Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering
Ekaterina Arkhangelskaia (1), Sourav Dutta (1) ((1) Saarland, University)

TL;DR
This paper uses DeepLIFT to analyze BERT's attention mechanisms in question answering, revealing patterns that may explain its superior performance and similarities to human reasoning.
Contribution
It introduces a method to interpret BERT's attention in question answering by applying DeepLIFT and analyzing attention shifts and patterns.
Findings
Attention shifts correlate with question types
Patterns resemble aspects of human reasoning
Provides insights into BERT's decision process
Abstract
There has been great success recently in tackling challenging NLP tasks by neural networks which have been pre-trained and fine-tuned on large amounts of task data. In this paper, we investigate one such model, BERT for question-answering, with the aim to analyze why it is able to achieve significantly better results than other models. We run DeepLIFT on the model predictions and test the outcomes to monitor shift in the attention values for input. We also cluster the results to analyze any possible patterns similar to human reasoning depending on the kind of input paragraph and question the model is trying to answer.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsTest · Linear Layer · Residual Connection · Attention Dropout · Linear Warmup With Linear Decay · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Dense Connections · Adam · WordPiece
