 task 5: Multi-choice question answering in examinations. 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