Journal of the ACM (JACM)
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
An algorithm for simultaneously bracketing parallel texts by aligning words
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Stochastic lexicalized inversion transduction grammar for alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reordering constraints for phrase-based statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Syntax-based alignment: supervised or unsupervised?
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A recursive statistical translation model
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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The PASCAL Challenge's textual entailment recognition task, or RTE, presents intriguing opportunities to test various implications of the strong language universal constraint posited by Wu's (1995, 1997) Inversion Transduction Grammar (ITG) hypothesis. The ITG Hypothesis provides a strong inductive bias, and has been repeatedly shown empirically to yield both efficiency and accuracy gains for numerous language acquisition tasks. Since the RTE challenge abstracts over many tasks, it invites meaningful analysis of the ITG Hypothesis across tasks including information retrieval, comparable documents, reading comprehension, question answering, information extraction, machine translation, and paraphrase acquisition. We investigate two new models for the RTE problem that employ simple generic Bracketing ITGs. Experimental results show that, even in the absence of any thesaurus to accommodate lexical variation between the Text and the Hypothesis strings, surprisingly strong results for a number of the task subsets are obtainable from the Bracketing ITG's structure matching bias alone.