Transforming a natural language (NL) question into a corresponding logical form (LF) is central to the knowledge-based question answering (KB-QA) task. Unlike most previous methods that achieve this goal based on mappings between lexicalized phrases and logical predicates, this paper goes one step further and proposes a novel embedding-based approach that maps NL-questions into LFs for KB-QA by leveraging semantic associations between lexical representations and KBproperties in the latent space. Experimental results demonstrate that our proposed method outperforms three KB-QA baseline methods on two publicly released QA data sets.
Yang et al. REF proposed a model to map the natural language questions into logical forms by leveraging the semantic associations between lexical representations and KG properties in the latent space.
Joint Relational Embeddings for Knowledge-based Question Answering
EMNLP
Computer Science