Confidence Measure for Word Alignment

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 932–940, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Confidence Measure for Word Alignment Fei Huang IBM T.J.Watson Research Center Yorktown Heights, NY 10598, USA huangfe@us.ibm.com Abstract In this paper we present a confidence mea- sure for word alignment based on the posterior probability of alignment links. We introduce sentence alignment confi- dence measure and alignment link con- fidence measure. Based on these mea- sures, we improve the alignment qual- ity by selecting high confidence sentence alignments and alignment links from mul- tiple word alignments of the same sen- tence pair. Additionally, we remove low confidence alignment links from the word alignment of a bilingual training corpus, which increases the alignment F-score, improves Chinese-English and Arabic-English translation quality and sig- nificantly reduces the phrase translation table size. 1 Introduction Data-driven approaches have been quite active in recent machine translation (MT) research. Many MT systems, such as statistical phrase-based and syntax-based systems, learn phrase translation pairs or translation rules from large amount of bilingual data with word alignment. The qual- ity of the parallel data and the word alignment have significant impacts on the learned transla- tion models and ultimately the quality of transla- tion output. Due to the high cost of commissioned translation, many parallel sentences are automat- ically extracted from comparable corpora, which inevitably introduce many ”noises”, i.e., inaccu- rate or non-literal translations. Given the huge amount of bilingual training data, word alignments are automatically generated using various algo- rithms ((Brown et al., 1994), (Vogel et al., 1996) Figure 1: An example of inaccurate translation and word alignment. and (Ittycheriah and Roukos, 2005)), which also introduce many word alignment errors. The example in Figure 1 shows the word align- ment of the given Chinese and English sentence pair, where the English words following each Chi- nese word is its literal translation. We find untrans- lated Chinese and English words (marked with underlines). These spurious words cause signifi- cant word alignment errors (as shown with dash lines), which in turn directly affect the quality of phrase translation tables or translation rules that are learned based on word alignment. In this paper we introduce a confidence mea- sure for word alignment, which is robust to extra or missing words in the bilingual sentence pairs, as well as word alignment errors. We propose a sentence alignment confidence measure based on the alignment’s posterior probability, and ex- tend it to the alignment link confidence measure. We illustrate the correlation between the align- ment confidence measure and the alignment qual- ity on the sentence level, and present several ap- proaches to improve alignment accuracy based on the proposed confidence measure: sentence align- ment selection, alignment link combination and alignment link filtering. Finally we demonstrate 932 the improved alignments also lead to better MT quality. The paper is organized as follows: In section 2 we introduce the sentence and alignment link confidence measures. In section 3 we …

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