Syntactic Testsuites and Textual Entailment Recognition

We focus on textual entailments mediated by syntax and propose a new methodology to evaluate textual entailment recognition systems on such data. The main idea is to generate a syntactically annotated corpus of pairs of (non-)entailments and to use error mining methodology from the parsing field to identify the most likely sources of errors. To generate the evaluation corpus we use a template based generation approach where sentences, semantic representations and syntactic annotations are all created at the same time. Furthermore, we adapt the error mining methodology initially proposed for parsing to the field of textual entailment. To illustrate the approach, we apply the proposed methodology to the Afazio RTE system (an hybrid system focusing on syntactic entailment) and show how it permits identifying the most likely sources of errors made by this system on a testsuite of 10 000 (non-)entailment pairs which is balanced in term of (non-)entailment and in term of syntactic annotations
Published in 2010