Multi-align: Combining Linguistic and Statistical Techniques to Improve Alignments for Adaptable MT

The continuously growing MT market faces the challenge of translating new languages, diverse genres, and different domains using a variety of available linguistic resources. As such, MT system adaptability has become a sought-after necessity. An adaptable statistical or Hybrid MT system relies heavily on the quality of word-level alignments of real-world data. Statistical alignment approaches provide a reasonable initial estimate for word alignment. However, they cannot handle certain types of linguistic phenomena such as long-distance dependencies and structural differences between languages (translation divergences). We address this issue in Multi-Align, a new framework for incremental testing of different alignment algorithms and their combinations. Our design allows users to tune their systems to the properties of a particular genre/domain while still benefiting from general linguistic knowledge associated with a language pair. We demonstrate that a combination of statistical and linguistically-informed alignments can resolve translation divergences during the alignment process.