Context Models for Textual Entailment and their Application to Statistical Machine Translation

Machine Translation systems frequently encounter terms they are not able to translate appropriately. Assume for example, that an SMT system translating Fujitsu has filed a lawsuit against Tellabs for patent infringement is missing the phrases:“filed a lawsuit against” in its phrase table. A previously suggested solution is to paraphrase (e.g. to: “sued”), and then to translate the paraphrased sentence. In this work we suggest a novel solution taking place when a paraphrase is not available: By translating a sentence whose meaning is entailed by the original one, we may lose some information, yet produce a useful translation nonetheless: “Fujitsu has accused Tellabs for patent infringement”. Textual Entailment provides an appropriate framework for handling both these solutions through the use of entailment rules. We thus propose a first application of this paradigm for SMT, with a primary focus on context models. While verifying the validity of the context for a rule application is a key issue, little work has been done in that area beyond the single word level typically address in WSD tasks. To address this issue, we develop probabilistic context models for semantic inference in general and to apply them specifically in SMT setting, thus exploiting Bar Ilan’s and XRCE’s expertise in these fields.