1. Semantic Evaluation of Machine Translation Billy Wong, City University of Hong Kong 21 st May 2010

2. Introduction  Surface text similarity is not a reliable indicator in automatic MT evaluation  Insensitive to variation of translation  Deeper linguistic analysis is preferred  WordNet is widely used for matching synonyms  E.g. METEOR (Banerjee & Lavie 2005), TERp (Snover et al. 2009), ATEC (Wong & Kit 2010)…  Is the similarity of words between MT outputs and references fully described?

3. Motivation  WordNet  Granularity of sense distinctions is highly fine-grained  Word pairs not in the same sense:  [mom vs mother], [safeguard vs security], [expansion vs extension], [journey vs tour], [impact vs influence]…etc.  Word pairs in similar meaning  Problematic if ignore them in evaluation  What is needed is a word similarity measure  Proposal:  Utilization of word similarity measures in automatic MT evaluation

4. Word Similarity Measures  Knowledge-based (WordNet)  Wup (Wu & Palmer 1994)  Res (Resnik 1995)  Jcn (Jiang & Conrath 1997)  Hso (Hirst & St-Onge 1998)  Lch (Leacock & Chodorow 1998)  Lin (Lin 1998)  Lesk (Banerjee & Pedersen 2002)  Corpus-based  LSA (Landauer et al. 1998)

5. Experiment  Three questions:  To what extent two words are considered similar?  Which word similarity measure(s) is/are more appropriate to use?  How much performance gain an MT evaluation metric can obtain by incorporating word similarity measures?

6. Setting  Data  MetricsMATR08 development data  1992 MT outputs  8 MT systems  4 references  Evaluation metric  Unigram matching  Exact match / synonym / semantically similar  Same weight  Three variants  Precision (p), recall (r) and F-measure (f) where c: MT output t: reference translation

7. Result (1)  Correlation thresholds of each measure

8. Result (2)  Correlation of the metric

9. Conclusion  The importance of semantically similar words in automatic MT evaluation  Two word similarity measures, wup and LSA, perform relatively better  Remaining problems  Semantic similarity vs. Semantic relatedness  E.g. [committee vs chairman] (LSA)  Most WordNet similarity measures run on verbs and nouns only

10. Thank you