Shared memories of bilingual subsentential alignments
This research belongs to the Natural Language Processing (NLP) field and more specifically focuses on the topic of Sub-sentential Alignments which is closely related to Machine Translation. The originality of this work consists in an example-based approach bootstrapped by the participation of non-expert annotators through an appropriate interface. The quest for a greater expressivity, such as observed in manual alignments, mainly motivates the whole approach. An important effort has been made to define a formal environment for this original architecture based on aligned examples. Several memories have been created, using syntactic informations from parsers outputs with reasonnable low-tech requirements. Two new alignment methods were compared with state-of-theart measures and three transformational metrics were introduced