Modularity in Inductively-Learned Word Pronunciation Systems

/ / i / II / II l II / / Modularity in Inductively-Learned Word Pronunciation Systems * Antal van den Bosch 1, Ton Weijters 2, Walter Daelemans 1 1 ILK / Computational Linguistics Tilburg University P.O. Box 90153 NL-5000 LE Tilburg The Netherlands {ant alb, walt er}@kub, nl 2 Department of Information Technology Eindhoven University of Technology P.O. Box 513 NL-5600 MB Eindhoven The Netherlands A. J.M.M. Weijt ers@tm, tue.nl Abstract In leading morpho-phonological theories and state-of-the-art text-to-speech systems it is assumed that word pronunciation cannot be learned or performed without in-between anal- yses at several abstraction levels (e.g., mor- phological, graphemic, phonemic, syllabic, and stress levels). We challenge this assump- tion for the case of English word pronunci- ation. Using IGTR~B, an inductive-learning decision-tree algorithms, we train and test three word-pronunciation systems in which the number of abstraction levels (implemented as sequenced modules) is reduced from five, via three, to one. The latter system, classifying letter strings directly as mapping to phonemes with stress markers, yields signitlcemtly better generali~tion accuracies than the two multi- module systems. Analyses of empirical results indicate that positive utility etfects of sequenc- ing modules are outweighed by cascading er- rors passed on between modules. 1 Introduction Learning word pronunciation can be a hard task when the relation between the spelling of a language and its corresponding pronunciation is many-to- many. The English writing system and its pronunci- ation are a notoriously complex example, mused by an apparent conflict between analog~/and inconsis- ~enc~/: Analogy. When two words or word chunks have a similar spelling, they tend to have a slmil~r pro- nunciation. This tendency (which generalises to other language tasks as well) is usually referred to as the analogy principle(De Saussure, 1916; Yvon, 1996; Daelemans, 1996). *This research was partially performed by the first and second author at the Department of Computer Sci- ence of the Universiteit Manstricht (The Netherlands), and partially in the context of the "Induction of Lin- guistic Knowledge" research programme, partially sup- ported by the Foundation for Language Speech and Logic (TSL), funded by the Netherlands Organization for Sci- entific Research (NWO). Inconsistency. Much of the analogy in English word pronunciation is disrupted by productive and complex word morphology, word stress, and gmphematics. Influential pre-Chomskyan ]ingu~tic theories have been pointing at the analogy principle as the under- lying principle for language learning (De Sanssure, 1916), and at induction as the reasoning method for generalising from learned instances of language tasks to new instances through analogy (Bloomfield, 1933). However, methods and resources (e.g., com- puter technology) were not available then to demon- strate how induction through analogy could be em- ployed to learn and model language tasks. Partly due to this lack of demonstrating power, Chomsky later stated "... I don't see any way of explaining the resulting final state [of language learning] in terms of any proposed general devel- opmental mecha, i~_m that has been sug- gested by artificial intelligence, sensorimo- tot mechanisms, or anything else" (Chore- sky, in (Piatelll-Palmadni, …

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