Automatic Acquisition of Hierarchical Transduction Models for Machine Translation

\Ve describe a method for the fully automatic learning of hierarchical Iinite state translation models. The input to lhe method is transcribed sl)eech utterances and th(>ir corre.~l)on(ling hu. man translations, and the output is a set of head transducers, i.e. statistical le×ical headoutward transducers. A word-alignment function and a head-ranking funcl, ion are [irst obtained, and then counts are generated for hypothesized stale transitions of head transducers whose lexical translations and word order chauges are consislent with the alignment. The method has been applied to create au EnglishSpanish translation model for ~t speech tI'anslation application, with word accuracy of over 75% a,s measured by a string-dislauce comparison to three reference tra.nslations. 1 I n t r o d u c t i o n The fully automatic constructiol, r.)f' translation lnodels offers benelits in t e rn l s of develol)ment effort and i)otentially in robustness over met}> ods requiring hand-coding of linguistic information. Itowevei', there art disadvantages to the automatic approaches proposed so far. The various methods described by P, rown el. al (1990; 1993) do not take into account tile natural strllcturing of strings into phrases. Examph>based translation, exemplified hy tile work of Sumita and lida (1995), requires very large amounts o[ lraiiling luatcrial. The number o[' stales in a sinlple finite state model such as those used by Viler et el. (1996) becomes extremely large when faced with languages with large word order ditferences. The work reported in \'Vu (1997), which uses an inside-outside type of training algorithm to learn statistical contextfree transduction, has a similar motivation to tilt, Clli'l'(? l]{ \Vol'k~ but the models \re describe here, being fully lexica.1, are more suitable for direct, statistical modelling. lu this paper, we show that both the network topology and parameters of a head transducer translation model (Alshawi, t996b) can t)e Darned fully automatically from a bilingual corpus. It has already been S}lOWll (Alshawi eL el., i997) that a. head transducer model with hand-coded structure, can be trained to give better accuracy than a comparable transfer-based system, with snmller model size, computational requirements, and development ef['ort. \Ve have applied the learning method to el'eate an English-Spanish translation model for a limited domain, with word a.ccuracy 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 comI)Ol,ent o[' an I';nglishSpanish speech tra.nslation system. \'~)' first present the atet)s of tile 'brallbd/lct.io,, lraining method in Section 2. In Section 3 we describe how we obtain an alignment function froHl SOllree word subsequences to target word subsequences ['or each transcribed uttera nce and its translation. The construction of states and transitions is specitied in Section 4; the method for selecting phrase head words is described in Section 5. The string comparison eva.htatiol~ metric we use. is described in Seclion 6, atl(l the results o[ testing the method in a limited domain of l';nglish-Spanish translation are reported in Section 7.