The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Semantic Parsing on Freebase from Question-Answer Pairs
Open accessAbstract
In this paper, we train a semantic parser that scales up to Freebase.Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs.The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question.We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus.Second, we use a bridging operation to generate additional predicates based on neighboring predicates.On the dataset of Cai and Yates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser.Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline.
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
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000
SUTime: A library for recognizing and normalizing time expressions
Chang, A. X., & Manning, C. (2012) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/5pi53hxdrsdt · Cited by 383
Cited by
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
“Next we study the variance of few-shot learning performance on the StoryCloze, Natural Questions, and Web Questions benchmarks presented in Section 6.1.”
BackgroundInfluentialTriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D. S., & Zettlemoyer, L. (2017) · arXiv · doi:10.48550/arxiv.1705.03551 · Cited by 1,650
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546
A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., & Lillicrap, T. (2017) · arXiv · doi:10.48550/arxiv.1706.01427 · Cited by 1,281
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Fedus, W., Zoph, B., & Shazeer, N. (2021) · arXiv · doi:10.48550/arxiv.2101.03961 · Cited by 1,005
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
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1041 · Cited by 623
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · arXiv · doi:10.48550/arxiv.1704.01696 · Cited by 585
Compositional Semantic Parsing on Semi-Structured Tables
Pasupat, P., & Liang, P. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1142 · Cited by 514
Self-citationGrattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Yin, P., Neubig, G., Yih, W.-T., & Riedel, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.745 · Cited by 487
Data Recombination for Neural Semantic Parsing
Jia, R., & Liang, P. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1002 · Cited by 441
Self-citationDROP: 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
“To train our model, we adopt the weakly supervised training method widely used in the semantic parsing literature (Berant et al., 2013a).”
BackgroundMethodR3: 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
Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R., & Nanayakkara, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00530 · Cited by 337
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1015 · Cited by 325
Latent Predictor Networks for Code Generation
Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1057 · Cited by 307
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Keysers, D., Schärli, N., Scales, N., Buisman, H., Furrer, D., Kashubin, S., Momchev, N., Sinopalnikov, D., Stafiniak, L., Tihon, T., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.09713 · Cited by 264
Kembhavi, A., Seo, M., Schwenk, D., Choi, J., Farhadi, A., & Hajishirzi, H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.571 · Cited by 244
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
“However, most current datasets (Berant et al., 2013; Bordes et al., 2015; Yih et al., 2015; Diefenbach et al., 2017) consist of simple questions (single-hop).”
BackgroundTabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W., Wang, H., Chen, J., Zhang, Y., Wang, H., Li, S., Zhou, X., & Wang, W. Y. (2019) · arXiv · doi:10.48550/arxiv.1909.02164 · Cited by 239
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (2019) · arXiv · doi:10.48550/arxiv.1903.00161 · Cited by 238
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K.-F., Liu, J., Bennett, P., Ahmed, J., & Overwijk, A. (2020) · arXiv · doi:10.48550/arxiv.2007.00808 · Cited by 204
Graph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
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
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
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Du, X., & Cardie, C. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1177 · Cited by 168
MetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (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.201 · Cited by 169
Symbolic Discovery of Optimization Algorithms
Chen, X., Liang, C., Huang, D., Real, E., Wang, K., Liu, Y., Pham, H., Dong, X., Luong, T., Hsieh, C.-J., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.06675 · Cited by 303
In-IDE Code Generation from Natural Language: Promise and Challenges
Xu, F. F., Vasilescu, B., & Neubig, G. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3487569 · Cited by 154
A Practical Survey on Faster and Lighter Transformers
Fournier, Q., Caron, G. M., & Aloise, D. (2023) · ACM Computing Surveys · doi:10.1145/3586074 · Cited by 145
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets
Lewis, P., Stenetorp, P., & Riedel, S. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.86 · Cited by 145
Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review
Zhang, W., & Zhang, J. (2025) · Mathematics · doi:10.3390/math13050856 · Cited by 145
A survey on knowledge-oriented retrieval-augmented generation
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., Yu, S., Zhang, B., Cao, J., Ma, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.10677 · Cited by 140
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
Episodic Transformer for Vision-and-Language Navigation
Pashevich, A., Schmid, C., & Sun, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01564 · Cited by 135
Miao, X., Oliaro, G., Zhang, Z., Cheng, X., Wang, Z., Zhang, Z., Wong, R. Y. Y., Zhu, A., Yang, L., Shi, X., et al. (2024) · Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 · doi:10.1145/3620666.3651335 · Cited by 114
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.12756 · Cited by 112
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Dubey, M., Banerjee, D., Chaudhuri, D., & Lehmann, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00671-6_7 · Cited by 111
SPoC: Search-based Pseudocode to Code
Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., & Liang, P. (2019) · arXiv · doi:10.48550/arxiv.1906.04908 · Cited by 103
Self-citationSimple Entity-Centric Questions Challenge Dense Retrievers
Sciavolino, C., Zhong, Z., Lee, J., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.496 · Cited by 98
Improving Passage Retrieval with Zero-Shot Question Generation
Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.249 · Cited by 90
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.168 · Cited by 81
Heinzerling, B., & Inui, K. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.153 · Cited by 78
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation
Elangovan, A., He, J., & Verspoor, K. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.113 · Cited by 75
Dense X Retrieval: What Retrieval Granularity Should We Use?
Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.845 · Cited by 74
Reducing conversational agents' overconfidence through linguistic calibration
Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2020) · arXiv · doi:10.48550/arxiv.2012.14983 · Cited by 72
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration
Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00494 · Cited by 72
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
Chen, B., Sun, L., & Han, X. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1071 · Cited by 69
Naturalizing a Programming Language via Interactive Learning
Wang, S. I., Ginn, S., Liang, P., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1086 · Cited by 59
Self-citationTell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions
Kumar Misra, D., Sung, J., Lee, K., & Saxena, A. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.005 · Cited by 59
Olausson, T., Gu, A., Lipkin, B., Zhang, C., Solar-Lezama, A., Tenenbaum, J., & Levy, R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.313 · Cited by 57
“…of a semantic parser rests on a long tradition of research (Kamath and Das, 2019) whose aim is to map fragments of natural language into useful, symbolic meaning representations (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Berant et al., 2013; Liang et al., 2013; Wong et al., 2023).”
BackgroundMoving beyond the Turing Test with the Allen AI Science Challenge
Schoenick, C., Clark, P., Tafjord, O., Turney, P., & Etzioni, O. (2017) · Communications of the ACM · doi:10.1145/3122814 · Cited by 54
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?
Wang, C., Liu, P., & Zhang, Y. (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.251 · Cited by 55
Hierarchical Query Graph Generation for Complex Question Answering over Knowledge Graph
Qiu, Y., Zhang, K., Wang, Y., Jin, X., Bai, L., Guan, S., & Cheng, X. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411888 · Cited by 52
A Dataset for Answering Time-Sensitive Questions
Chen, W., Wang, X., & Wang, W. Y. (2021) · arXiv · doi:10.48550/arxiv.2108.06314 · Cited by 40
Calibrating Factual Knowledge in Pretrained Language Models
Dong, Q., Dai, D., Song, Y., Xu, J., Sui, Z., & Li, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.438 · Cited by 39
ExplainaBoard: An Explainable Leaderboard for NLP
Liu, P., Fu, J., Xiao, Y., Yuan, W., Chang, S., Dai, J., Liu, Y., Ye, Z., & Neubig, G. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations · doi:10.18653/v1/2021.acl-demo.34 · Cited by 35
Context-aware Path Ranking for Knowledge Base Completion
Mazumder, S., & Liu, B. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/166 · Cited by 35
What Language Model to Train if You Have One Million GPU Hours?
Le Scao, T., Wang, T., Hesslow, D., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., Press, O., et al. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.54 · Cited by 33
Xu, K., Zhang, K., Li, J., Huang, W., & Wang, Y. (2024) · doi:10.20944/preprints202411.1648.v1 · Cited by 28
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Lin, X. V., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.01352 · Cited by 26
(2018) · International Conference on Computational Linguistics · Cited by 25
Kedia, A., Zaidi, M. A., & Lee, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.285 · Cited by 14
Towards End-to-End Optimization of LLM-based Applications with Ayo
Tan, X., Jiang, Y., Yang, Y., & Xu, H. (2025) · Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 · doi:10.1145/3676641.3716278 · Cited by 13
Modeling Paths for Explainable Knowledge Base Completion
Stadelmaier, J., & Padó, S. (2019) · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w19-4816 · Cited by 12
LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration
Cao, Y., Gao, Z., Li, Z., Xie, X., Zhou, S. K., & Xu, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3748191.3748194 · Cited by 11
Large Language Models Can Self-Correct with Key Condition Verification
Wu, Z., Zeng, Q., Zhang, Z., Tan, Z., Shen, C., & Jiang, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.714 · Cited by 36
“We select WebQ for open-domain question answering and use exact match (EM) score and F1 score to evaluate model performance.”
BackgroundMethodInfluentialLeveraging Abstract Meaning Representation for Knowledge Base Question Answering
Kapanipathi, P., Abdelaziz, I., Ravishankar, S., Roukos, S., Gray, A., Astudillo, R., Chang, M., Cornelio, C., Dana, S., Fokoue, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.01707 · Cited by 9
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
Shao, Z., & Huang, M. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.128 · Cited by 6
Think before you speak: Training Language Models With Pause Tokens
Goyal, S., Ji, Z., Rawat, A. S., Menon, A. K., Kumar, S., & Nagarajan, V. (2023) · arXiv · doi:10.48550/arxiv.2310.02226 · Cited by 2
“Web Questions: A fact-recall dataset of commonly-asked questions on the web (Berant et al., 2013).”
BackgroundRelation-Aware Question Answering for Heterogeneous Knowledge Graphs
Du, H., Huang, Q., Li, C., Zhang, C., Li, Y., & Zhao, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.906 · Cited by 2
Metadata
- Authors
- Berant, J., Chou, A., Frostig, R., & Liang, P.
- Venue
- Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
- Published
- 2013
- Pages
- 1533-1544
- OpenAlex
- W2252136820
- Cited by
- 1,517
- References
- 2
- FWCI
- 46.83
- 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
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.
