APhrase-BasedModelofAlignmentforNaturalLanguage.pptVIP

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APhrase-BasedModelofAlignmentforNaturalLanguage.ppt

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APhrase-BasedModelofAlignmentforNaturalLanguage

A Phrase-Based Model of Alignmentfor Natural Language Inference Bill MacCartney, Michel Galley, and Christopher D. Manning Stanford University 26 October 2008 Natural language inference (NLI) (aka RTE) Does premise P justify an inference to hypothesis H? An informal notion of inference; variability of linguistic expression Alignment example Approaches to NLI alignment NLI alignment vs. MT alignment Doubtful — NLI alignment differs in several respects: Monolingual: can exploit resources like WordNet Asymmetric: P often longer has content unrelated to H Cannot assume semantic equivalence NLI aligner must accommodate frequent unaligned content Little training data available MT aligners use unsupervised training on huge amounts of bitext NLI aligners must rely on supervised training much less data Contributions of this paper In this paper, we: Undertake the first systematic study of alignment for NLI Existing NLI aligners use idiosyncratic methods, are poorly documented, use proprietary data Examine the relation between alignment in NLI and MT How do existing MT aligners perform on NLI alignment task? Propose a new model of alignment for NLI: MANLI Outperforms existing MT NLI aligners on NLI alignment task The MANLI aligner A model of alignment for NLI consisting of four components: Phrase-based alignment representation A feature-based scoring function Score edits as linear combination of features, then sum: Decoding using simulated annealing Perceptron learning of feature weights We use a variant of averaged perceptron [Collins 2002] The MSR RTE2 alignment data Previously, little supervised data Now, MSR gold alignments for RTE2 [Brockett 2007] dev test sets, 800 problems each Token-based, but many-to-many allows implicit alignment of phrases 3 independent annotators 3 of 3 agreed on 70% of proposed links 2 of 3 agreed on 99.7% of proposed links merged using majority rule Evaluation on MSR data We evaluate several systems on MSR data A simple baseline aligner MT a