Kevin Flanagan


Bilingual phrase-to-phrase alignment for arbitrarily-small datasets
Kevin Flanagan
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

This paper presents a novel system for sub-sentential alignment of bilingual sentence pairs, however few, using readily-available machine-readable bilingual dictionaries. Performance is evaluated against an existing gold-standard parallel corpus where word alignments are annotated, showing results that are a considerable improvement on a comparable system and on GIZA++ performance for the same corpus. Since naïve application of the system for N languages would require N(N - 1) dictionaries, it is also evaluated using a pivot language, where only 2(N - 1) dictionaries would be required, with surprisingly similar performance. The system is proposed as an alternative to statistical methods, for use with very small corpora or for ‘on-the-fly’ alignment.

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Filling in the gaps: what we need from TM subsegment recall
Kevin Flanagan
Proceedings of Translating and the Computer 36