---
title: "Compositional Semantic Parsing on Semi-Structured Tables — Pasupat 2015"
description: "The authors propose a new task: answering complex questions over HTML tables from Wikipedia, while training the… 33 references, cited by 404 papers here."
authors: ["Panupong Pasupat", "Percy Liang"]
year: 2015
venue: "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)"
source: "https://aclanthology.org/P15-1142/"
doi: "10.3115/v1/p15-1142"
arxiv: "1508.00305"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/"
---
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# Compositional Semantic Parsing on Semi-Structured Tables

**Authors:** [Panupong Pasupat](https://md.research.elamur.ai/free/en/authors/panupong-pasupat/) and [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/) · **Year:** 2015 · **Venue:** 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) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P15-1142/) · [PDF](https://aclanthology.org/P15-1142.pdf) · [DOI 10.3115/v1/p15-1142](https://doi.org/10.3115/v1/p15-1142) · [arXiv 1508.00305](https://arxiv.org/abs/1508.00305)

**Citations:** [404](https://md.research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary

The authors combine two goals of semantic parsing that previously had to be traded off against each other: a broad knowledge source and complex multi-step questions. Early systems understood complex questions, but only over small single-domain databases. Later systems worked with large knowledge bases such as Freebase, yet answered only simple questions. The authors propose a new task: answering complex questions over HTML tables from Wikipedia, while training the system only on question–answer pairs. The tables in the test set do not appear during training, so a relation lexicon cannot be compiled in advance. Each table is deterministically converted into a knowledge graph with nodes for normalized numbers and dates. A broad-coverage grammar builds candidate logical forms in the lambda DCS language. A log-linear model ranks the candidates, and beam search with type- and denotation-based pruning keeps the combinatorial explosion in check. The open WikiTableQuestions dataset was created for evaluation. The system clearly outperforms the baseline approaches and has become a starting point for research on question answering over tables.

## Key points

- **WikiTableQuestions**: 22,033 questions over 2,108 Wikipedia tables, each table having at least 8 rows and 5 columns. The questions were written and verified by workers on Amazon Mechanical Turk. An answer was kept only if two workers agreed on it, and about 69% of the questions remained after this filter. 20% of the tables were held out for testing.
- **Accuracy**: 37.1% on tables the system did not see during training. An information-retrieval baseline achieves 12.7%, and a simple semantic parser achieves 24.3%.
- **Method**: a knowledge graph (rows, cells, and auxiliary Next and Index edges), lambda DCS logical forms, log-linear ranking of candidates, and training with AdaGrad and L1 regularization.
- **Coverage**: 79% of the questions can be manually translated into a logical form, and the system generates the correct form among its candidates in 53.5% of cases.
- **Error analysis**: 29% are caused by normalization and types, 25% by entity linking to the text, another 25% by candidate ranking, and 21% by question types the system does not handle.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [🧾 Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/)

## Cite

Cite as: Panupong Pasupat and Percy Liang. 2015. Compositional Semantic Parsing on Semi-Structured Tables. 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).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pasupat2015compositional,
  title = {{Compositional Semantic Parsing on Semi-Structured Tables}},
  author = {Panupong Pasupat and Percy Liang},
  booktitle = {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)},
  year = {2015},
  doi = {10.3115/v1/p15-1142},
  eprint = {1508.00305},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.3115/v1/p15-1142}
}
```

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## References

33 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. I. Androutsopoulos, G. D. Ritchie, and P. Thanisch. 1995. Natural language interfaces to databases – an introduction. Journal of Natural Language Engineering, 1:29–81. [find](https://scholar.google.com/scholar?q=I%2E+Androutsopoulos%2C+G%2E+D%2E+Ritchie%2C+and+P%2E+Thanisch%2E+1995%2E+Natural+language+interfaces+to+databases+%E2%80%93+an+introduction%2E+Journal+of+Natural+Language+Engineering%2C+1%3A29%E2%80%9381%2E)
2. J. Berant and P. Liang. 2014. Semantic parsing via paraphrasing. In Association for Computational Linguistics (ACL). [our card](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/)
3. J. Berant, A. Chou, R. Frostig, and P. Liang. 2013. Semantic parsing on Freebase from question-answer pairs. In Empirical Methods in Natural Language Processing (EMNLP). [our card](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/)
4. M. J. Cafarella, A. Halevy, D. Z. Wang, E. Wu, and Y. Zhang. 2008. WebTables: exploring the power of tables on the web. In Very Large Data Bases (VLDB), pages 538–549. [find](https://scholar.google.com/scholar?q=M%2E+J%2E+Cafarella%2C+A%2E+Halevy%2C+D%2E+Z%2E+Wang%2C+E%2E+Wu%2C+and+Y%2E+Zhang%2E+2008%2E+WebTables%3A+exploring+the+power+of+tables+on+the+web%2E+In+Very+Large+Data+Bases+%28VLDB%29%2C+pages+538%E2%80%93549%2E)
5. Q. Cai and A. Yates. 2013. Large-scale semantic parsing via schema matching and lexicon extension. In Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Q%2E+Cai+and+A%2E+Yates%2E+2013%2E+Large%2Dscale+semantic+parsing+via+schema+matching+and+lexicon+extension%2E+In+Association+for+Computational+Linguistics+%28ACL%29%2E)
6. J. Duchi, E. Hazan, and Y. Singer. 2010. Adaptive subgradient methods for online learning and stochastic optimization. In Conference on Learning Theory (COLT). [find](https://scholar.google.com/scholar?q=J%2E+Duchi%2C+E%2E+Hazan%2C+and+Y%2E+Singer%2E+2010%2E+Adaptive+subgradient+methods+for+online+learning+and+stochastic+optimization%2E+In+Conference+on+Learning+Theory+%28COLT%29%2E)
7. A. Fader, L. Zettlemoyer, and O. Etzioni. 2013. Paraphrase-driven learning for open question answering. In Association for Computational Linguistics (ACL). [our card](https://md.research.elamur.ai/free/en/papers/fader-2013-paraphrase-driven-learning-open-question/)
8. A. Fader, L. Zettlemoyer, and O. Etzioni. 2014. Open question answering over curated and extracted knowledge bases. In International Conference on Knowledge Discovery and Data Mining (KDD), pages 1156–1165. [find](https://scholar.google.com/scholar?q=A%2E+Fader%2C+L%2E+Zettlemoyer%2C+and+O%2E+Etzioni%2E+2014%2E+Open+question+answering+over+curated+and+extracted+knowledge+bases%2E+In+International+Conference+on+Knowledge+Discovery+and+Data+Mining+%28KDD%29%2C+pages+1156)
9. H. Gonzalez, A. Y. Halevy, C. S. Jensen, A. Langen, J. Madhavan, R. Shapley, W. Shen, and J. Goldberg-Kidon. 2010. Google fusion tables: web-centered data management and collaboration. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 1061–1066. [find](https://scholar.google.com/scholar?q=H%2E+Gonzalez%2C+A%2E+Y%2E+Halevy%2C+C%2E+S%2E+Jensen%2C+A%2E+Langen%2C+J%2E+Madhavan%2C+R%2E+Shapley%2C+W%2E+Shen%2C+and+J%2E+Goldberg%2DKidon%2E+2010%2E+Google+fusion+tables%3A+web%2Dcentered+data+management+and+collaboration%2E+In+Proceedings+)
10. R. Gupta and S. Sarawagi. 2009. Answering table augmentation queries from unstructured lists on the web. In Very Large Data Bases (VLDB), number 1, pages 289–300. [find](https://scholar.google.com/scholar?q=R%2E+Gupta+and+S%2E+Sarawagi%2E+2009%2E+Answering+table+augmentation+queries+from+unstructured+lists+on+the+web%2E+In+Very+Large+Data+Bases+%28VLDB%29%2C+number+1%2C+pages+289%E2%80%93300%2E)
11. H. Ji and R. Grishman. 2011. Knowledge base population: Successful approaches and challenges. In Association for Computational Linguistics (ACL), pages 1148–1158. [find](https://scholar.google.com/scholar?q=H%2E+Ji+and+R%2E+Grishman%2E+2011%2E+Knowledge+base+population%3A+Successful+approaches+and+challenges%2E+In+Association+for+Computational+Linguistics+%28ACL%29%2C+pages+1148%E2%80%931158%2E)
12. J. Krishnamurthy and T. Kollar. 2013. Jointly learning to parse and perceive: Connecting natural language to the physical world. Transactions of the Association for Computational Linguistics (TACL), 1:193– 206. [find](https://scholar.google.com/scholar?q=J%2E+Krishnamurthy+and+T%2E+Kollar%2E+2013%2E+Jointly+learning+to+parse+and+perceive%3A+Connecting+natural+language+to+the+physical+world%2E+Transactions+of+the+Association+for+Computational+Linguistics+%28TACL%29%2C+1)
13. T. Kwiatkowski, L. Zettlemoyer, S. Goldwater, and M. Steedman. 2011. Lexical generalization in CCG grammar induction for semantic parsing. In Empirical Methods in Natural Language Processing (EMNLP), pages 1512–1523. [our card](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2011-lexical-generalization-ccg-grammar-induction/)
14. T. Kwiatkowski, E. Choi, Y. Artzi, and L. Zettlemoyer. 2013. Scaling semantic parsers with on-the-fly ontology matching. In Empirical Methods in Natural Language Processing (EMNLP). [our card](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2013-scaling-semantic-parsers-fly-ontology/)
15. P. Liang. 2013. Lambda dependency-based compositional semantics. arXiv. [find](https://scholar.google.com/scholar?q=P%2E+Liang%2E+2013%2E+Lambda+dependency%2Dbased+compositional+semantics%2E+arXiv%2E)
16. G. Limaye, S. Sarawagi, and S. Chakrabarti. 2010. Annotating and searching web tables using entities, types and relationships. In Very Large Data Bases (VLDB), volume 3, pages 1338–1347. [find](https://scholar.google.com/scholar?q=G%2E+Limaye%2C+S%2E+Sarawagi%2C+and+S%2E+Chakrabarti%2E+2010%2E+Annotating+and+searching+web+tables+using+entities%2C+types+and+relationships%2E+In+Very+Large+Data+Bases+%28VLDB%29%2C+volume+3%2C+pages+1338%E2%80%931347%2E)
17. Masaum, M. Schmitz, R. Bart, S. Soderland, and O. Etzioni. 2012. Open language learning for information extraction. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), pages 523– 534. [find](https://scholar.google.com/scholar?q=Masaum%2C+M%2E+Schmitz%2C+R%2E+Bart%2C+S%2E+Soderland%2C+and+O%2E+Etzioni%2E+2012%2E+Open+language+learning+for+information+extraction%2E+In+Empirical+Methods+in+Natural+Language+Processing+and+Computational+Natural+Langua)
18. P. Pasupat and P. Liang. 2014. Zero-shot entity extraction from web pages. In Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=P%2E+Pasupat+and+P%2E+Liang%2E+2014%2E+Zero%2Dshot+entity+extraction+from+web+pages%2E+In+Association+for+Computational+Linguistics+%28ACL%29%2E)
19. R. Pimplikar and S. Sarawagi. 2012. Answering table queries on the web using column keywords. In Very Large Data Bases (VLDB), volume 5, pages 908– 919. [find](https://scholar.google.com/scholar?q=R%2E+Pimplikar+and+S%2E+Sarawagi%2E+2012%2E+Answering+table+queries+on+the+web+using+column+keywords%2E+In+Very+Large+Data+Bases+%28VLDB%29%2C+volume+5%2C+pages+908%E2%80%93+919%2E)
20. A. Popescu, O. Etzioni, and H. Kautz. 2003. Towards a theory of natural language interfaces to databases. In International Conference on Intelligent User Interfaces (IUI), pages 149–157. [find](https://scholar.google.com/scholar?q=A%2E+Popescu%2C+O%2E+Etzioni%2C+and+H%2E+Kautz%2E+2003%2E+Towards+a+theory+of+natural+language+interfaces+to+databases%2E+In+International+Conference+on+Intelligent+User+Interfaces+%28IUI%29%2C+pages+149%E2%80%93157%2E)
21. P. Price. 1990. Evaluation of spoken language systems: The ATIS domain. In Proceedings of the Third DARPA Speech and Natural Language Workshop, pages 91–95. [find](https://scholar.google.com/scholar?q=P%2E+Price%2E+1990%2E+Evaluation+of+spoken+language+systems%3A+The+ATIS+domain%2E+In+Proceedings+of+the+Third+DARPA+Speech+and+Natural+Language+Workshop%2C+pages+91%E2%80%9395%2E)
22. S. Reddy, M. Lapata, and M. Steedman. 2014. Large-scale semantic parsing without question-answer pairs. Transactions of the Association for Computational Linguistics (TACL), 2(10):377–392. [find](https://scholar.google.com/scholar?q=S%2E+Reddy%2C+M%2E+Lapata%2C+and+M%2E+Steedman%2E+2014%2E+Large%2Dscale+semantic+parsing+without+question%2Danswer+pairs%2E+Transactions+of+the+Association+for+Computational+Linguistics+%28TACL%29%2C+2%2810%29%3A377%E2%80%93392%2E)
23. Z. Syed, T. Finin, V. Mulwad, and A. Joshi. 2010. Exploiting a web of semantic data for interpreting tables. In Proceedings of the Second Web Science Conference. [find](https://scholar.google.com/scholar?q=Z%2E+Syed%2C+T%2E+Finin%2C+V%2E+Mulwad%2C+and+A%2E+Joshi%2E+2010%2E+Exploiting+a+web+of+semantic+data+for+interpreting+tables%2E+In+Proceedings+of+the+Second+Web+Science+Conference%2E)
24. C. Unger and P. Cimiano. 2011. Pythia: compositional meaning construction for ontology-based question answering on the semantic web. In Proceedings of the 16th international conference on Natural language processing and information systems, pages 153–160. [find](https://scholar.google.com/scholar?q=C%2E+Unger+and+P%2E+Cimiano%2E+2011%2E+Pythia%3A+compositional+meaning+construction+for+ontology%2Dbased+question+answering+on+the+semantic+web%2E+In+Proceedings+of+the+16th+international+conference+on+Natural+lang)
25. C. Unger, L. Bühmann, J. Lehmann, A. Ngonga, D. Gerber, and P. Cimiano. 2012. Template-based question answering over RDF data. In World Wide Web (WWW), pages 639–648. [find](https://scholar.google.com/scholar?q=C%2E+Unger%2C+L%2E+Bu%CC%88hmann%2C+J%2E+Lehmann%2C+A%2E+Ngonga%2C+D%2E+Gerber%2C+and+P%2E+Cimiano%2E+2012%2E+Template%2Dbased+question+answering+over+RDF+data%2E+In+World+Wide+Web+%28WWW%29%2C+pages+639%E2%80%93648%2E)
26. P. Venetis, A. Halevy, J. Madhavan, M. Paşca, W. Shen, F. Wu, G. Miao, and C. Wu. 2011. Recovering semantics of tables on the web. In Very Large Data Bases (VLDB), volume 4, pages 528–538. [find](https://scholar.google.com/scholar?q=P%2E+Venetis%2C+A%2E+Halevy%2C+J%2E+Madhavan%2C+M%2E+Pas%CC%A7ca%2C+W%2E+Shen%2C+F%2E+Wu%2C+G%2E+Miao%2C+and+C%2E+Wu%2E+2011%2E+Recovering+semantics+of+tables+on+the+web%2E+In+Very+Large+Data+Bases+%28VLDB%29%2C+volume+4%2C+pages+528%E2%80%93538%2E)
27. Y. Wang, L. Deng, and A. Acero. 2011. Semantic frame-based spoken language understanding. Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, pages 41–91. [find](https://scholar.google.com/scholar?q=Y%2E+Wang%2C+L%2E+Deng%2C+and+A%2E+Acero%2E+2011%2E+Semantic+frame%2Dbased+spoken+language+understanding%2E+Spoken+Language+Understanding%3A+Systems+for+Extracting+Semantic+Information+from+Speech%2C+pages+41%E2%80%9391%2E)
28. Y. W. Wong and R. J. Mooney. 2007. Learning synchronous grammars for semantic parsing with lambda calculus. In Association for Computational Linguistics (ACL), pages 960–967. [our card](https://md.research.elamur.ai/free/en/papers/wong-2007-learning-synchronous-grammars-semantic-parsing/)
29. Y. W. Wong, D. Widdows, T. Lokovic, and K. Nigam. 2009. Scalable attribute-value extraction from semi-structured text. In IEEE International Conference on Data Mining Workshops, pages 302–307. [find](https://scholar.google.com/scholar?q=Y%2E+W%2E+Wong%2C+D%2E+Widdows%2C+T%2E+Lokovic%2C+and+K%2E+Nigam%2E+2009%2E+Scalable+attribute%2Dvalue+extraction+from+semi%2Dstructured+text%2E+In+IEEE+International+Conference+on+Data+Mining+Workshops%2C+pages+302%E2%80%93307%2E)
30. F. Wu and D. S. Weld. 2010. Open information extraction using Wikipedia. In Association for Computational Linguistics (ACL), pages 118–127. [our card](https://md.research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/)
31. M. Zelle and R. J. Mooney. 1996. Learning to parse database queries using inductive logic programming. In Association for the Advancement of Artificial Intelligence (AAAI), pages 1050–1055. [our card](https://md.research.elamur.ai/free/en/papers/zelle-1996-learning-parse-database-queries-using/)
32. L. S. Zettlemoyer and M. Collins. 2007. Online learning of relaxed CCG grammars for parsing to logical form. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), pages 678–687. [find](https://scholar.google.com/scholar?q=L%2E+S%2E+Zettlemoyer+and+M%2E+Collins%2E+2007%2E+Online+learning+of+relaxed+CCG+grammars+for+parsing+to+logical+form%2E+In+Empirical+Methods+in+Natural+Language+Processing+and+Computational+Natural+Language+Lear)
33. Z. Zhang, K. Q. Zhu, H. Wang, and H. Li. 2013. Automatic extraction of top-k lists from the web. In International Conference on Data Engineering. [find](https://scholar.google.com/scholar?q=Z%2E+Zhang%2C+K%2E+Q%2E+Zhu%2C+H%2E+Wang%2C+and+H%2E+Li%2E+2013%2E+Automatic+extraction+of+top%2Dk+lists+from+the+web%2E+In+International+Conference+on+Data+Engineering%2E)