Fuzzy-Match Repair Guided by Quality Estimation

Computer-aided translation tools based on translation memories are widely used to assist professional translators. A translation memory (TM) consists of a set of translation units (TU) made up of source- and target-language segment pairs. For the translation of a new source segment <inline-formula><tex-math notation="LaTeX">$s^{\prime }$</tex-math><alternatives><mml:math><mml:msup><mml:mi>s</mml:mi><mml:mo>'</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq1-3021361.gif"/></alternatives></inline-formula>, these tools search the TM and retrieve the TUs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq2-3021361.gif"/></alternatives></inline-formula> whose source segments are more similar to <inline-formula><tex-math notation="LaTeX">$s^{\prime }$</tex-math><alternatives><mml:math><mml:msup><mml:mi>s</mml:mi><mml:mo>'</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq3-3021361.gif"/></alternatives></inline-formula>. The translator then chooses a TU and edit the target segment <inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq4-3021361.gif"/></alternatives></inline-formula> to turn it into an adequate translation of <inline-formula><tex-math notation="LaTeX">$s^{\prime }$</tex-math><alternatives><mml:math><mml:msup><mml:mi>s</mml:mi><mml:mo>'</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq5-3021361.gif"/></alternatives></inline-formula>. <italic>Fuzzy-match repair</italic> (FMR) techniques can be used to automatically modify the parts of <inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq6-3021361.gif"/></alternatives></inline-formula> that need to be edited. We describe a language-independent FMR method that first uses machine translation to generate, given <inline-formula><tex-math notation="LaTeX">$s^{\prime }$</tex-math><alternatives><mml:math><mml:msup><mml:mi>s</mml:mi><mml:mo>'</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq7-3021361.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq8-3021361.gif"/></alternatives></inline-formula>, a set of candidate fuzzy-match repaired segments, and then chooses the best one by estimating their quality. An evaluation on three different language pairs shows that the selected candidate is a good approximation to the best (oracle) candidate produced and is closer to reference translations than machine-translated segments and unrepaired fuzzy matches (<inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="sanchezmartinez-ieq9-3021361.gif"/></alternatives></inline-formula>). In addition, a single quality estimation model trained on a mix of data from all the languages performs well on any of the languages used.