Towards a Unified Approach to Memory- and Statistical-Based Machine Translation

Towards a Unified Approach to Memory- and Statistical-Based Machine Translation Daniel Marcu Information Sciences Institute and Department of Computer Science University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 marcu@isi.edu Abstract We present a set of algorithms that en- able us to translate natural language sentences by exploiting both a trans- lation memory and a statistical-based translation model. Our results show that an automatically derived transla- tion memory can be used within a sta- tistical framework to often find trans- lations of higher probability than those found using solely a statistical model. The translations produced using both the translation memory and the sta- tistical model are significantly better than translations produced by two com- mercial systems: our hybrid system translated perfectly 58% of the 505 sentences in a test collection, while the commercial systems translated per- fectly only 40-42% of them. 1 Introduction Over the last decade, much progress has been made in the fields of example-based (EBMT) and statistical machine translation (SMT). EBMT sys- tems work by modifying existing, human pro- duced translation instances, which are stored in a translation memory (TMEM). Many methods have been proposed for storing translation pairs in a TMEM, finding translation examples that are relevant for translating unseen sentences, and modifying and integrating translation fragments to produce correct outputs. Sato (1992), for ex- ample, stores complete parse trees in the TMEM and selects and generates new translations by performing similarity matchings on these trees. Veale and Way (1997) store complete sentences; new translations are generated by modifying the TMEM translation that is most similar to the in- put sentence. Others store phrases; new trans- lations are produced by optimally partitioning the input into phrases that match examples from the TMEM (Maruyana and Watanabe, 1992), or by finding all partial matches and then choosing the best possible translation using a multi-engine translation system (Brown, 1999). With a few exceptions (Wu and Wong, 1998), most SMT systems are couched in the noisy chan- nel framework (see Figure 1). In this framework, the source language, let’s say English, is assumed to be generated by a noisy probabilistic source.1 Most of the current statistical MT systems treat this source as a sequence of words (Brown et al., 1993). (Alternative approaches exist, in which the source is taken to be, for example, a sequence of aligned templates/phrases (Wang, 1998; Och et al., 1999) or a syntactic tree (Yamada and Knight, 2001).) In the noisy-channel framework, a mono- lingual corpus is used to derive a statistical lan- guage model that assigns a probability to a se- quence of words or phrases, thus enabling one to distinguish between sequences of words that are grammatically correct and sequences that are not. A sentence-aligned parallel corpus is then used in order to build a probabilistic translation model 1For the rest of this paper, we use the terms source and target languages according to the jargon specific to the noisy-channel framework. In this framework, the source lan- guage is …

Content truncated to 500 words