Semantic parsing

Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning.[1] Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation,[2] question answering,[1][3] ontology induction,[4] automated reasoning,[5] and code generation.[6][7] The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations.[8]

Types

Shallow
Main article: Semantic role labeling

Shallow semantic parsing is concerned with identifying entities in an utterance and labelling them with the roles they play. Shallow semantic parsing is sometimes known as slot-filling or frame semantic parsing, since its theoretical basis comes from frame semantics, wherein a word evokes a frame of related concepts and roles. Slot-filling systems are widely used in virtual assistants in conjunction with intent classifiers, which can be seen as mechanisms for identifying the frame evoked by an utterance.[9][10] Popular architectures for slot-filling are largely variants of an encoder-decoder model, wherein two recurrent neural networks (RNNs) are trained jointly to encode an utterance into a vector and to decode that vector into a sequence of slot labels.[11] This type of model is used in the Amazon Alexa spoken language understanding system.[9]

Deep

Deep semantic parsing, also known as compositional semantic parsing, is concerned with producing precise meaning representations of utterances that can contain significant compositionality.[12] Shallow semantic parsers can parse utterances like "show me flights from Boston to Dallas" by classifying the intent as "list flights", and filling slots "source" and "destination" with "Boston" and "Dallas", respectively. However, shallow semantic parsing cannot parse arbitrary compositional utterances, like "show me flights from Boston to anywhere that has flights to Juneau". Deep semantic parsing attempts to parse such utterances, typically by converting them to a formal meaning representation language.

Representation languages

Early semantic parsers used highly domain-specific meaning representation languages,[13] with later systems using more extensible languages like Prolog,[14] lambda calculus,[15] lambda dependency-based compositional semantics (λ-DCS),[16] SQL,[17][18] Python,[19] Java,[20] the Alexa Meaning Representation Language,[9] and the Abstract Meaning Representation (AMR). Some work has used more exotic meaning representations, like query graphs,[21] semantic graphs,[22] or vector representations.[23]

Models

Most modern deep semantic parsing models are either based on defining a formal grammar for a chart parser or utilizing RNNs to directly translate from a natural language to a meaning representation language. Examples of systems built on formal grammars are the Cornell Semantic Parsing Framework,[24] Stanford University's Semantic Parsing with Execution (SEMPRE),[3] and the Word Alignment-based Semantic Parser (WASP).[25]

Datasets

Datasets used for training statistical semantic parsing models are divided into two main classes based on application: those used for question answering via knowledge base queries, and those used for code generation.

Question answering

A standard dataset for question answering via semantic parsing is the Air Travel Information System (ATIS) dataset, which contains questions and commands about upcoming flights as well as corresponding SQL.[17] Another benchmark dataset is the GeoQuery dataset which contains questions about the geography of the U.S. paired with corresponding Prolog.[14] The Overnight dataset is used to test how well semantic parsers adapt across multiple domains; it contains natural language queries about 8 different domains paired with corresponding λ-DCS expressions.[26]

Code generation

Popular datasets for code generation include two trading card datasets that link the text that appears on cards to code that precisely represents those cards. One was constructed linking Magic: The Gathering card texts to Java snippets; the other by linking Hearthstone card texts to Python snippets.[20] The IFTTT dataset[27] uses a specialized domain-specific language with short conditional commands. The Django dataset[28] pairs Python snippets with English and Japanese pseudocode describing them. The RoboCup dataset[29] pairs English rules with their representations in a domain-specific language that can be understood by virtual soccer-playing robots.

gollark: So presumably it's 70% or whatever blue.
gollark: I mean, it doesn't look like 30% of the *squares* are blue, but I assume you're just saying that the thing deliberately skews towards redness regardless of population.
gollark: I don't understand the question or why you think that's particularly relevant.
gollark: The split in what?
gollark: I don't think this substantively addresses what I said.

See also

References
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  2. Andreas, Jacob, Andreas Vlachos, and Stephen Clark. "Semantic parsing as machine translation." Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vol. 2. 2013.
  3. Berant, Jonathan, et al. "Semantic Parsing on Freebase from Question-Answer Pairs." EMNLP. Vol. 2. No. 5. 2013.
  4. Poon, Hoifung, and Pedro Domingos. "Unsupervised ontology induction from text." Proceedings of the 48th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010.
  5. Kaliszyk, Cezary, Josef Urban, and Jiří Vyskočil. "Automating formalization by statistical and semantic parsing of mathematics." International Conference on Interactive Theorem Proving. Springer, Cham, 2017.
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  7. Yin, Pengcheng; Neubig, Graham (2017-04-05). "A Syntactic Neural Model for General-Purpose Code Generation". arXiv:1704.01696 [cs.CL].
  8. Wilks, Y. and Fass, D. (1992) The Preference Semantics Family, In Computers and Mathematics with Applications, Volume 23, Issues 2-5, Pages 205-221.
  9. Kumar, Anjishnu, et al. "Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding." arXiv preprint arXiv:1711.00549 (2017).
  10. Bapna, Ankur, et al. "Towards zero-shot frame semantic parsing for domain scaling." arXiv preprint arXiv:1707.02363(2017).
  11. Liu, Bing, and Ian Lane. "Attention-based recurrent neural network models for joint intent detection and slot filling." arXiv preprint arXiv:1609.01454 (2016).
  12. Liang, Percy, and Christopher Potts. "Bringing machine learning and compositional semantics together." Annu. Rev. Linguist. 1.1 (2015): 355-376.
  13. Woods, William A. Semantics for a question-answering system. Vol. 27. Garland Pub., 1979.
  14. Zelle, John M., and Raymond J. Mooney. "Learning to parse database queries using inductive logic programming." Proceedings of the national conference on artificial intelligence. 1996.
  15. Wong, Yuk Wah, and Raymond Mooney. "Learning synchronous grammars for semantic parsing with lambda calculus." Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 2007.
  16. Liang, Percy. "Lambda dependency-based compositional semantics." arXiv preprint arXiv:1309.4408 (2013).
  17. Hemphill, Charles T., John J. Godfrey, and George R. Doddington. "The ATIS spoken language systems pilot corpus." Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24–27, 1990. 1990.
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  21. Yih, Scott Wen-tau, et al. "Semantic parsing via staged query graph generation: Question answering with knowledge base." (2015).
  22. Reddy, Siva, Mirella Lapata, and Mark Steedman. "Large-scale semantic parsing without question-answer pairs." Transactions of the Association of Computational Linguistics 2.1 (2014): 377-392.
  23. Guu, Kelvin, John Miller, and Percy Liang. "Traversing knowledge graphs in vector space." arXiv preprint arXiv:1506.01094 (2015).
  24. Artzi, Yoav. "Cornell SPF: Cornell semantic parsing framework." arXiv preprint arXiv:1311.3011 (2013).
  25. Wong, Yuk Wah; Mooney, Raymond J. (2006-06-04). Learning for semantic parsing with statistical machine translation. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -. Association for Computational Linguistics. pp. 439–446. CiteSeerX 10.1.1.135.7209. doi:10.3115/1220835.1220891.
  26. Wang, Yushi, Jonathan Berant, and Percy Liang. "Building a semantic parser overnight." 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). Vol. 1. 2015.
  27. Quirk, Chris, Raymond Mooney, and Michel Galley. "Language to code: Learning semantic parsers for if-this-then-that recipes." 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). Vol. 1. 2015.
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