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The Web as a Knowledge-Base for Answering Complex Questions
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Alon Talmor, Jonathan Berant. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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“As for other KBQA datasets, e.g., ComplexWebQuestions (Talmor and Berant, 2018), the proportion of implicit relations is also small.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Talmor and Berant, 2018
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- Y. Artzi and L. Zettlemoyer. 2013. Weakly supervised learning of semantic parsers for mapping instruc- tions to actions. Transactions of the Association for Computational Linguistics (TACL) 1:49-62.
- J. Bao, N. Duan, Z. Yan, M. Zhou, and T. Zhao. 2016. Constraint-based question answering with knowl- edge graph. In International Conference on Com- putational Linguistics (COLING).
- J. Berant, A. Chou, R. Frostig, and P. Liang. 2013. Se- mantic parsing on Freebase from question-answer pairs. In Empirical Methods in Natural Language Processing (EMNLP).
- J. Berant and P. Liang. 2015. Imitation learning of agenda-based semantic parsers. Transactions of the Association for Computational Linguistics (TACL) 3:545-558.
- K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In International Conference on Management of Data (SIGMOD). pages 1247-1250.
- C. Buck, J. Bulian, M. Ciaramita, A. Gesmundo, N. Houlsby, W. Gajewski, and W. Wang. 2017. Ask the right questions: Active question reformu- lation with reinforcement learning. arXiv preprint arXiv:1705.07830 . · arXiv
- D. Chen, J. Bolton, and C. D. Manning. 2016. A thor- ough examination of the CNN / Daily Mail reading comprehension task. In Association for Computa- tional Linguistics (ACL).
- D. Chen, A. Fisch, J. Weston, and A. Bordes. 2017. Reading Wikipedia to answer open-domain ques- tions. arXiv preprint arXiv:1704.00051 . · arXiv
- K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Ben- gio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 . · arXiv
- C. Clark and M. Gardner. 2017. Simple and effec- tive multi-paragraph reading comprehension. arXiv preprint arXiv:1710.10723 . · arXiv
- J. Duchi, E. Hazan, and Y. Singer. 2010. Adaptive sub- gradient methods for online learning and stochastic optimization. In Conference on Learning Theory (COLT).
- M. Dunn, , L. Sagun, M. Higgins, U. Guney, V. Cirik, and K. Cho. 2017. SearchQA: A new Q&A dataset augmented with context from a search engine. arXiv .
- K. Guu, P. Pasupat, E. Z. Liu, and P. Liang. 2017. From language to programs: Bridging reinforce- ment learning and maximum marginal likelihood. In Association for Computational Linguistics (ACL).
- K. M. Hermann, T. Koisk, E. Grefenstette, L. Espe- holt, W. Kay, M. Suleyman, and P. Blunsom. 2015. Teaching machines to read and comprehend. In Ad- vances in Neural Information Processing Systems (NIPS).
- D. Hewlett, A. Lacoste, L. Jones, I. Polosukhin, A. Fandrianto, J. Han, M. Kelcey, and D. Berthelot. 2016. Wikireading: A novel large-scale language understanding task over Wikipedia. In Association for Computational Linguistics (ACL).
- F. Hill, A. Bordes, S. Chopra, and J. Weston. 2015. The goldilocks principle: Reading children's books with explicit memory representations. In International Conference on Learning Representations (ICLR).
- M. Iyyer, W. Yih, and M. Chang. 2016. Answering complicated question intents expressed in decom- posed question sequences. CoRR 0.
- R. Jia and P. Liang. 2017. Adversarial examples for evaluating reading comprehension systems. In Em- pirical Methods in Natural Language Processing (EMNLP).
- M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised chal- lenge dataset for reading comprehension. In Associ- ation for Computational Linguistics (ACL).
- J. Krishnamurthy and T. Mitchell. 2012. Weakly supervised training of semantic parsers. In Em- pirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL). pages 754-765.
- T. Kwiatkowski, E. Choi, Y. Artzi, and L. Zettlemoyer. 2013. Scaling semantic parsers with on-the-fly on- tology matching. In Empirical Methods in Natural Language Processing (EMNLP).
- G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy. 2017. Race: Large-scale reading comprehen- sion dataset from examinations. arXiv preprint arXiv:1704.04683 . · arXiv
- K. lee, M. Lewis, and L. Zettlemoyer. 2016. Global neural CCG parsing with optimality guarantees. In Empirical Methods in Natural Language Processing (EMNLP).
- P. Liang. 2013. Lambda dependency-based composi- tional semantics. arXiv preprint arXiv:1309.4408 . · arXiv
- P. Liang, M. I. Jordan, and D. Klein. 2011. Learn- ing dependency-based compositional semantics. In Association for Computational Linguistics (ACL). pages 590-599.
- T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In Workshop on Cognitive Computing at NIPS.
- R. Nogueira and K. Cho. 2016. End-to-end goal-driven web navigation. In Advances in Neural Information Processing Systems (NIPS).
- T. Onishi, H. Wang, M. Bansal, K. Gimpel, and D. McAllester. 2016. Who did what: A large-scale person-centered cloze dataset. In Empirical Meth- ods in Natural Language Processing (EMNLP).
- P. Pasupat and P. Liang. 2015. Compositional semantic parsing on semi-structured tables. In Association for Computational Linguistics (ACL).
- J. Pennington, R. Socher, and C. D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP).
- M. Steedman. 2000. The Syntactic Process. MIT Press.
- I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. In Ad- vances in Neural Information Processing Systems (NIPS). pages 3104-3112.
- O. Vinyals, M. Fortunato, and N. Jaitly. 2015. Pointer networks. In Advances in Neural Information Pro- cessing Systems (NIPS). pages 2674-2682.
- W. Wang, N. Yang, F. Wei, B. Chang, and M. Zhou. 2017. Gated self-matching networks for reading comprehension and question answering. In Associ- ation for Computational Linguistics (ACL).
- Y. Wang, J. Berant, and P. Liang. 2015. Building a semantic parser overnight. In Association for Com- putational Linguistics (ACL).
- Y. Watanabe, B. Dhingra, and R. Salakhutdinov. 2017. Question answering from unstructured text by retrieval and comprehension. arXiv preprint arXiv:1703.08885 . · arXiv
- J. Welbl, P. Stenetorp, and S. Riedel. 2017. Construct- ing datasets for multi-hop reading comprehension across documents. arXiv preprint arXiv:1710.06481 . · arXiv
- W. Yih, M. Richardson, C. Meek, M. Chang, and J. Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In Associ- ation for Computational Linguistics (ACL).
- M. Zelle and R. J. Mooney. 1996. Learning to parse database queries using inductive logic program- ming. In Association for the Advancement of Arti- ficial Intelligence (AAAI). pages 1050-1055.
- L. S. Zettlemoyer and M. Collins. 2005. Learning to map sentences to logical form: Structured classifica- tion with probabilistic categorial grammars. In Un- certainty in Artificial Intelligence (UAI). pages 658- 666.
- V. Zhong, C. Xiong, and R. Socher. 2017. Seq2sql: Generating structured queries from natural lan- guage using reinforcement learning. arXiv preprint arXiv:1709.00103 . · arXiv
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Metadata
- Authors
- Talmor, A., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 641-651
- OpenAlex
- W2793065281
- Cited by
- 520
- References
- 0
- Citations by year
- 2026: 75 · 2025: 60 · 2024: 42 · 2023: 74 · 2022: 75 · 2021: 78 · 2020: 59 · 2019: 47 · 2018: 10
- FWCI
- 19.72
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Israel Science Foundation
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- complex question answering, question answering, knowledge base construction
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