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Friday, March 21, 2014 - 12:00pm

IRCS Conference Room

Chris Callison-Burch
Department of Computer and Information Sciences
University of Pennsylvania

Large-scale paraphrasing for natural language understanding and generation

I will present my method for learning paraphrases - pairs of English expressions with equivalent meaning - from the bilingual parallel corpora, which are more commonly used to train statistical machine translation systems. My method pairs English phrases like <thrown into jail, imprisoned> when they share an aligned foreign phrase like festgenommen. Because bitexts are large and because a phrase can be aligned many different foreign phrases (including phrases in multiple foreign languages), the method extracts a diverse set of paraphrases. For thrown into jail, we not only learn imprisoned, but also arrested, detained, incarcerated, jailed, locked up, taken into custody, and thrown into prison, along with a set of incorrect/noisy paraphrases. I'll show a number of methods for filtering out the poor paraphrases, by defining a paraphrase probability calculated from translation model probabilities, and by re-ranking the candidate paraphrases using monolingual distributional similarity measures.

In addition to lexical and phrasal paraphrases, I'll show how the bilingual pivoting method can be extended to learn meaning-preserving syntactic transformations like the English possessive rule or dative shift. I'll describe a way of using synchronous context free grammars (SCGFs) to represent these rules. This formalism allows us to re-use much of the machinery from statistical machine translation to perform sentential paraphrasing. We can adapt our "paraphrase grammars" to do monolingual text-to-text generation tasks like sentence compression or simplification.

I'll also briefly sketch future directions for adding a semantics to the paraphrases, which my lab has begun exploring for the new DARPA DEFT program.