For ASR:
✅ H1 supported: Local accuracy still changes.
❌ H2 not supported: Metric rankings stay pretty stable.
This is probably because ASR outputs are less ambiguous, and metrics focus on similar properties, such as phonetic or lexical accuracy.

(🧵8/9)

Here’s what we found for MT and Ranking:
✅ H1 supported: Local accuracy varies a lot across systems and algorithms.
✅ H2 supported: Metric rankings shift between contexts.

🚨 Picking a metric based purely on global performance is risky!

Choose wisely. 🧙🏻‍♂️

(🧵7/9)

We evaluate this framework across three tasks:
📝 Machine Translation (MT)
🎙 Automatic Speech Recognition (ASR)
📈 Ranking

We cover popular metrics like BLEU, COMET, BERTScore, WER, METEOR, nDCG, and more!

(🧵6/9)

We test two hypotheses:
🧪H1: The absolute local accuracy of a metric changes as the context changes
🧪H2: The relative local accuracy (how metrics rank against each other) also changes across contexts

(🧵5/9)

More formally: given an input x, an output y from a context c, and a degraded version y′, we ask: how often does the metric score y higher than y′ across all inputs in the context c?

We create y′ using perturbations that simulate realistic degradations automatically.

(🧵4/9)

🎯 Metric accuracy measures how often a metric picks the better system output.
🌍 Global accuracy averages this over all outputs.
🔎 Local accuracy zooms in on a specific context (like a model, domain, or quality level).

Contexts are just meaningful slices of your data.

(🧵3/9)

Ever trusted a metric that works great on average, only for it to fail in your specific use case?

In our #NAACL2025 paper (w/ @841io.bsky.social), we show why global evaluations are not enough and why context matters more than you think.

📄 aclanthology.org/2025.finding...
#NLP #Evaluation

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