UAlacant word-level machine translation quality estimation system at WMT 2015

By Miquel Esplà-Gomis, Felipe Sánchez-Martínez, Mikel L. Forcada. 2015

11 citations recorded by the source corpus.

This paper describes the Universitat d’Alacant submissions (labelled as UAlacant) for the machine translation quality estimation (MTQE) shared task in WMT 2015, where we participated in the wordlevel MTQE sub-task. The method we used to produce our submissions uses external sources of bilingual information as a black box to spot sub-segment correspondences between a source segmentS and the translation hypothesisT produced by a machine translation system. This is done by segmenting bothS andT into overlapping subsegments of variable length and translating them in both translation directions, using the available sources of bilingual information on the fly. For our submissions, two sources of bilingual information were used: machine translation (Apertium and Google Translate) and the bilingual concordancer Reverso Context. After obtaining the subsegment correspondences, a collection of features is extracted from them, which are then used by a binary classifer to obtain the final “GOOD” or “BAD” word-level quality labels. We prepared two submissions for this year’s edition of WMT 2015: one using the features produced by our system, and one combining them with the baseline features published by the organisers of the task, which were ranked third and first for the sub-task, respectively.

Subfields: Machine Translation

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In Machine Translation

  1. Varvara Logacheva
  2. Lucia Specia
  3. Frédéric Blain
  4. Kashif Shah
  5. Trevor Cohn
  6. Daniel Beck

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