Neural Machine Translation for Query Construction and Composition
Abstract
Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2018
- DOI:
- arXiv:
- arXiv:1806.10478
- Bibcode:
- 2018arXiv180610478S
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Databases;
- 68T99;
- I.2.6;
- I.2.7
- E-Print:
- ICML workshop on Neural Abstract Machines &