Domain Mismatch Effects on Zero-Shot Cross-Lingual Transfer in XTREME-R Benchmarks

Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction (UBLI) and unsupervised neural machine translation (UNMT) do not work well when the underlying monolingual corpora come from different domains (e.g., French text from Wikipedia but English text from UN proceedi

Research goal: What is the impact of domain mismatch between intermediate English tasks and non-English target tasks (e.g., sentiment analysis vs. natural language inference) on zero-shot cross-lingual transfer performance in XTREME-R benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.