Robustness of Synergistic Optimization in Cross-Lingual NLI on XNLI

Abstract

Abstract: Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose app

Research Question

Does the proposed synergistic optimization method maintain robustness in cross-lingual natural language inference tasks on the XNLI dataset when evaluated against models trained with exclusive cross-lingual objectives?

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