SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window

We are The prevailing approach for neural machine translation motivated by different but related ideas: (i) neural metrics is to work at the sentence level, metrics often make use of underlying language constructing sequences of contextualized encoder models trained on wider contexts, which means states from the source sentence, a reference translation, there is no real impediment to feeding them multiple and a system output. The specific mechanics sentences, and (ii) a sentence's evaluation will vary by metric, but a general approach, employed differ based on its order in a block of sentences, by COMET (Rei et al., 2020b), is to pool these encodings so it may be helpful to evaluate each sentence in into separate sentence-level embeddings, multiple different contexts. In this work, we therefore concatenate them, and fed them into a regressor, experiment with a strided window approach which is trained against human annotations. Quality applied to COMET, whose underlying encoder is Estimation (QE) approaches work similarly, but InfoXLM (Lample and Conneau, 2019; Chi et al., do not have access to a reference translation.