Abstract
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 KBQA 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.
Highlights
Knowledge-based question answering (KB-QA) involves answering questions posed in natural language (NL) using existing knowledge bases (KBs)
Previous works (Mooney, 2007; Liang et al, 2011; Cai and Yates, 2013; Fader et al, 2013; Berant et al, 2013; Bao et al, 2014) usually leveraged mappings between NL phrases and logical predicates as lexical triggers to perform transformation tasks in semantic parsing, but they had to deal with two limitations: (i) as the meaning of a logical predicate often has different natural language expression (NLE) forms, the lexical triggers extracted for a predicate may at times are limited in size; (ii) entities detected by the named entity recognition (NER) component will be used to compose the logical forms together with the logical predicates, so their types should be consistent with the predicates as well
Our method learns semantic mappings between NLEs and the KB2 based on the paired relationships of the following three components: C denotes a set of bag-of-words as context features (c) for NLEs that are the lexical representations of a logical predicate (p) in KB; T denotes a set of entity types (t) in KB and each type can be used as the abstract expression of a subject entity
Summary
Knowledge-based question answering (KB-QA) involves answering questions posed in natural language (NL) using existing knowledge bases (KBs). Low-dimensional embeddings of n-grams, entity types, and predicates are jointly learned from an existing knowledge base and from entries that are mined from NL texts labeled as KBproperties with weak supervision. Each such entry corresponds to an NL expression of a triple in the KB. The contributions of this work are two-fold: (1) as a smoothing technique, the low-dimensional embeddings can alleviate the coverage issues of lexical triggers; (2) our joint approach integrates entity span selection and predicate mapping tasks for KB-QA For this we built independent entity embeddings as the additional component, solving the entity disambiguation problem
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