A Benchmark for Evaluating Machine Translation Metrics on Dialects
  Without Standard Orthography
Noëmi Aepli Chantal Amrhein Florian Schottmann Rico Sennrich
For sensible progress in natural language processing, it is important that we
are aware of the limitations of the evaluation metrics we use. In this work, we
evaluate how robust metrics are to non-standardized dialects, i.e. spelling
differences in language varieties that do not have a standard orthography. To
investigate this, we collect a dataset of human translations and human
judgments for automatic machine translations from English to two Swiss German
dialects. We further create a challenge set for dialect variation and benchmark
existing metrics' performances. Our results show that existing metrics cannot
reliably evaluate Swiss German text generation outputs, especially on segment
level. We propose initial design adaptations that increase robustness in the
face of non-standardized dialects, although there remains much room for further
improvement. The dataset, code, and models are available here:
https://github.com/textshuttle/dialect_eval