Conflict as an Inverse of Attention in Sequence Relationship
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Attention is a very efficient way to model the relationship between two sequences by comparing how similar two intermediate representations are. Initially demonstrated in NMT, it is a standard in all NLU tasks today when efficient interaction between sequences is considered. However, we show that attention, by virtue of its composition, works best only when it is given that there is a match somewhere between two sequences. It does not very well adapt to cases when there is no similarity between two sequences or if the relationship is contrastive. We propose an Conflict model which is very similar to how attention works but which emphasizes mostly on how well two sequences repel each other and finally empirically show how this method in conjunction with attention can boost the overall performance.
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