  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.
