Phrase Weights. Statistical NLP Spring Lecture 10: Phrase Alignment. Dan Klein UC Berkeley

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1 Statistical NLP Spring Phrase Weights Lecture : Phrase Alignment Dan Klein UC Berkeley

2 Phrase Scoring Phrase Size cats aiment poisson les chats le frais. Learning weights has been tried, several times: [Marcu and Wong, ] [DeNero et al, 6] and others Seems not to work well, for a variety of partially understood reasons Phrases do help But they don t need to be long Why should this be? like fresh fish.. Main issue: big chunks get all the weight, obvious priors don t help Though, [DeNero et al 8] Lexical Weighting Phrase Alignment

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4 Identifying Phrasal Translations, a number of US citizens, 一批美国公民 past two year in, one lots US citizen Past Go over Phrase alignment models: Underlying assumption: Choose a segmentation and a one-to-one phrase alignment There is a correct phrasal segmentation Unique Segmentations? Identifying Phrasal Translations, a number of US citizens, a number of US citizens, 一批美国公民 past two year in, one lots US citizen, 一批美国公民 past two year in, one lots US citizen Problem : Overlapping phrases can be useful (and complementary) Problem : Phrases and their sub-phrases can both be useful Hypothesis: This is why models of phrase alignment don t work well This talk: Modeling sets of overlapping, multi-scale phrase pairs Input: sentence pairs Output: extracted phrases But the Standard Pipeline has Overlap! Our Task: Predict Extraction Sets Conditional model of extraction sets given sentence pairs past two year in 过去 两 年 中 5 过去 两 年 中 5 Sentence Pair Word Alignment Extracted Phrases Sentence Pair Extracted Phrases + ``Word Phrases Alignments M O T I V A T I O N M O T I V A T I O N

5 Alignments Imply Extraction Sets Incorporating Possible Alignments Word-level alignment links Word-to-span alignments Extraction set of bispans Sure and possible word links Word-to-span alignments Extraction set of bispans 5 past two year in 5 past two year in M O D E L M O D E L Linear Model for Extraction Sets Features on Bispans and Sure Links Some features on sure links Features on sure links Features on all bispans 5 HMM posteriors Presence in dictionary Numbers & punctuation 过地球 go over Earth Features on bispans over the Earth HMM phrase table features: e.g., phrase relative frequencies Lexical indicator features for phrases with common words Shape features: e.g., Chinese character counts Monolingual phrase features: e.g., the M O D E L F E A T U R E S Getting Gold Extraction Sets Discriminative Training with MIRA Hand Aligned: Sure and possible word links Word-to-span alignments Extraction set of bispans Deterministic: Find min and max alignment index for each word Gold (annotated) Guess (arg max w ɸ) Deterministic: A bispan is included iff every word within the bispan aligns within the bispan Loss function: Training Criterion: F-score of bispan errors (precision & recall) Minimal change to w such that the gold is preferred to the guess by a loss-scaled margin T R A I N I N G T R A I N I N G 5

6 Inference: An ITG Parser Experimental Setup ITG captures some bispans Chinese-to-English newswire Parallel corpus:. million words; sentences length Supervised data: 5 training & 9 test sentences (NIST ) Unsupervised Model: Jointly trained HMM (Berkeley Aligner) MT systems: Tuned and tested on NIST and 5 I N F E R E N C E Baselines and Limited Systems Word Alignment Performance HMM: Joint training & competitive posterior decoding Source of many features for supervised models State-of-the-art unsupervised baseline -AER HMM ITG Coarse Full ITG: Coarse: Supervised ITG aligner with block terminals Re-implementation of Haghighi et al., 9 State-of-the-art supervised baseline Supervised block ITG + possible alignments Coarse pass of full extraction set model Recall Precision Extracted Bispan Performance Translation Performance (BLEU) F5 F Recall HMM ITG Coarse Full Joshua Moses HMM ITG Coarse Full Precision Supervised conditions also included HMM alignments 6

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