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arXiv · 2304.05221

Towards preserving word order importance through Forced Invalidation

Hadeel Al-Negheimish·Pranava Madhyastha·Alessandra Russo
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Abstract

Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-trained language models are insensitive to word order. The performance on NLU tasks remains unchanged even after randomly permuting the word of a sentence, where crucial syntactic information is destroyed. To help preserve the importance of word order, we propose a simple approach called Forced Invalidation (FI): forcing the model to identify permuted sequences as invalid samples. We perform an extensive evaluation of our approach on various English NLU and QA based tasks over BERT-based and attention-based models over word embeddings. Our experiments demonstrate that Forced Invalidation significantly improves the sensitivity of the models to word order.

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Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo. 2023-04-11. Towards preserving word order importance through Forced Invalidation. https://arxiv.org/abs/2304.05221

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