Acquiring Word-Meaning Mappings for Natural Language Interfaces

TL;DR

This paper introduces WOLFIE, a system that learns semantic lexicons from sentence-meaning pairs, demonstrating its effectiveness across multiple languages and its scalability to larger datasets, with a focus on reducing annotation effort through active learning.

Contribution

The paper presents WOLFIE, a novel system for acquiring semantic lexicons from corpora, and explores active learning to minimize annotation effort in natural language understanding.

Findings

WOLFIE effectively learns semantic lexicons for multiple languages.

WOLFIE outperforms similar systems in lexicon quality.

Active learning reduces annotation requirements significantly.

Abstract

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.