Recognizing paraphrases and textual entailment using inversion transduction grammars

Experimental results on the MSR Paraphrase Corpus show that, even in the absence of any thesaurus to accommodate lexical variation between the paraphrases, an uninterpolated average precision of at least 76% is obtainable from the Bracketing ITG's structure matching bias alone.

Abstract

We present first results using paraphrase as well as textual entailment data to test the language universal constraint posited by Wu's (1995, 1997) Inversion Transduction Grammar (ITG) hypothesis. In machine translation and alignment, the ITG Hypothesis provides a strong inductive bias, and has been shown empirically across numerous language pairs and corpora to yield both efficiency and accuracy gains for various language acquisition tasks. Monolingual paraphrase and textual entailment recognition datasets, however, potentially facilitate closer tests of certain aspects of the hypothesis than bilingual parallel corpora, which simultaneously exhibit many irrelevant dimensions of cross-lingual variation. We investigate this using simple generic Bracketing ITGs containing no language-specific linguistic knowledge. Experimental results on the MSR Paraphrase Corpus show that, even in the absence of any thesaurus to accommodate lexical variation between the paraphrases, an uninterpolated average precision of at least 76% is obtainable from the Bracketing ITG's structure matching bias alone. This is consistent with experimental results on the Pascal Recognising Textual Entailment Challenge Corpus, which show surpisingly strong results for a number of the task subsets.