Abstract
Machine Reading Comprehension (MRC) is a great NLP task that requires concentration on making the machine read, scan documents, and extract meaning from the text, just like a human reader.One of the MRC system challenges is not only having to understand the context to extract the answer but also being aware of the trust-worthy of the given question is possible or not.Thought pre-trained language models (PTMs) have shown their performance on many NLP downstream tasks, but it still has a limitation in the fixed-length input. We propose an unsupervised context selector that shortens the given context but still contains the answers within related contexts.In VLSP2021-MRC shared task dataset, we also empirical several training strategies consisting of unanswerable question sample selection and different adversarial training approaches, which slightly boost the performance 2.5% in EM score and 1% in F1 score.
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