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Presented by SUN Jun

MEANT: semi-automatic metric for evaluating for MT evaluation via semantic frames an asembling of ACL11,IJCAI11,SSST11 Chi-kiu Lo & Dekai Wu. Presented by SUN Jun. MT’s often bad. MT3: So far , the sale in the mainland of China for nearly two months of SK – II line of products MT1:

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Presented by SUN Jun

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  1. MEANT: semi-automatic metric for evaluatingfor MTevaluation via semantic framesan asembling of ACL11,IJCAI11,SSST11Chi-kiu Lo & Dekai Wu Presented by SUN Jun

  2. MT’s often bad • MT3: So far , the sale in the mainland of China for nearly two months of SK – II line of products • MT1: So far , nearly two months sk –ii the sale of products in the mainland of China to resume sales. • MT2: So far, in the mainland of China to stop selling nearly two months of SK – 2 products sales resumed. • Ref: Until after their sales had ceased in mainland China for almost tow months, sales of the complete range of SK – II products have now been resumed. BLEU: 0.124 BLEU: 0.012 BLEU: 0.013

  3. Metrics besides BLEU have problems • Lexical similarity based metrics (eg. NIST, METEOR) • Good at capturing fluency • Correlate poorly with human judgment on adequacy • Syntax based (eg. STM, Liu and Gildea, 2005) • Much better at capturing grammaticality • Still more fluency oriented than adequacy-oriented • Non-automatic metrics (eg. HTER) • Use human annotators to solve non-trivial problem of finding min edit distance to evaluate adequacy • Human-training & Labor intensive

  4. ＭＥＡＮＴ：SRL for MT evaluation • Intuition behind the idea: • Useful translation help users accurately understand the basic event structure of source utterances—“ who did what to whom, when, where and why” . • Hypothesis of the work: • MT utility can best be evaluated via SRL • Better than: • N-gram based metrics like BLEU (adequacy) • Human training intensive metrics like HTER (time cost) • Complex aggregate metrics like ULC (representation transparency)

  5. Q. Do PRED & ARGj correlate to human adequacy judgments?

  6. Q. Do PRED & ARGj correlate to human adequacy judgments?

  7. Experimental settings • Exp settings 1 -- Corpus • ACL11: draw 40 sentences from Newswire datasets in GALE P2.5 (with SRL in ref/src, 3-output) • IJCAI11: draw 40, draw 35 from previous data set and draw 39 from broadcast news WMT2010-MetricsMaTr

  8. Experimental settings • Exp settings 2 – Annotation of SRL on MT reference and output • SRL: Propbank style

  9. Experimental settings • Exp settings 3 –SRL evaluation as MT evaluation • Correct, incorrect, partial (predicate & argument) • Partial: part of the meaning is correctly translated • Extra meaning in a role filler is not penalized unless it belongs in another role • Incorrectly translated predicate means the entire frame is wrong (no count of arguments)

  10. Experimental settings • Exp settings 3 –SRL evaluation as MT evaluation • F-measure Based scores • weights tuned by confusion Matrix on dev

  11. Experimental settings • Exp settings 4 – Evaluation of evaluation • WMT and NIST MetricsMaTr (2010) • Kendall’s τ rank correlation coefficient • evaluate the correlation of the proposed metric with human judgments on translation adequacy ranking. • A higher value for τ indicates more similarity to the ranking by the evaluation metric to the human judgment. • The range of possible values of correlation coefficient is [-1,1], where 1 means the systems are ranked

  12. Observations • HMEANT vs other metric

  13. Observations • HMEANT on CV data

  14. Observations • HMEANT annotated via Mono vs Bi-lingual Error analysis: annotators drop parts of the meaning in the translation when trying to align them to the source input

  15. Observations • HMEANT vs MEANT (automatic SRL) • SRL tool: ASSERT, 87% (Pradhan et al. 2004) 80 %

  16. Q2: Impact of each individual semantic role to the metric’s correlation • A preliminary exp • For each ARGj , PRED, we manually compared each English MT output against its reference translation. Using the counts thus obtained, we computed the precision, recall, and f-score for PRED and each ARGj type.

  17. IJCAI 11: evaluation the individual impact • The preliminary exp suggest effectiveness • Propose metrics for evaluating individual impact

  18. IJCAI 11: evaluation the individual impact • The preliminary exp suggest effectiveness

  19. IJCAI 11: evaluation the individual impact • Results

  20. IJCAI 11: evaluation the individual impact • Results2 Automatic SRL tool 76-93%

  21. Q: Can be Even more Accurate? • SSST11...

  22. Conclusion • ACL11 • Bring MEANT, HMEANT • HMEANT correlates well to human judges, as well as more expensive HTER • Automatic SRL yields 80% correlations • IJCAI11 • Study impact of each individual semantic roles • SSST11 • Propose Length based weighting scheme to evaluate contribution of each semantic frame

  23. END

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