AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data

TL;DR

AMUSE introduces a multilingual semantic parsing pipeline that maps natural language questions to SPARQL queries using probabilistic models, overcoming lexical gaps with innovative translation and embedding techniques, and demonstrating effectiveness across multiple languages.

Contribution

It presents the first multilingual QALD pipeline that learns to convert questions into logical forms using probabilistic inference and language-independent representations.

Findings

Effective cross-lingual performance on QALD-6 dataset

Novel combination of machine translation and word embeddings

Overcomes lexical gaps in multilingual semantic parsing

Abstract

The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic representations. We rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish.