A Case Study on Context Encoding in Multi-Encoder based Document-Level
  Neural Machine Translation
Ramakrishna Appicharla Baban Gain Santanu Pal Asif Ekbal
Recent studies have shown that the multi-encoder models are agnostic to the
choice of context, and the context encoder generates noise which helps improve
the models in terms of BLEU score. In this paper, we further explore this idea
by evaluating with context-aware pronoun translation test set by training
multi-encoder models trained on three different context settings viz, previous
two sentences, random two sentences, and a mix of both as context.
Specifically, we evaluate the models on the ContraPro test set to study how
different contexts affect pronoun translation accuracy. The results show that
the model can perform well on the ContraPro test set even when the context is
random. We also analyze the source representations to study whether the context
encoder generates noise. Our analysis shows that the context encoder provides
sufficient information to learn discourse-level information. Additionally, we
observe that mixing the selected context (the previous two sentences in this
case) and the random context is generally better than the other settings.