Built on

Pasupat, P., Liang, P.: Compositional semantic parsing on semi-structured tables. In: ACL (2015)
2015
Earlier work this paper cites.
Dong, L., Lapata, M.: Language to logical form with neural attention. ArXiv abs/1601.01280
Original
2016
Earlier work this paper cites.
Jauhar, S.K., Turney, P.D., Hovy, E.H.: Tabmcq: A dataset of general knowledge tables and multiple-choice questions. ArXiv abs/1602.03960
Original
2016
Earlier work this paper cites.
Neelakantan, A., Le, Q.V., Sutskever, I.: Neural programmer: Inducing latent programs with gradient descent. CoRR abs/1511.04834
Original
2016
Earlier work this paper cites.
Sun, H., Ma, H., He, X., tau Yih, W., Su, Y., Yan, X.: Table cell search for question answering. In: WWW (2016)
2016
Earlier work this paper cites.
Yin, P., Lu, Z., Li, H., Kao, B.: Neural Enquirer: Learning to Query Tables with Natural Language. In: IJCAI (2016)
2016
Earlier work this paper cites.
Iyyer, M., tau Yih, W., Chang, M.W.: Search-based neural structured learning for sequential question answering. In: ACL (2017)
2017
Earlier work this paper cites.
Krishnamurthy, J., Dasigi, P., Gardner, M.: Neural semantic parsing with type constraints for semi-structured tables. In: EMNLP (2017)
2017
Earlier work this paper cites.
Neelakantan, A., Le, Q.V., Abadi, M., McCallum, A., Amodei, D.: Learning a natural language interface with neural programmer. ArXiv abs/1611.08945
Original
2017
Earlier work this paper cites.
Xu, X., Liu, C., Song, D.X.: Sqlnet: Generating structured queries from natural language without reinforcement learning. ArXiv abs/1711.04436
Original
2017
Earlier work this paper cites.
Yin, P., Neubig, G.: A syntactic neural model for general-purpose code generation. arXiv preprint arXiv:1704.01696 (2017)
Original
2017
Earlier work this paper cites.
Zhong, V., Xiong, C., Socher, R.: Seq2sql: Generating structured queries from natural language using reinforcement learning. ArXiv abs/1709.00103
Original
2017
Earlier work this paper cites.
Cho, M., Amplayo, R.K., Hwang, S.w., Park, J.: Adversarial TableQA: Attention Supervision for Question Answering on Tables. In: PMLR (2018)
2018
Earlier work this paper cites.
Dong, L., Lapata, M.: Coarse-to-fine decoding for neural semantic parsing. In: ACL (2018)
2018
Earlier work this paper cites.
Liang, C., Norouzi, M., Berant, J., Le, Q.V., Lao, N.: Memory augmented policy optimization for program synthesis and semantic parsing. In: NeurIPS (2018)
2018
Earlier work this paper cites.
Misra, D.K., Chang, M.W., He, X., Yih, W.T.: Policy shaping and generalized update equations for semantic parsing from denotations. In: EMNLP (2018)
2018
Earlier work this paper cites.
Sun, Y., Tang, D., Duan, N., Ji, J., Cao, G., Feng, X., Qin, B., Liu, T., Zhou, M.: Semantic parsing with syntax- and table-aware sql generation. ArXiv abs/1804.08338
Original
2018
Earlier work this paper cites.
Wang, C., Tatwawadi, K., Brockschmidt, M., Huang, P.S., Mao, Y., Polozov, O., Singh, R.: Robust text-to-sql generation with execution-guided decoding. arXiv abs/1807.03100
Original
2018
Earlier work this paper cites.

Similar

Yu, T., Li, Z., Zhang, Z., Zhang, R., Radev, D.R.: Typesql: Knowledge-based type-aware neural text-to-sql generation. In: NAACL (2018)
2018
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Yu, T., Zhang, R., Yang, K.C., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I.Z., Yao, Q., Roman, S., Zhang, Z., Radev, D.R.: Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In: EMNLP (2018)
2018
Cited alongside, same era.
Dasigi, P., Gardner, M., Murty, S., Zettlemoyer, L., Hovy, E.H.: Iterative search for weakly supervised semantic parsing. In: NAACL (2019)
2019
Cited alongside, same era.
He, P., Mao, Y., Chakrabarti, K., Chen, W.: X-sql: reinforce schema representation with context. ArXiv abs/1908.08113
Original
2019
Cited alongside, same era.
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Then

Abbasiantaeb, Z., Momtazi, S.: Text-based question answering from information retrieval and deep neural network perspectives: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11
2021
Later among the works it cites.
Cao, R., Chen, L., Chen, Z., Zhao, Y., Zhu, S., Yu, K.: Lgesql: Line graph enhanced text-to-sql model with mixed local and non-local relations. In: ACL (2021)
2021
Later among the works it cites.
Chen, W., Chang, M.W., Schlinger, E., Wang, W.Y., Cohen, W.W.: Open question answering over tables and text. ArXiv abs/2010.10439
Original
2021
Later among the works it cites.
Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M.I., Huang, T.H.K., Routledge, B.R., Wang, W.Y.: Finqa: A dataset of numerical reasoning over financial data. ArXiv abs/2109.00122
Original
2021
Later among the works it cites.

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2022

A Survey on Table Question Answering: Recent Advances

Jin, Nengzheng, Siebert, Joanna, Li, Dongfang et al.

Understand

Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question.

In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic.
Hence, we aim to provide an overview of available datasets and representative methods in table QA.
We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods.

Built on

Pasupat, P., Liang, P.: Compositional semantic parsing on semi-structured tables. In: ACL (2015)
2015
Earlier work this paper cites.
Dong, L., Lapata, M.: Language to logical form with neural attention. ArXiv abs/1601.01280
Original
2016
Earlier work this paper cites.
Jauhar, S.K., Turney, P.D., Hovy, E.H.: Tabmcq: A dataset of general knowledge tables and multiple-choice questions. ArXiv abs/1602.03960
Original
2016
Earlier work this paper cites.
Neelakantan, A., Le, Q.V., Sutskever, I.: Neural programmer: Inducing latent programs with gradient descent. CoRR abs/1511.04834
Original
2016
Earlier work this paper cites.
Sun, H., Ma, H., He, X., tau Yih, W., Su, Y., Yan, X.: Table cell search for question answering. In: WWW (2016)
2016
Earlier work this paper cites.
Yin, P., Lu, Z., Li, H., Kao, B.: Neural Enquirer: Learning to Query Tables with Natural Language. In: IJCAI (2016)
2016
Earlier work this paper cites.
Iyyer, M., tau Yih, W., Chang, M.W.: Search-based neural structured learning for sequential question answering. In: ACL (2017)
2017
Earlier work this paper cites.
Krishnamurthy, J., Dasigi, P., Gardner, M.: Neural semantic parsing with type constraints for semi-structured tables. In: EMNLP (2017)
2017
Earlier work this paper cites.
Neelakantan, A., Le, Q.V., Abadi, M., McCallum, A., Amodei, D.: Learning a natural language interface with neural programmer. ArXiv abs/1611.08945
Original
2017
Earlier work this paper cites.
Xu, X., Liu, C., Song, D.X.: Sqlnet: Generating structured queries from natural language without reinforcement learning. ArXiv abs/1711.04436
Original
2017
Earlier work this paper cites.
Yin, P., Neubig, G.: A syntactic neural model for general-purpose code generation. arXiv preprint arXiv:1704.01696 (2017)
Original
2017
Earlier work this paper cites.
Zhong, V., Xiong, C., Socher, R.: Seq2sql: Generating structured queries from natural language using reinforcement learning. ArXiv abs/1709.00103
Original
2017
Earlier work this paper cites.
Cho, M., Amplayo, R.K., Hwang, S.w., Park, J.: Adversarial TableQA: Attention Supervision for Question Answering on Tables. In: PMLR (2018)
2018
Earlier work this paper cites.
Dong, L., Lapata, M.: Coarse-to-fine decoding for neural semantic parsing. In: ACL (2018)
2018
Earlier work this paper cites.
Liang, C., Norouzi, M., Berant, J., Le, Q.V., Lao, N.: Memory augmented policy optimization for program synthesis and semantic parsing. In: NeurIPS (2018)
2018
Earlier work this paper cites.
Misra, D.K., Chang, M.W., He, X., Yih, W.T.: Policy shaping and generalized update equations for semantic parsing from denotations. In: EMNLP (2018)
2018
Earlier work this paper cites.
Sun, Y., Tang, D., Duan, N., Ji, J., Cao, G., Feng, X., Qin, B., Liu, T., Zhou, M.: Semantic parsing with syntax- and table-aware sql generation. ArXiv abs/1804.08338
Original
2018
Earlier work this paper cites.
Wang, C., Tatwawadi, K., Brockschmidt, M., Huang, P.S., Mao, Y., Polozov, O., Singh, R.: Robust text-to-sql generation with execution-guided decoding. arXiv abs/1807.03100
Original
2018
Earlier work this paper cites.

Similar

Yu, T., Li, Z., Zhang, Z., Zhang, R., Radev, D.R.: Typesql: Knowledge-based type-aware neural text-to-sql generation. In: NAACL (2018)
2018
Cited alongside, same era.
Yu, T., Zhang, R., Yang, K.C., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I.Z., Yao, Q., Roman, S., Zhang, Z., Radev, D.R.: Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In: EMNLP (2018)
2018
Cited alongside, same era.
Dasigi, P., Gardner, M., Murty, S., Zettlemoyer, L., Hovy, E.H.: Iterative search for weakly supervised semantic parsing. In: NAACL (2019)
2019
Cited alongside, same era.
He, P., Mao, Y., Chakrabarti, K., Chen, W.: X-sql: reinforce schema representation with context. ArXiv abs/1908.08113
Original
2019
Cited alongside, same era.
Hwang, W., Yim, J.Y., Park, S., Seo, M.: A comprehensive exploration on wikisql with table-aware word contextualization. ArXiv abs/1902.01069

Then

Abbasiantaeb, Z., Momtazi, S.: Text-based question answering from information retrieval and deep neural network perspectives: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11
2021
Later among the works it cites.
Cao, R., Chen, L., Chen, Z., Zhao, Y., Zhu, S., Yu, K.: Lgesql: Line graph enhanced text-to-sql model with mixed local and non-local relations. In: ACL (2021)
2021
Later among the works it cites.
Chen, W., Chang, M.W., Schlinger, E., Wang, W.Y., Cohen, W.W.: Open question answering over tables and text. ArXiv abs/2010.10439
Original
2021
Later among the works it cites.
Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M.I., Huang, T.H.K., Routledge, B.R., Wang, W.Y.: Finqa: A dataset of numerical reasoning over financial data. ArXiv abs/2109.00122
Original
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2019

Cited alongside, same era.

Li, D., Yu, Y., Chen, Q., Li, X.: Bertsel: Answer selection with pre-trained models. arXiv preprint arXiv:1905.07588 (2019)

Original

2019

Cited alongside, same era.

Min, S., Chen, D., Hajishirzi, H., Zettlemoyer, L.: A discrete hard em approach for weakly supervised question answering. In: EMNLP (2019)

2019

Cited alongside, same era.

Müller, T., Piccinno, F., Nicosia, M., Shaw, P., Altun, Y.: Answering conversational questions on structured data without logical forms. ArXiv abs/1908.11787

Original

2019

Cited alongside, same era.

Wang, B., Titov, I., Lapata, M.: Learning semantic parsers from denotations with latent structured alignments and abstract programs. In: EMNLP (2019)

2019

Cited alongside, same era.

Ainslie, J., Ontañón, S., Alberti, C., Cvicek, V., Fisher, Z.K., Pham, P., Ravula, A., Sanghai, S.K., Wang, Q., Yang, L.: Etc: Encoding long and structured inputs in transformers. In: EMNLP (2020)

2020

Cited alongside, same era.

Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer. ArXiv abs/2004.05150

Original

2020

Cited alongside, same era.

Chen, W., Zha, H., Chen, Z., Xiong, W., Wang, H., Wang, W.Y.: Hybridqa: A dataset of multi-hop question answering over tabular and textual data. In: FINDINGS (2020)

2020

Cited alongside, same era.

Cheng, Z., Dong, H., Wang, Z., Jia, R., Guo, J., Gao, Y., Han, S., Lou, J.G., Zhang, D.: Hitab: A hierarchical table dataset for question answering and natural language generation. ArXiv abs/2108.06712

Original

2021