MuG-QA: Multilingual Grammatical Question Answering for RDF Data
By E. Zimina, J. Nummenmaa, K. Jarvelin, Jaakko Peltonen, Kostas Stefanidis. 2018
3 citations recorded by the source corpus.
We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.
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
- Sebastian Riedel
- Patrick Lewis
- Luke S. Zettlemoyer
- Barlas Oğuz
- Graham Neubig
- Mohit Bansal
- Wen-tau Yih
- Hannaneh Hajishirzi
In Natural Language Processing
- Sebastian Riedel
- Patrick Lewis
- Barlas Oğuz
- Ruty Rinott
- Sebastian Ruder
- Luke S. Zettlemoyer
- Graham Neubig
- Holger Schwenk
Citations in the corpus
- MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, methodology): “Cross-lingual QA Modelling Cross-lingual QA as a discipline has been explored in QA for RDF data for a number of years, such as the QALD-3 and 5 tracks (Cimiano et al., 2013; Unger et al., 2015), with more recent work from Zimina et al. (2018).”