When Does Translation Require Context? A Data-driven, Multilingual Exploration
This work addresses the need for better evaluation of discourse in machine translation, though it is incremental as it builds on existing context-aware MT efforts.
The authors tackled the problem of measuring discourse phenomena in machine translation by developing the Multilingual Discourse-Aware (MuDA) benchmark, finding that common context-aware models show only marginal improvements over context-agnostic ones.
Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics. Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. In this paper, we develop the Multilingual Discourse-Aware (MuDA) benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena in any given dataset. The choice of phenomena is inspired by a novel methodology to systematically identify translations requiring context. We confirm the difficulty of previously studied phenomena while uncovering others that were previously unaddressed. We find that common context-aware MT models make only marginal improvements over context-agnostic models, which suggests these models do not handle these ambiguities effectively. We release code and data for 14 language pairs to encourage the MT community to focus on accurately capturing discourse phenomena.