Task Domain Alignment in Zero-Shot Cross-Lingual Transfer for XTREME-R

Abstract

Abstract: Transfer learning from large language models (LLMs) has emerged as a powerful technique to enable knowledge-based fine-tuning for a number of tasks, adaptation of models for different domains and even languages. However, it remains an open question, if and when transfer learning will work, i.e. leading to positive or negative transfer. In this paper, we analyze the knowledge transfer across three natural language processing (NLP) tasks - text classification, sentimental analysis, and sentence similarity, using three LLMs - BERT, RoBERTa, and XLNet - and analyzing their performance, by fine-tun

Research Question

What is the impact of task domain alignment between intermediate and target tasks on zero-shot cross-lingual transfer performance in XTREME-R, when intermediate tasks are selected from different NLP domains (e.g., NLI vs. Question Answering)?

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