Head Automata and Bilingual Tiling: Translation with Minimal Representations
Hiyan Alshawi (AT&T Research)

TL;DR
This paper introduces a novel language model using head automata for efficient dependency parsing and a bilingual tiling approach for machine translation, emphasizing minimal representations and dynamic programming techniques.
Contribution
It proposes a new automata-based model for dependency parsing and a bilingual tiling algorithm for translation, advancing minimal and efficient linguistic representations.
Findings
Automata-based dependency parsing improves efficiency.
Bilingual tiling enhances translation quality.
Cost functions impact model performance.
Abstract
We present a language model consisting of a collection of costed bidirectional finite state automata associated with the head words of phrases. The model is suitable for incremental application of lexical associations in a dynamic programming search for optimal dependency tree derivations. We also present a model and algorithm for machine translation involving optimal ``tiling'' of a dependency tree with entries of a costed bilingual lexicon. Experimental results are reported comparing methods for assigning cost functions to these models. We conclude with a discussion of the adequacy of annotated linguistic strings as representations for machine translation.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · semigroups and automata theory
