tailieunhanh - Báo cáo khoa học: "Log-linear Models for Word Alignment"

We present a framework for word alignment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source langauge sentence, the target language sentence and possible additional variables. Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. In this paper, we use IBM Model 3 alignment probabilities, POS correspondence, and bilingual dictionary coverage as features. Our experiments show that log-linear models significantly outperform IBM translation models. . | Log-linear Models for Word Alignment Yang Liu Qun Liu and Shouxun Lin Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Haidian District P O. Box 2704 Beijing 100080 China yliu liuqun sxlin @ Abstract We present a framework for word alignment based on log-linear models. All knowledge sources are treated as feature functions which depend on the source langauge sentence the target language sentence and possible additional variables. Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. In this paper we use IBM Model 3 alignment probabilities POS correspondence and bilingual dictionary coverage as features. Our experiments show that log-linear models significantly outperform IBM translation models. 1 Introduction Word alignment which can be defined as an object for indicating the corresponding words in a parallel text was first introduced as an intermediate result of statistical translation models Brown et al. 1993 . In statistical machine translation word alignment plays a crucial role as word-aligned corpora have been found to be an excellent source of translation-related knowledge. Various methods have been proposed for finding word alignments between parallel texts. There are generally two categories of alignment approaches statistical approaches and heuristic approaches. Statistical approaches which depend on a set of unknown parameters that are learned from training data try to describe the relationship between a bilingual sentence pair Brown et al. 1993 Vogel and Ney 1996 . Heuristic approaches obtain word alignments by using various similarity functions between the types of the two languages Smadja et al. 1996 Ker and Chang 1997 Melamed 2000 . The central distinction between statistical and heuristic approaches is that statistical approaches are based on well-founded probabilistic models while heuristic ones are not. Studies reveal that statistical .