Paper: Joint Relational Embeddings for Knowledge-based Question Answering

ACL ID D14-1071
Title Joint Relational Embeddings for Knowledge-based Question Answering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Transforming a natural language (NL) question into a corresponding logical form (LF) is central to the knowledge-based question answering (KB-QA) task. Un- like most previous methods that achieve this goal based on mappings between lex- icalized phrases and logical predicates, this paper goes one step further and pro- poses a novel embedding-based approach that maps NL-questions into LFs for KB- QA by leveraging semantic associations between lexical representations and KB- properties in the latent space. Experimen- tal results demonstrate that our proposed method outperforms three KB-QA base- line methods on two publicly released QA data sets.