How Far Can Machine Translation Quality Take You? Extrinsic Discourse Evaluation in Goal-Oriented Setups
arXiv:2606.16596v1 Announce Type: new Abstract: Existing machine translation (MT) metrics and discourse-focused evaluations primarily assess translation quality intrinsically, without measuring the downstream consequences of translation errors. In this work, we focus on extrinsic discourse evaluation of machine translation under two distinct regimes: static and interactive. Under the static regime, we propose an entity counting task as a probe of referential consistency in discourse. We show that high intrinsic MT quality does not reliably predict downstream discourse success and strong MT sys
The paper highlights a growing need to move beyond intrinsic evaluations of AI models, particularly in natural language processing, as these models are increasingly deployed in real-world, goal-oriented applications.
This research challenges the prevailing assumption that high intrinsic machine translation quality automatically translates to success in downstream, discourse-focused tasks, forcing a re-evaluation of current AI development and assessment methods.
The focus for evaluating machine translation and potentially other generative AI shifts from purely linguistic metrics to practical, extrinsic success criteria, emphasizing the real-world utility and impact of AI outcomes.
- · Developers of extrinsic evaluation methodologies
- · Users of machine translation in complex tasks
- · AI ethicists and safety researchers
- · Developers relying solely on intrinsic MT metrics
- · Projects with black-box evaluation approaches
AI development shifts towards optimizing for downstream task success rather than solely intrinsic performance metrics.
New benchmarks and datasets emerge that are specifically designed for extrinsic, discourse-level evaluation of AI systems.
This could lead to a ' Cambrian explosion ' of specialized AI models tailored for specific complex tasks, rather than general-purpose models, as evaluation becomes more granular.
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