Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model
Kosuke Takahashi, Katsuhito Sudoh, Satoshi Nakamura · 2020
We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references.The proposed method evaluates a translation hypothesis in a regression model.The model takes the paired source, reference, and hypothesis sentence all together as an input.A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors.Our experiments show that our proposed method using Crosslingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences.Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.