The Web as a Knowledge-Base for Answering Complex Questions
Open accessAbstract
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.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In ComplexWebQuestions, the proportion of implicit relations is also small.
“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
Section 4.2 describes the proportions of question types in ComplexWebQuestions, but does not report a proportion of implicit relations. Searches for “implicit relations” and related terms found no passage in our copy that addresses that proportion; the reported percentages for word reordering concern a different measure. Page numbers were not available in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 43
- 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. 2016. Data recombination for neu- ral semantic parsing. In Association for Computa- tional Linguistics (ACL).
- 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).
- P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016. Squad: 100,000+ questions for machine comprehen- sion of text. In Empirical Methods in Natural Lan- guage 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
Cited by
Sorted by citations
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804 · see citing works
CoQA: A Conversational Question Answering Challenge
Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975 · see citing works
QuAC: Question Answering in Context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1241 · Cited by 714 · see citing works
“CoQA (Reddy et al., 2018) CSQA (Saha et al., 2018) CQA (Talmor and Berant, 2018) SQA (Iyyer et al., 2017) NarrativeQA (Kocisk´y et al., 2017) TriviaQA (Joshi et al., 2017) SQuAD 2.0 (Rajpurkar et al., 2018) MS Marco (Nguyen et al., 2016) NewsQA (Trischler et al., 2016) Table 1: Comparison of the QUAC dataset to other…”
BackgroundInfluentialOpen Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., & Cohen, W. W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1455 · Cited by 510 · see citing works
Towards Reasoning in Large Language Models: A Survey
Huang, J., & Chang, K. C.-C. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.67 · Cited by 428 · see citing works
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (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/n19-1246 · Cited by 393 · see citing works
“, 2018), requiring passage retrieval (Joshi et al., 2017; Yang et al., 2018; Talmor and Berant, 2018), mismatched passages and questions (Saha et al.”
BackgroundPullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
Sun, H., Bedrax-Weiss, T., & Cohen, W. W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1242 · Cited by 397 · see citing works
KILT: a Benchmark for Knowledge Intensive Language Tasks
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.200 · Cited by 357 · see citing works
Survey on evaluation methods for dialogue systems
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09866-x · Cited by 333 · see citing works
Measuring and Narrowing the Compositionality Gap in Language Models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv · doi:10.48550/arxiv.2210.03350 · Cited by 294 · see citing works
♫ MuSiQue: Multihop Questions via Single-hop Question Composition
Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00475 · Cited by 283 · see citing works
Progress in Neural NLP: Modeling, Learning, and Reasoning
Zhou, M., Duan, N., Liu, S., & Shum, H. (2020) · Engineering · doi:10.1016/j.eng.2019.12.014 · Cited by 295 · see citing works
FinQA: A Dataset of Numerical Reasoning over Financial Data
Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.-H., Routledge, B., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.300 · Cited by 258 · see citing works
“Popular datasets include ComplexWebQuestions (Talmor and Berant, 2018), WikiTableQuestions (Pasupat and Liang, 2015), Spider (Yu et al.”
Measuring and Narrowing the Compositionality Gap in Language Models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.378 · Cited by 303 · see citing works
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287 · see citing works
“ComplexWebQuestions and QAngaroo datasets are also utilized KB when constructing the dataset, but they do not utilize the logical rules as we did.”
BackgroundMethodInfluential<i>Did Aristotle Use a Laptop?</i> A Question Answering Benchmark with Implicit Reasoning Strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., & Berant, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00370 · Cited by 242 · see citing works
Self-citationXie, T., Wu, C. H., Shi, P., Zhong, R., Scholak, T., Yasunaga, M., Wu, C.-S., Zhong, M., Yin, P., Wang, S. I., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.39 · Cited by 271 · see citing works
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
He, G., Lan, Y., Jiang, J., Zhao, W. X., & Wen, J.-R. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441753 · Cited by 228 · see citing works
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1259 · Cited by 222 · see citing works
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
Fisch, A., Talmor, A., Jia, R., Seo, M., Choi, E., & Chen, D. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5801 · Cited by 220 · see citing works
Self-citationHybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Chen, W., Zha, H., Chen, Z., Xiong, W., Wang, H., & Wang, W. Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.91 · Cited by 216 · see citing works
LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia
Dubey, M., Banerjee, D., Abdelkawi, A., & Lehmann, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30796-7_5 · Cited by 194 · see citing works
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T.-S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.254 · Cited by 193 · see citing works
Unsupervised Commonsense Question Answering with Self-Talk
Shwartz, V., West, P., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.373 · Cited by 186 · see citing works
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
Gu, Y., Kase, S., Vanni, M., Sadler, B. M., Liang, P., Yan, X., & Su, Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449992 · Cited by 187 · see citing works
Measuring Compositional Generalization: A Comprehensive Method on Realistic DataPreprint
Keysers, D., Schärli, N., Scales, N., Buisman, H., Furrer, D., Kashubin, S., Momchev, N., Sinopalnikov, D., Stafiniak, Ł., Tihon, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.09713 · Cited by 213 · see citing works
Dynamically Fused Graph Network for Multi-hop Reasoning
Qiu, L., Xiao, Y., Qu, Y., Zhou, H., Li, L., Zhang, W., & Yu, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1617 · Cited by 181 · see citing works
Hierarchical Graph Network for Multi-hop Question Answering
Fang, Y., Sun, S., Gan, Z., Pillai, R., Wang, S., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.710 · Cited by 176 · see citing works
Knowledge Base Question Answering via Encoding of Complex Query Graphs
Luo, K., Lin, F., Luo, X., & Zhu, K. Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1242 · Cited by 174 · see citing works
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions
Lan, Y., He, G., Jiang, J., Jiang, J., Zhao, W. X., & Wen, J.-R. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/611 · Cited by 185 · see citing works
Sequence-to-Sequence Knowledge Graph Completion and Question Answering
Saxena, A., Kochsiek, A., & Gemulla, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.201 · Cited by 168 · see citing works
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Rogers, A., Gardner, M., & Augenstein, I. (2022) · ACM Computing Surveys · doi:10.1145/3560260 · Cited by 177 · see citing works
Can You Unpack That? Learning to Rewrite Questions-in-Context
Elgohary, A., Peskov, D., & Boyd-Graber, J. L. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1605 · Cited by 159 · see citing works
(2020) · doi:10.18653/v1/2020.acl-main.91 · Cited by 214 · see citing works
Compositional Questions Do Not Necessitate Multi-hop Reasoning
Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., & Zettlemoyer, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1416 · Cited by 138 · see citing works
Constrained Language Models Yield Few-Shot Semantic Parsers
Shin, R., Lin, C., Thomson, S., Chen, C., Roy, S., Platanios, E. A., Pauls, A., Klein, D., Eisner, J., & Van Durme, B. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.608 · Cited by 139 · see citing works
“, 2018), reading comprehension (Talmor and Berant, 2018; Yang et al., 2018; Dua et al., 2019; Abujabal et al., 2019), and visual question answering (Johnson et al.”
BackgroundQuestion Answering Over Temporal Knowledge Graphs
Saxena, A., Chakrabarti, S., & Talukdar, P. P. (2021) · doi:10.18653/v1/2021.acl-long.520 · Cited by 153 · see citing works
Ibrahim, N., AboulEla, S., Ibrahim, A. A., & Kashef, R. F. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00175-8 · Cited by 142 · see citing works
Complex Temporal Question Answering on Knowledge Graphs
Jia, Z., Pramanik, S., Roy, R. S., & Weikum, G. (2021) · International Conference on Information and Knowledge Management · doi:10.1145/3459637.3482416 · Cited by 170 · see citing works
Unsupervised Question Decomposition for Question Answering
Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.713 · Cited by 123 · see citing works
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering
Bosselut, A., Bras, R. J. L., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16625 · Cited by 25 · see citing works
Complex Knowledge Base Question Answering: A Survey
Lan, Y., He, G., Jiang, J., Jiang, J., Zhao, W. X., & Wen, J.-R. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3223858 · Cited by 131 · see citing works
Case-based Reasoning for Natural Language Queries over Knowledge Bases
Das, R., Zaheer, M., Thai, D., Godbole, A., Perez, E., Lee, J. Y., Tan, L., Polymenakos, L., & McCallum, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.755 · Cited by 121 · see citing works
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Zhang, J., Zhang, X., Yu, J., Tang, J., Tang, J., Li, C., & Chen, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.396 · Cited by 118 · see citing works
Shi, J., Cao, S., Hou, L., Li, J., & Zhang, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.341 · Cited by 119 · see citing works
SPARQA: Skeleton-Based Semantic Parsing for Complex Questions over Knowledge Bases
Sun, Y., Zhang, L., Cheng, G., & Qu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6426 · Cited by 117 · see citing works
Neural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835533 · Cited by 124 · see citing works
Wang, F., Zhang, Z., Zhang, X., Wu, Z., Mo, T., Lu, Q., Wang, W., Li, R., Xu, J., Tang, X., et al. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3768165 · Cited by 106 · see citing works
Tan, Y., Min, D., Li, Y., Li, W., Hu, N., Chen, Y., & Qi, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47240-4_19 · Cited by 144 · see citing works
Answering Complex Open-domain Questions Through Iterative Query Generation
Qi, P., Lin, X., Mehr, L., Wang, Z., & Manning, C. D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1261 · Cited by 96 · see citing works
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., & Manning, C. D. (2018) · arXiv · doi:10.48550/arxiv.1809.09600 · Cited by 97 · see citing works
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering
Feldman, Y., & El‐Yaniv, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1222 · Cited by 94 · see citing works
Knowledge Graph-Guided Retrieval Augmented Generation
Zhu, X., Xie, Y., Liu, Y., Li, Y., & Hu, W. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.449 · Cited by 91 · see citing works
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1613 · Cited by 197 · see citing works
Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks
Chen, Y., Wu, L., & Zaki, M. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264519 · Cited by 98 · see citing works
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Ke, P., Ji, H., Ran, Y., Cui, X., Wang, L., Song, L., Zhu, X., & Huang, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.223 · Cited by 92 · see citing works
Cao, S., Shi, J., Pan, L., Nie, L., Xiang, Y., Hou, L., Li, J., He, B., & Zhang, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.422 · Cited by 85 · see citing works
Song, L., Wang, Z., Yu, M., Zhang, Y., Florian, R., & Gildea, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02040 · Cited by 82 · see citing works
Ye, Y., Hui, B., Yang, M., Li, B., Huang, F., & Li, Y. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591708 · Cited by 112 · see citing works
WebQA: Multihop and Multimodal QA
Chang, Y., Cao, G., Narang, M., Gao, J., Suzuki, H., & Bisk, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01600 · Cited by 82 · see citing works
Few-shot In-context Learning on Knowledge Base Question Answering
Li, T., Ma, X., Zhuang, A., Gu, Y., Su, Y., & Chen, W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.385 · Cited by 75 · see citing works
Learning to Answer Complex Questions over Knowledge Bases with Query Composition
Bhutani, N., Zheng, X., & Jagadish, H. V. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358033 · Cited by 74 · see citing works
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized ArchitecturesPreprint
Furrer, D., van Zee, M., Scales, N., & Schärli, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08970 · Cited by 79 · see citing works
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning StrategiesPreprint
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., & Berant, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02235 · Cited by 70 · see citing works
Self-citationGraph Neural Networks for Natural Language Processing: A SurveyPreprint
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06090 · Cited by 70 · see citing works
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
Tan, X., Wang, X., Liu, Q., Xu, X., Yuan, X., & Zhang, W. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714892 · Cited by 75 · see citing works
Christmann, P., Saha Roy, R., Abujabal, A., Singh, J., & Weikum, G. (2019) · MPG.PuRe (Max Planck Society) · Cited by 66 · see citing works
A Benchmark for Systematic Generalization in Grounded Language UnderstandingPreprint
Ruis, L., Andreas, J., Baroni, M., Bouchacourt, D., & Lake, B. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05161 · Cited by 66 · see citing works
Differentiable Reasoning over a Virtual Knowledge BasePreprint
Dhingra, B., Zaheer, M., Balachandran, V., Neubig, G., Salakhutdinov, R., & Cohen, W. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10640 · Cited by 70 · see citing works
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
Tanaka, R., Nishida, K., Nishida, K., Hasegawa, T., Saito, I., & Saito, K. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26598 · Cited by 63 · see citing works
Recent progress in leveraging deep learning methods for question answering
Hao, T., Li, X., He, Y., Wang, F. L., & Qu, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06748-3 · Cited by 62 · see citing works
Difficulty-Controllable Multi-hop Question Generation from Knowledge Graphs
Kumar, V., Hua, Y., Ramakrishnan, G., Qi, G., Gao, L., & Li, Y.-F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30793-6_22 · Cited by 62 · see citing works
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Zhao, Y., Li, Y., Li, C., & Zhang, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.454 · Cited by 71 · see citing works
Natural Language Reasoning, A Survey
Yu, F., Zhang, H., Tiwari, P., & Wang, B. (2024) · ACM Computing Surveys · doi:10.1145/3664194 · Cited by 78 · see citing works
To Test Machine Comprehension, Start by Defining Comprehension
Dunietz, J., Burnham, G., Bharadwaj, A., Rambow, O., Chu‐Carroll, J., & Ferrucci, D. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.701 · Cited by 62 · see citing works
Christmann, P., Roy, R. S., Abujabal, A., Singh, J., & Weikum, G. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358016 · Cited by 55 · see citing works
ARL: An adaptive reinforcement learning framework for complex question answering over knowledge base
Zhang, Q., Weng, X., Zhou, G., Zhang, Y., & Huang, J. X. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102933 · Cited by 54 · see citing works
Costa, T. S., Gottschalk, S., & Demidova, E. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412760 · Cited by 53 · see citing works
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
(376528), X. L., (20096703), S. P., (20096706), R. R., (20096709), A. A., (3824542), Y. W., & Weikum, G. (2019) · Figshare · Cited by 53 · see citing works
Knowledge Base Question Answering with Topic Units
Lan, Y., Wang, S., & Jiang, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/701 · Cited by 51 · see citing works
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
Ren, H., Dai, H., Dai, B., Chen, X., Yasunaga, M., Sun, H., Schuurmans, D., Leskovec, J., & Zhou, D. (2021) · International Conference on Machine Learning · Cited by 50 · see citing works
QuALITY: Question Answering with Long Input Texts, Yes!
Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.391 · Cited by 51 · see citing works
Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey
Yani, M., & Krisnadhi, A. A. (2021) · Information · doi:10.3390/info12070271 · Cited by 50 · see citing works
DoQA - Accessing Domain-Specific FAQs via Conversational QA
Campos, J. A., Otegi, A., Soroa, A., Deriu, J., Cieliebak, M., & Agirre, E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.652 · Cited by 49 · see citing works
A Simple Yet Strong Pipeline for HotpotQA
Groeneveld, D., Khot, T., Mausam, & Sabharwal, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.711 · Cited by 46 · see citing works
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering
Ding, J., Hu, W. P., Xu, Q., & Qu, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1263 · Cited by 48 · see citing works
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models
Khot, T., Khashabi, D., Richardson, K., Clark, P. E., & Sabharwal, A. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.99 · Cited by 48 · see citing works
Kaiser, M., Roy, R. S., & Weikum, G. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462859 · Cited by 48 · see citing works
Temporal Reasoning on Implicit Events from Distant Supervision
Zhou, B., Richardson, K., Ning, Q., Khot, T., Sabharwal, A., & Roth, D. (2020) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.naacl-main.107 · Cited by 96 · see citing works
Complex Question Decomposition for Semantic Parsing
Zhang, H., Cai, J., Xu, J., & Wang, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1440 · Cited by 46 · see citing works
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents
Lin, B. Y., Sheng, Y., Vo, N., & Tata, S. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403153 · Cited by 47 · see citing works
Is Graph Structure Necessary for Multi-hop Question Answering?
Shao, N., Cui, Y., Liu, T., Wang, S., & Hu, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.583 · Cited by 46 · see citing works
Chen, Y., Li, H., Qi, G., Wu, T., & Wang, T. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3207477 · Cited by 41 · see citing works
The state of the art in open domain complex question answering: a survey
Etezadi, R., & Shamsfard, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03732-9 · Cited by 42 · see citing works
Dynamically Fused Graph Network for Multi-hop ReasoningPreprint
Xiao, Y., Qu, Y., Qiu, L., Zhou, H., Li, L., Zhang, W., & Yu, Y. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06933 · Cited by 42 · see citing works
Knowledge Graph-augmented Language Models for Complex Question Answering
Sen, P., Mavadia, S., & Saffari, A. (2023) · doi:10.18653/v1/2023.nlrse-1.1 · Cited by 41 · see citing works
Gao, J., Galley, M., & Li, L. C. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 44 · see citing works
The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge
Auer, S., Barone, D. A. C., Bartz, C., Cortes, E. G., Jaradeh, M. Y., Karras, O., Koubarakis, M., Mouromtsev, D. I., Pliukhin, D., Radyush, D., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33607-z · Cited by 40 · see citing works
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
Cheng, Y. D., Li, S., Liu, B., Zhao, R., Li, S., Lin, C., & Zheng, Y. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.465 · Cited by 43 · see citing works
Answering Open-Domain Questions of Varying Reasoning Steps from Text
Qi, P., Lee, H., Sido, O. T., & Manning, C. D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.292 · Cited by 52 · see citing works
- 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
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