Morphological, Syntactical and Semantic Knowledge in Statistical Machine Translation
Marta R. Costa‐jussà, Chris Quirk · 2013
This tutorial focuses on how morphology, syntax and semantics may be introduced into a standard phrase-based statistical machine translation system with techniques such as machine learning, parsing and word sense disambiguation, among others. Regarding the phrase-based system, we will describe only the key theory behind it. The main challenges of this approach are that the output contains unknown words, wrong word orders and non-adequate translated words. To solve these challenges, recent research enhances the standard system using morphology, syntax and semantics. Morphologically-rich languages have many different surface forms, even though the stem of a word may be the same. This leads to rapid vocabulary growth, as various prefixes and suffixes can combine with stems in a large number of possible combinations. Language model probability estimation is less robust because many more word forms occur rarely in the data. This morphologically-induced sparsity can be reduced by incorporating morphological information into the SMT system. We will describe the three most common solutions to face morphology: preprocessing the data so that the input language more closely resembles the output language; using additional language models that introduce morphological information; and post-processing the output to add proper inflections. Syntax differences between the source and target language may lead to significant differences in the relative word order of translated words. Standard phrasebased SMT systems surmount reordering/syntactic challenges by learning from data. Most approaches model reordering inside translation units and using statistical methodologies, which limits the performance in language pairs with different grammatical structures. We will briefly introduce some recent advances in