Towards preserving word order importance through Forced Invalidation

• URL: http://arxiv.org/abs/2304.05221v1
• Date: Tue, 11 Apr 2023 13:42:10 GMT
• Title: Towards preserving word order importance through Forced Invalidation
• Authors: Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
• Abstract summary: We show that pre-trained language models are insensitive to word order. We propose Forced Invalidation to help preserve the importance of word order. Our experiments demonstrate that Forced Invalidation significantly improves the sensitivity of the models to word order.
• Score: 80.33036864442182
• License: http://creativecommons.org/licenses/by/4.0/
• 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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