Semantic parsing

Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning.

Applications of semantic parsing include machine translation,[2] question answering,[1][3] ontology induction,[4] automated reasoning,[5] and code generation.

[10][11] Early research of semantic parsing included the generation of grammar manually [12] as well as utilizing applied programming logic.

[16] This improved upon manual grammars primarily because they leveraged the syntactical nature of the sentence, but they still couldn’t cover enough variation and weren’t robust enough to be used in the real world.

Not only was it providing competitive results on the existing datasets, but it was robust to noise and did not require a lot of supervision and manual intervention.

Intermediate efforts and modifications to the Seq2Seq to incorporate syntax and semantic meaning have been attempted,[18][19] with a marked improvement in results, but there remains a lot of ambiguity to be taken care of.

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.

A well-established field of study, semantic parsing finds use in voice assistants, question answering, instruction following, and code generation.

Nonetheless, more approachable formalisms, like conventional programming languages, and NMT-style models that are considerably more accessible to a wider NLP audience, are made possible by recent work with neural encoder-decoder semantic parsers.

[39] Recently, semantic parsing is gaining significant popularity as a result of new research works and many large companies, namely Google, Microsoft, Amazon, etc.

The RoboCup dataset[43] pairs English rules with their representations in a domain-specific language that can be understood by virtual soccer-playing robots.

This method is useful in a number of contexts: Semantic parsing aims to improve various applications' efficiency and efficacy by bridging the gap between human language and machine processing in each of these domains.