---
title: "Learning an Executable Neural Semantic Parser — Cheng 2019"
description: "Cheng et al. propose a neural parser (TNSP) that builds tree-shaped FunQL logical forms through a… 77 references, cited by 23 papers here."
authors: ["Jianpeng Cheng", "Siva Reddy", "Vijay Saraswat", "Mirella Lapata"]
year: 2019
venue: "Computational Linguistics"
source: "https://aclanthology.org/J19-1002/"
doi: "10.1162/coli_a_00342"
licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)"
licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cheng-2019-learning-executable-neural-semantic-parser/"
---
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# Learning an Executable Neural Semantic Parser

**Authors:** [Jianpeng Cheng](https://md.research.elamur.ai/free/en/authors/jianpeng-cheng/), [Siva Reddy](https://md.research.elamur.ai/free/en/authors/siva-reddy/), Vijay Saraswat and [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2019 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2019](https://md.research.elamur.ai/free/en/venues/cl/2019/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/J19-1002/) · [PDF](https://aclanthology.org/J19-1002.pdf) · [DOI 10.1162/coli\_a\_00342](https://doi.org/10.1162/coli_a_00342) · [OpenAlex](https://openalex.org/W2963960541)

**Citations:** 40 citations (OpenAlex, 2026-10-14) · [23](https://md.research.elamur.ai/free/en/papers/cheng-2019-learning-executable-neural-semantic-parser/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Semantic parsing converts a question into a machine-readable program that can be run against a knowledge base or database to get an answer. Plain sequence-to-sequence neural parsers are flexible, but they can emit malformed outputs and do not show how meaning is composed. Cheng et al. propose a neural parser (TNSP) that builds tree-shaped FunQL logical forms through a transition system. This system pairs a generic tree-construction procedure with a small, domain-independent grammar of the query language, so every output is well-formed. Structured recurrent networks encode both the sentence and the history of generation steps. Soft, hard and structured attention link words to logical-form tokens without a hand-built lexicon. The parser is trained in three settings: with gold logical forms (GEOQUERY), with question–answer pairs (WEBQUESTIONS, GRAPHQUESTIONS), and with only unlabeled sentences plus a knowledge base (SPADES). Across these benchmarks it yields high-quality, executable parses, and its hard and structured attention make its decisions inspectable. The approach reduces reliance on costly annotation and domain-specific engineering.

## Key points
- **Generation and lineage:** Logical forms are expressed in FunQL and can be generated top-down or bottom-up. The model generalizes Cheng et al. (2017), which used only top-down generation with hard attention.
- **Learning from denotations:** A generative parser uses beam search to propose candidate logical forms. A discriminative ranker then rescores them using global features of utterance–logical form–denotation triplets. The authors contrast this with reinforcement-learning parsers such as Neelakantan et al. (2017).
- **Structured vs. soft attention:** Structured attention gives only a slight gain over soft attention, and only in the fully supervised GEOQUERY setting. It brings no benefit under weak or distant supervision.
- **Cost of structured attention:** It requires a forward-backward pass with O(2n²) complexity, compared with O(n) for soft attention.
- **Interpretability and future work:** Examples from GRAPHQUESTIONS show which words the model selects when predicting relations, via sampling for hard attention and Viterbi for structured attention. Planned work includes filtering spurious logical forms, tree-to-tree input (e.g., dependency trees) and multi-domain training with shared parameters.

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

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- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 359 works · 77,352 citations
- [IBM Research - Thomas J. Watson Research Center](https://md.research.elamur.ai/free/en/labs/ibm-research-thomas-j-watson-research-center/) — Research institute, Yorktown Heights, United States · 53 works · 49,193 citations

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## Cite

Cite as: Jianpeng Cheng, Siva Reddy, Vijay Saraswat and Mirella Lapata. 2019. Learning an Executable Neural Semantic Parser. Computational Linguistics.

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

```bibtex
@article{cheng2019learning,
  title = {{Learning an Executable Neural Semantic Parser}},
  author = {Jianpeng Cheng, Siva Reddy, Vijay Saraswat and Mirella Lapata},
  journal = {Computational Linguistics},
  year = {2019},
  doi = {10.1162/coli\_a\_00342},
  url = {https://doi.org/10.1162/coli_a_00342}
}
```

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## References

77 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Andreas, Jacob, Andreas Vlachos, and Stephen Clark. 2013. Semantic parsing as machine translation. In Proceedings of the Association for Computational Linguistics, pages 47–52, Sofia. [find](https://scholar.google.com/scholar?q=Andreas%2C+Jacob%2C+Andreas+Vlachos%2C+and+Stephen+Clark%2E+2013%2E+Semantic+parsing+as+machine+translation%2E+In+Proceedings+of+the+Association+for+Computational+Linguistics%2C+pages+47%E2%80%9352%2C+Sofia%2E)
2. Artzi, Yoav and Luke Zettlemoyer. 2011. Bootstrapping semantic parsers from conversations. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 421–432, Edinburgh. [find](https://scholar.google.com/scholar?q=Artzi%2C+Yoav+and+Luke+Zettlemoyer%2E+2011%2E+Bootstrapping+semantic+parsers+from+conversations%2E+In+Proceedings+of+the+2011+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+421%E2%80%93432%2C+Edi)
3. Artzi, Yoav and Luke Zettlemoyer. 2013. [Weakly supervised learning of semantic parsers for mapping instructions to actions](https://md.research.elamur.ai/free/en/papers/artzi-2013-weakly-supervised-learning-semantic-parsers/). Transactions of the Association for Computational Linguistics, 1(1):49–62.
4. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. [Neural machine translation by jointly learning to align and translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/). In Proceedings of the 4th International Conference on Learning Representations, San Diego.
5. Baker, Collin F., Charles J. Fillmore, and John B. Lowe. 1998. [The Berkeley FrameNet project](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/). In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1, pages 86–90, Montreal.
6. Banarescu, Laura, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. [Abstract meaning representation for sembanking](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/). In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Disco…
7. Bast, Hannah and Elmar Haussmann. 2015. More accurate question answering on Freebase. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pages 1431–1440, Melbourne. [find](https://scholar.google.com/scholar?q=Bast%2C+Hannah+and+Elmar+Haussmann%2E+2015%2E+More+accurate+question+answering+on+Freebase%2E+In+Proceedings+of+the+24th+ACM+International+Conference+on+Information+and+Knowledge+Management%2C+pages+1431%E2%80%931440%2C+)
8. Berant, Jonathan, Andrew Chou, Roy Frostig, and Percy Liang. 2013a. [Semantic parsing on Freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1533–1544, Seattle, WA.
9. Berant, Jonathan, Andrew Chou, Roy Frostig, and Percy Liang. 2013b. [Semantic parsing on Freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1533–1544, Seattle, WA.
10. Berant, Jonathan and Percy Liang. 2014. [Semantic parsing via paraphrasing](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1415–1425, Baltimore, MD.
11. Berant, Jonathan and Percy Liang. 2015. [Imitation learning of agenda-based semantic parsers](https://md.research.elamur.ai/free/en/papers/berant-2015-imitation-learning-agenda-based-semantic/). Transactions of the Association for Computational Linguistics, 3:545–558.
12. Bisk, Yonatan, Siva Reddy, John Blitzer, Julia Hockenmaier, and Mark Steedman. 2016. Evaluating induced CCG parsers on grounded semantic parsing. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2022–2027, Austin, TX. [find](https://scholar.google.com/scholar?q=Bisk%2C+Yonatan%2C+Siva+Reddy%2C+John+Blitzer%2C+Julia+Hockenmaier%2C+and+Mark+Steedman%2E+2016%2E+Evaluating+induced+CCG+parsers+on+grounded+semantic+parsing%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Met)
13. Bordes, Antoine, Sumit Chopra, and Jason Weston. 2014. Question answering with subgraph embeddings. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 615–620, Doha. [find](https://scholar.google.com/scholar?q=Bordes%2C+Antoine%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+2014%2E+Question+answering+with+subgraph+embeddings%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+p)
14. Cai, Jonathon, Richard Shin, and Dawn Song. 2017. Making neural programming architectures generalize via recursion. arXiv:1074.06611. [find](https://scholar.google.com/scholar?q=Cai%2C+Jonathon%2C+Richard+Shin%2C+and+Dawn+Song%2E+2017%2E+Making+neural+programming+architectures+generalize+via+recursion%2E+arXiv%3A1074%2E06611%2E)
15. Cai, Qingqing and Alexander Yates. 2013. Large-scale semantic parsing via schema matching and lexicon extension. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 423–433, Sofia. [find](https://scholar.google.com/scholar?q=Cai%2C+Qingqing+and+Alexander+Yates%2E+2013%2E+Large%2Dscale+semantic+parsing+via+schema+matching+and+lexicon+extension%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computational+Linguist)
16. Chen, David L. and Raymond J. Mooney. 2011. Learning to interpret natural language navigation instructions from observations. In Proceedings of the 25th Conference on Artificial Intelligence, volume 2, pages 859–865, San Francisco, CA. [find](https://scholar.google.com/scholar?q=Chen%2C+David+L%2E+and+Raymond+J%2E+Mooney%2E+2011%2E+Learning+to+interpret+natural+language+navigation+instructions+from+observations%2E+In+Proceedings+of+the+25th+Conference+on+Artificial+Intelligence%2C+volume+2)
17. Cheng, Jianpeng, Li Dong, and Mirella Lapata. 2016. [Long short-term memory-networks for machine reading](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 551–561, Austin, TX.
18. Cheng, Jianpeng, Adam Lopez, and Mirella Lapata. 2017. A generative parser with a discriminative recognition algorithm. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 118–124, Vancouver. [find](https://scholar.google.com/scholar?q=Cheng%2C+Jianpeng%2C+Adam+Lopez%2C+and+Mirella+Lapata%2E+2017%2E+A+generative+parser+with+a+discriminative+recognition+algorithm%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+L)
19. Cheng, Jianpeng, Siva Reddy, Vijay Saraswat, and Mirella Lapata. 2017. Learning structured natural language representations for semantic parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44–55, Vancouver. [find](https://scholar.google.com/scholar?q=Cheng%2C+Jianpeng%2C+Siva+Reddy%2C+Vijay+Saraswat%2C+and+Mirella+Lapata%2E+2017%2E+Learning+structured+natural+language+representations+for+semantic+parsing%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Assoc)
20. Clarke, James, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010. Driving semantic parsing from the world’s response. In Proceedings of the 14th Conference on Computational Natural Language Learning, pages 18–27, Uppsala. [find](https://scholar.google.com/scholar?q=Clarke%2C+James%2C+Dan+Goldwasser%2C+Ming%2DWei+Chang%2C+and+Dan+Roth%2E+2010%2E+Driving+semantic+parsing+from+the+world%E2%80%99s+response%2E+In+Proceedings+of+the+14th+Conference+on+Computational+Natural+Language+Learning%2C)
21. Dong, Li and Mirella Lapata. 2016. [Language to logical form with neural attention](https://md.research.elamur.ai/free/en/papers/dong-2016-language-logical-form-neural-attention/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33–43, Berlin.
22. Dong, Li, Jonathan Mallinson, Siva Reddy, and Mirella Lapata. 2017. [Learning to paraphrase for question answering](https://md.research.elamur.ai/free/en/papers/dong-2017-learning-paraphrase-question-answering/). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 886–897, Copenhagen.
23. Dong, Li, Furu Wei, Ming Zhou, and Ke Xu. 2015. Question answering over Freebase with multi-column convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volu… [find](https://scholar.google.com/scholar?q=Dong%2C+Li%2C+Furu+Wei%2C+Ming+Zhou%2C+and+Ke+Xu%2E+2015%2E+Question+answering+over+Freebase+with+multi%2Dcolumn+convolutional+neural+networks%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Compu)
24. Dyer, Chris, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015. [Transition-based dependency parsing with stack long short-term memory](https://md.research.elamur.ai/free/en/papers/dyer-2015-transition-based-dependency-parsing-stack/). In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natura…
25. Dyer, Chris, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. 2016. Recurrent neural network grammars. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 199–209, San Diego, CA. [find](https://scholar.google.com/scholar?q=Dyer%2C+Chris%2C+Adhiguna+Kuncoro%2C+Miguel+Ballesteros%2C+and+Noah+A%2E+Smith%2E+2016%2E+Recurrent+neural+network+grammars%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Association+fo)
26. Fan, Xing, Emilio Monti, Lambert Mathias, and Markus Dreyer. 2017. Transfer learning for neural semantic parsing. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 48–56, Vancouver. [find](https://scholar.google.com/scholar?q=Fan%2C+Xing%2C+Emilio+Monti%2C+Lambert+Mathias%2C+and+Markus+Dreyer%2E+2017%2E+Transfer+learning+for+neural+semantic+parsing%2E+In+Proceedings+of+the+2nd+Workshop+on+Representation+Learning+for+NLP%2C+pages+48%E2%80%9356%2C+Va)
27. Gabrilovich, Evgeniy, Michael Ringgaard, and Amarnag Subramanya. 2013. FACC1: Freebase annotation of ClueWeb corpora, version 1 (release date 2013-06-26, format version 1, correction level 0). lemurproject.org/clueweb12/FACC1. [find](https://scholar.google.com/scholar?q=Gabrilovich%2C+Evgeniy%2C+Michael+Ringgaard%2C+and+Amarnag+Subramanya%2E+2013%2E+FACC1%3A+Freebase+annotation+of+ClueWeb+corpora%2C+version+1+%28release+date+2013%2D06%2D26%2C+format+version+1%2C+correction+level+0%29%2E+lemurpr)
28. Goldman, Omer, Veronica Latcinnik, Udi Naveh, Amir Globerson, and Jonathan Berant. 2018. Weakly-supervised semantic parsing with abstract examples, pages 1809–1819, Melbourne. [find](https://scholar.google.com/scholar?q=Goldman%2C+Omer%2C+Veronica+Latcinnik%2C+Udi+Naveh%2C+Amir+Globerson%2C+and+Jonathan+Berant%2E+2018%2E+Weakly%2Dsupervised+semantic+parsing+with+abstract+examples%2C+pages+1809%E2%80%931819%2C+Melbourne%2E)
29. Herzig, Jonathan and Jonathan Berant. 2017. Neural semantic parsing over multiple knowledge-bases. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 623–628, Vancouver. Hochreiter, Sepp and Jürgen Schmidhuber. 1997.… [find](https://scholar.google.com/scholar?q=Herzig%2C+Jonathan+and+Jonathan+Berant%2E+2017%2E+Neural+semantic+parsing+over+multiple+knowledge%2Dbases%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+2%3A)
30. Iyer, Srinivasan, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer. 2017. Learning a neural semantic parser from user feedback. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 963–973, Vancouver. [find](https://scholar.google.com/scholar?q=Iyer%2C+Srinivasan%2C+Ioannis+Konstas%2C+Alvin+Cheung%2C+Jayant+Krishnamurthy%2C+and+Luke+Zettlemoyer%2E+2017%2E+Learning+a+neural+semantic+parser+from+user+feedback%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+th)
31. Jia, Robin and Percy Liang. 2016. Data recombination for neural semantic parsing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12–22, Berlin. [find](https://scholar.google.com/scholar?q=Jia%2C+Robin+and+Percy+Liang%2E+2016%2E+Data+recombination+for+neural+semantic+parsing%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+pa)
32. Kate, Rohit J. and Raymond J. Mooney. 2006. [Using string-kernels for learning semantic parsers](https://md.research.elamur.ai/free/en/papers/kate-2006-string-kernels-learning-semantic-parsers/). In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pages 913–920, Sydney.
33. Kate, Rohit J., Yuk Wah Wong, and Raymond J. Mooney. 2005. Learning to Transform Natural to Formal Languages. In Proceedings for the 20th National Conference on Artificial Intelligence, pages 1062–1068, Pittsburgh, PA. [find](https://scholar.google.com/scholar?q=Kate%2C+Rohit+J%2E%2C+Yuk+Wah+Wong%2C+and+Raymond+J%2E+Mooney%2E+2005%2E+Learning+to+Transform+Natural+to+Formal+Languages%2E+In+Proceedings+for+the+20th+National+Conference+on+Artificial+Intelligence%2C+pages+1062%E2%80%93106)
34. Kim, Yoon, Carl Denton, Luong Hoang, and Alexander M. Rush. 2017. Structured attention networks. In 5th International Conference on Learning Representations, Toulon. [find](https://scholar.google.com/scholar?q=Kim%2C+Yoon%2C+Carl+Denton%2C+Luong+Hoang%2C+and+Alexander+M%2E+Rush%2E+2017%2E+Structured+attention+networks%2E+In+5th+International+Conference+on+Learning+Representations%2C+Toulon%2E)
35. Konstas, Ioannis, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017. [Neural AMR: Sequence-to-sequence models for parsing and generation](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 146–157, Va…
36. Krishnamurthy, Jayant, Pradeep Dasigi, and Matt Gardner. 2017. Neural semantic parsing with type constraints for semi-structured tables. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1516–1526, Copenhagen. [find](https://scholar.google.com/scholar?q=Krishnamurthy%2C+Jayant%2C+Pradeep+Dasigi%2C+and+Matt+Gardner%2E+2017%2E+Neural+semantic+parsing+with+type+constraints+for+semi%2Dstructured+tables%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+N)
37. Krishnamurthy, Jayant and Tom Mitchell. 2012. [Weakly supervised training of semantic parsers](https://md.research.elamur.ai/free/en/papers/krishnamurthy-2012-weakly-supervised-training-semantic-parsers/). In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 754–765, Jeju Island.
38. Krishnamurthy, Jayant and Tom M. Mitchell. 2014. Joint syntactic and semantic parsing with combinatory categorial grammar. In Proceedings of the Association for Computational Linguistics, pages 1188–1198, Baltimore, MD. [find](https://scholar.google.com/scholar?q=Krishnamurthy%2C+Jayant+and+Tom+M%2E+Mitchell%2E+2014%2E+Joint+syntactic+and+semantic+parsing+with+combinatory+categorial+grammar%2E+In+Proceedings+of+the+Association+for+Computational+Linguistics%2C+pages+1188%E2%80%931)
39. Kuncoro, Adhiguna, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, and Noah A. Smith. 2017. What do recurrent neural network grammars learn about syntax? In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Paper… [find](https://scholar.google.com/scholar?q=Kuncoro%2C+Adhiguna%2C+Miguel+Ballesteros%2C+Lingpeng+Kong%2C+Chris+Dyer%2C+Graham+Neubig%2C+and+Noah+A%2E+Smith%2E+2017%2E+What+do+recurrent+neural+network+grammars+learn+about+syntax%3F+In+Proceedings+of+the+15th+Confe)
40. Kwiatkowksi, Tom, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman. 2010. Inducing probabilistic CCG grammars from logical form with higher-order unification. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1223–1233, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Kwiatkowksi%2C+Tom%2C+Luke+Zettlemoyer%2C+Sharon+Goldwater%2C+and+Mark+Steedman%2E+2010%2E+Inducing+probabilistic+CCG+grammars+from+logical+form+with+higher%2Dorder+unification%2E+In+Proceedings+of+the+2010+Conferenc)
41. Kwiatkowski, Tom, Eunsol Choi, Yoav Artzi, and Luke Zettlemoyer. 2013. [Scaling semantic parsers with on-the-fly ontology matching](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2013-scaling-semantic-parsers-fly-ontology/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1545–1556, Seattle, WA.
42. Kwiatkowski, Tom, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman. 2011. [Lexical generalization in CCG grammar induction for semantic parsing](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2011-lexical-generalization-ccg-grammar-induction/). In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1512–1523, Edinburgh.
43. Lafferty, John, Andrew Mccallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning, pages 282–298, San Francisco, CA. [find](https://scholar.google.com/scholar?q=Lafferty%2C+John%2C+Andrew+Mccallum%2C+and+Fernando+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+Proceedings+of+the+18th+International+Confere)
44. Liang, Chen, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. 2017. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 23–33… [find](https://scholar.google.com/scholar?q=Liang%2C+Chen%2C+Jonathan+Berant%2C+Quoc+Le%2C+Kenneth+D%2E+Forbus%2C+and+Ni+Lao%2E+2017%2E+Neural+symbolic+machines%3A+Learning+semantic+parsers+on+freebase+with+weak+supervision%2E+In+Proceedings+of+the+55th+Annual+Mee)
45. Liang, Percy. 2013. Lambda dependency-based compositional semantics. arXiv preprint arXiv:1309.4408. [find](https://scholar.google.com/scholar?q=Liang%2C+Percy%2E+2013%2E+Lambda+dependency%2Dbased+compositional+semantics%2E+arXiv+preprint+arXiv%3A1309%2E4408%2E)
46. Liang, Percy. 2016. Learning executable semantic parsers for natural language understanding. Communications of the ACM, 59(9):68–76. [find](https://scholar.google.com/scholar?q=Liang%2C+Percy%2E+2016%2E+Learning+executable+semantic+parsers+for+natural+language+understanding%2E+Communications+of+the+ACM%2C+59%289%29%3A68%E2%80%9376%2E)
47. Liang, Percy, Michael Jordan, and Dan Klein. 2011. Learning dependency-based compositional semantics. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 590–599, Portland, OR. [find](https://scholar.google.com/scholar?q=Liang%2C+Percy%2C+Michael+Jordan%2C+and+Dan+Klein%2E+2011%2E+Learning+dependency%2Dbased+compositional+semantics%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+Human+)
48. Liu, Yang and Mirella Lapata. 2018. [Learning structured text representations](https://md.research.elamur.ai/free/en/papers/liu-2018-learning-structured-text-representations/). Transactions of the Association for Computational Linguistics, 6:63–75.
49. Lu, Wei, Hwee Tou Ng, Wee Sun Lee, and Luke S. Zettlemoyer. 2008. [A generative model for parsing natural language to meaning representations](https://md.research.elamur.ai/free/en/papers/lu-2008-generative-model-parsing-natural-language/). In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 783–792, Honolulu, HI.
50. Matuszek, Cynthia, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, and Dieter Fox. 2012. A joint model of language and perception for grounded attribute learning. In Proceedings of the 29th International Conference on Machine Learning, pages 1671–1678, Edinburgh. [find](https://scholar.google.com/scholar?q=Matuszek%2C+Cynthia%2C+Nicholas+FitzGerald%2C+Luke+Zettlemoyer%2C+Liefeng+Bo%2C+and+Dieter+Fox%2E+2012%2E+A+joint+model+of+language+and+perception+for+grounded+attribute+learning%2E+In+Proceedings+of+the+29th+Interna)
51. Montague, Richard. 1973. The proper treatment of quantification in ordinary English. In K. J. J. Hintikka, J. M. E. Moravcsik, and P. Suppes, editors, Approaches to Natural Language, volume 49 of Synthese Library. Springer Netherlands, pages 221–242. [find](https://scholar.google.com/scholar?q=Montague%2C+Richard%2E+1973%2E+The+proper+treatment+of+quantification+in+ordinary+English%2E+In+K%2E+J%2E+J%2E+Hintikka%2C+J%2E+M%2E+E%2E+Moravcsik%2C+and+P%2E+Suppes%2C+editors%2C+Approaches+to+Natural+Language%2C+volume+49+of+Synt)
52. Neelakantan, Arvind, Quoc V. Le, Martin Abadi, Andrew McCallum, and Dario Amodei. 2017. Learning a natural language interface with neural programmer. 5th International Conference on Learning Representations, Toulon. [find](https://scholar.google.com/scholar?q=Neelakantan%2C+Arvind%2C+Quoc+V%2E+Le%2C+Martin+Abadi%2C+Andrew+McCallum%2C+and+Dario+Amodei%2E+2017%2E+Learning+a+natural+language+interface+with+neural+programmer%2E+5th+International+Conference+on+Learning+Represent)
53. Neelakantan, Arvind, Quoc V. Le, and Ilya Sutskever. 2016. Neural programmer: Inducing latent programs with gradient descent. 4th International Conference on Learning Representations, San Juan. [find](https://scholar.google.com/scholar?q=Neelakantan%2C+Arvind%2C+Quoc+V%2E+Le%2C+and+Ilya+Sutskever%2E+2016%2E+Neural+programmer%3A+Inducing+latent+programs+with+gradient+descent%2E+4th+International+Conference+on+Learning+Representations%2C+San+Juan%2E)
54. Pasupat, Panupong and Percy Liang. 2015. [Compositional semantic parsing on semi-structured tables](https://md.research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/). In 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), volume 1, p…
55. Pennington, Jeffrey, Richard Socher, and Christopher Manning. 2014. [GloVe: Global vectors for word representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1532–1543, Doha.
56. Popescu, Ana-Maria, Alex Armanasu, Oren Etzioni, David Ko, and Alexander Yates. 2004. Modern natural language interfaces to databases: Composing statistical parsing with semantic tractability. In Proceedings of COLING 2004, pages 141–147, Geneva. [find](https://scholar.google.com/scholar?q=Popescu%2C+Ana%2DMaria%2C+Alex+Armanasu%2C+Oren+Etzioni%2C+David+Ko%2C+and+Alexander+Yates%2E+2004%2E+Modern+natural+language+interfaces+to+databases%3A+Composing+statistical+parsing+with+semantic+tractability%2E+In+Proc)
57. Rabinovich, Maxim, Mitchell Stern, and Dan Klein. 2017. Abstract syntax networks for code generation and semantic parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1139–1149, Vancouver. [find](https://scholar.google.com/scholar?q=Rabinovich%2C+Maxim%2C+Mitchell+Stern%2C+and+Dan+Klein%2E+2017%2E+Abstract+syntax+networks+for+code+generation+and+semantic+parsing%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computationa)
58. Reddy, Siva, Mirella Lapata, and Mark Steedman. 2014. Large-scale semantic parsing without question-answer pairs. Transactions of the Association for Computational Linguistics, 2:377–392. Reddy, Siva, Oscar Täckström, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, and Mirella Lapat… [find](https://scholar.google.com/scholar?q=Reddy%2C+Siva%2C+Mirella+Lapata%2C+and+Mark+Steedman%2E+2014%2E+Large%2Dscale+semantic+parsing+without+question%2Danswer+pairs%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+2%3A377%E2%80%93392%2E+Reddy%2C+Siva%2C)
59. Reed, Scott and Nando De Freitas. 2016. Neural programmer-interpreters. 4th International Conference on Learning Representations, San Juan. [find](https://scholar.google.com/scholar?q=Reed%2C+Scott+and+Nando+De+Freitas%2E+2016%2E+Neural+programmer%2Dinterpreters%2E+4th+International+Conference+on+Learning+Representations%2C+San+Juan%2E)
60. The Syntactic Process, MIT Press. Su, Yu, Huan Sun, Brian Sadler, Mudhakar Srivatsa, Izzeddin Gur, Zenghui Yan, and Xifeng Yan. 2016. On generating characteristic-rich question sets for qa evaluation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 56… [find](https://scholar.google.com/scholar?q=The+Syntactic+Process%2C+MIT+Press%2E+Su%2C+Yu%2C+Huan+Sun%2C+Brian+Sadler%2C+Mudhakar+Srivatsa%2C+Izzeddin+Gur%2C+Zenghui+Yan%2C+and+Xifeng+Yan%2E+2016%2E+On+generating+characteristic%2Drich+question+sets+for+qa+evaluation%2E)
61. Suhr, Alane, Srinivasan Iyer, and Yoav Artzi. 2018. Learning to map context-dependent sentences to executable formal queries. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pag… [find](https://scholar.google.com/scholar?q=Suhr%2C+Alane%2C+Srinivasan+Iyer%2C+and+Yoav+Artzi%2E+2018%2E+Learning+to+map+context%2Ddependent+sentences+to+executable+formal+queries%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chapter+of+the)
62. Sutskever, Ilya, James Martens, George Dahl, and Geoffrey Hinton. 2013. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning, pages 1139–1147, Atlanta, GA. [find](https://scholar.google.com/scholar?q=Sutskever%2C+Ilya%2C+James+Martens%2C+George+Dahl%2C+and+Geoffrey+Hinton%2E+2013%2E+On+the+importance+of+initialization+and+momentum+in+deep+learning%2E+In+Proceedings+of+the+30th+International+Conference+on+Machin)
63. Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 2014. [Sequence to sequence learning with neural networks](https://md.research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/). In Advances in Neural Information Processing Systems 27, pages 3104–3112, Montreal.
64. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In Advances in Neural Information Processing Systems 30, pages 5998–6008, Montreal. Wen, Tsung Hsien, Milica Gasic, Nikola Mrkšić, Pei-Hao…
65. Williams, Ronald J. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4):229–256. [find](https://scholar.google.com/scholar?q=Williams%2C+Ronald+J%2E+1992%2E+Simple+statistical+gradient%2Dfollowing+algorithms+for+connectionist+reinforcement+learning%2E+Machine+Learning%2C+8%283%2D4%29%3A229%E2%80%93256%2E)
66. Winograd, Terry. 1972. Understanding natural language. Cognitive Psychology, 3(1):1–191. [find](https://scholar.google.com/scholar?q=Winograd%2C+Terry%2E+1972%2E+Understanding+natural+language%2E+Cognitive+Psychology%2C+3%281%29%3A1%E2%80%93191%2E)
67. Wong, Yuk Wah and Raymond Mooney. 2006. [Learning for semantic parsing with statistical machine translation](https://md.research.elamur.ai/free/en/papers/wong-2006-learning-semantic-parsing-statistical-machine/). In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 439–446, New York, NY.
68. Woods, William A., Ronald M. Kaplan, and Bonnie Nash-Webber. 1972. The Lunar sciences: Natural language information system: Final report. Technical Report BBN Report 2378, Bolt Beranek and Newman. [find](https://scholar.google.com/scholar?q=Woods%2C+William+A%2E%2C+Ronald+M%2E+Kaplan%2C+and+Bonnie+Nash%2DWebber%2E+1972%2E+The+Lunar+sciences%3A+Natural+language+information+system%3A+Final+report%2E+Technical+Report+BBN+Report+2378%2C+Bolt+Beranek+and+Newman%2E)
69. Xu, Kun, Siva Reddy, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016. [Question answering on Freebase via relation extraction and textual evidence](https://md.research.elamur.ai/free/en/papers/xu-2016-question-answering-freebase-relation-extraction/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2326–2336, Berlin.
70. Yao, Xuchen and Benjamin Van Durme. 2014. Information extraction over structured data: Question answering with Freebase. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 956–966, Baltimore, MD. Yih, Wen tau, Ming-Wei Chang, Xia… [find](https://scholar.google.com/scholar?q=Yao%2C+Xuchen+and+Benjamin+Van+Durme%2E+2014%2E+Information+extraction+over+structured+data%3A+Question+answering+with+Freebase%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational+)
71. Yin, Pengcheng and Graham Neubig. 2017. [A syntactic neural model for general-purpose code generation](https://md.research.elamur.ai/free/en/papers/yin-2017-syntactic-neural-model-general-purpose/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 440–450, Vancouver.
72. Zelle, John M. and Raymond J. Mooney. 1996. [Learning to parse database queries using inductive logic programming](https://md.research.elamur.ai/free/en/papers/zelle-1996-learning-parse-database-queries-using/). In Proceedings of the 13th National Conference on Artificial Intelligence, pages 1050–1055, Portland, OR.
73. Zelle, John Marvin. 1995. Using inductive logic programming to automate the construction of natural language parsers. Ph.D. thesis, University of Texas at Austin. [find](https://scholar.google.com/scholar?q=Zelle%2C+John+Marvin%2E+1995%2E+Using+inductive+logic+programming+to+automate+the+construction+of+natural+language+parsers%2E+Ph%2ED%2E+thesis%2C+University+of+Texas+at+Austin%2E)
74. Zettlemoyer, Luke and Michael Collins. 2007. Online learning of relaxed CCG grammars for parsing to logical form. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 678–687, Prague. [find](https://scholar.google.com/scholar?q=Zettlemoyer%2C+Luke+and+Michael+Collins%2E+2007%2E+Online+learning+of+relaxed+CCG+grammars+for+parsing+to+logical+form%2E+In+Proceedings+of+the+2007+Joint+Conference+on+Empirical+Methods+in+Natural+Language+P)
75. Zettlemoyer, Luke S. and Michael Collins. 2005. [Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars](https://md.research.elamur.ai/free/en/papers/zettlemoyer-2012-learning-map-sentences-logical-form/). In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pages 658–666, Edinburgh.
76. Zhao, Kai and Liang Huang. 2015. Type-driven incremental semantic parsing with polymorphism. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1416–1421, Denver, CO. [find](https://scholar.google.com/scholar?q=Zhao%2C+Kai+and+Liang+Huang%2E+2015%2E+Type%2Ddriven+incremental+semantic+parsing+with+polymorphism%2E+In+Proceedings+of+the+2015+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Li)
77. Zhong, Victor, Caiming Xiong, and Richard Socher. 2017. [Seq2SQL: Generating structured queries from natural language using reinforcement learning](https://md.research.elamur.ai/free/en/papers/zhong-2017-seq2sql-generating-structured-queries-natural/). CoRR, abs/1709.00103.

*Updated 30 October 2026*