Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
Multilingual aspect-based sentiment analysis remains a blind spot in transformer research despite rapid LLM progress. This systematic evaluation across seven languages and four subtasks reveals that fine-tuned large language models outperform specialized architectures on complex generative tasks, while few-shot variants narrow the gap on simpler problems. The findings expose how cross-lingual transfer and machine translation strategies shape model performance at scale, signaling that production NLP systems targeting non-English markets still require careful architecture selection rather than one-size-fits-all LLM deployment.
Modelwire context
ExplainerThe more pointed finding here isn't that fine-tuned LLMs win on complex tasks, it's that they don't uniformly dominate simpler ones, meaning practitioners can't default to a single deployment choice and must actually profile task complexity before committing to an architecture.
This connects most directly to the pattern visible in the SAGE counseling framework covered the same day: both papers push back against the assumption that general-purpose LLMs are sufficient out of the box for specialized, structured tasks. SAGE addressed this by embedding domain knowledge into the generation process; this paper addresses it by showing that task granularity and language resource level together determine which architecture actually performs. Taken together, these two papers form a quiet but consistent argument appearing across recent arXiv cs.CL work: the 'just use the biggest model' heuristic breaks down as soon as you leave English-centric, high-resource conditions. The electricity forecasting benchmark covered the same day makes an analogous point in a different domain, that domain shift invalidates assumptions baked into prior system choices.
Watch whether any of the seven languages tested here include genuinely low-resource cases where machine translation outperforms direct cross-lingual transfer, because if that result holds on a follow-up evaluation with updated multilingual models, it would force a concrete rethink of translation-as-preprocessing pipelines in production NLP.
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MentionsLarge Language Models · Transformers · Aspect-Based Sentiment Analysis · Cross-lingual Transfer
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