Automatic Acquisition of Hierarchical Transduction Models for Machine Translation

Automatic Acquisition of Hierarchical Transduction Models for Machine Translation Hiyan Alshawi Srinivas Bangalore Shona Douglas AT&T Labs Research 180 Park Avenue, P.O. Box 971 Florham Park, NJ 07932 USA Abstract We describe a method for the fully automatic learning of hierarchical finite state translation models. The input to the method is transcribed speech utterances and their corresponding hu- man translations, and the output is a set of head transducers, i.e. statistical lexical head- outward transducers. A word-alignment func- tion and a head-ranking function are first ob- tained, and then counts are generated for hy- pothesized state transitions of head transduc- ers whose lexical translations and word order changes are consistent with the alignment. The method has been applied to create an English- Spanish translation model for a speech trans- lation application, with word accuracy of over 75% as measured by a string-distance compari- son to three reference translations. 1 Introduction The fully automatic construction of translation lnodels offers benefits in terms of development effort and potentially in robustness over meth- ods requiring hand-coding of linguistic informa- tion. However, there are disadvantages to the automatic approaches proposed so far. The var- ious methods described by Brown et. al (1990; 1993) do not take into account the natural struc- turing of strings into phrases. Example-based translation, exemplified by the work of Sumita and Iida (1995), requires very large amounts of training material. The number of states in a simple finite state model such as those used by Vilar et al. (1996) becomes extremely large when faced with languages with large word order differences. The work reported in Wu (1997), which uses an inside-outside type of training algorithm to learn statistical context- free transduction, has a similar motivation to the current work, but the models we describe here, being fully lexical, are more suitable for direct statistical modelling. In this paper, we show that both the net- work topology and parameters of a head trans- ducer translation model (Alshawi, 1996b) can be learned fully automatically from a bilingual corpus. It has already been shown (Alshawi et al., 1997) that a head transducer model with hand-coded structure can be trained to give bet- ter accuracy than a comparable transfer-based system, with smaller model size, computational requirements, and development effort. We have applied the learning method to cre- ate an English-Spanish translation model for a limited domain, with word accuracy of over 75% measured by a string distance comparison (as used in speech recognition) to three reference translations. The resulting translation model has been used as a component of an English- Spanish speech translation system. We first present the steps of the transduc- tion training method in Section 2. In Section 3 we describe how we obtain an alignment func- tion from source word subsequences to target word subsequences for each transcribed utter- ance and its translation. The construction of states and transitions is specified in Section 4; the method for selecting phrase head words is described in Section 5. The string comparison evaluation metric …

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