A Deep Architecture for Semantic Parsing
By Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann. 2014
60 citations recorded by the source corpus.
Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantics. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resourcepoor languages.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Jianfeng Gao
- Mirella Lapata
- Ruslan Salakhutdinov
- Luke S. Zettlemoyer
- Li Dong
- Graham Neubig
- Yoshua Bengio
In Natural Language Processing
- Mirella Lapata
- Mohit Bansal
- Ruslan Salakhutdinov
- Li Dong
- Graham Neubig
- Jianfeng Gao
- Luke S. Zettlemoyer
- Richard Socher
Citations in the corpus
- Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (2014, methodology): “From an architectural standpoint, our encoder-decoder model is most similar to [20], who proposed a two-step embedding and generation procedure for semantic parsing.”
- Language to Logical Form with Neural Attention (2016, background): “Grefenstette et al. (2014) propose a different architecture for semantic parsing based on the combination of two neural network models.”
- Joint learning of ontology and semantic parser from text (2016, unknown): “6, compared to some other methods, like the one based on neural networks [4].”
- Gist: Efficient Data Encoding for Deep Neural Network Training (2018, unknown): “Among feature maps, we first target ReLU activation functions which are heavily used in all major CNNs targeted for vision-related tasks [22], [48], [13], [31].”