A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, stateof-the-art systems often fail in this regard, particularly in cases where the source and target languages partition the "meaning space" in different ways. For instance, "I cut my finger." and "I cut my finger off." describe different states of the world but are translated to French (by both Fairseq and Google Translate) as "Je me suis coupé le doigt.", which is ambiguous as to whether the finger is detached. More generally, translation systems are typically manyto-one (non-injective) functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pretrained neural sequence-to-sequence model. This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality (measured by BLEU score).
Cohn-Gordon and Goodman REF showed that neural machine translation systems often learn many-to-one sentence mappings that result in meaning loss, such that two different sentences (meanings) in the source language are mapped to the same sentence (meaning) in the target language.
Lost in Machine Translation: A Method to Reduce Meaning Loss
ArXiv
ArXiv
Computer Science