Rajarshee Mitra

Rajarshee Mitra

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.

Rajarshee Mitra

Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We shift our attention to generative question answering (gQA) by which we facilitate machine to read passages and answer questions by learning to generate the answers. We frame the problem as a generative task where the encoder being a network that models the relationship between question and passage and encoding them to a vector thus facilitating the decoder to directly form an abstraction of the answer. Not being able to retain facts and making repetitions are common mistakes that affect the overall legibility of answers. To counter these issues, we employ copying mechanism and maintenance of coverage vector in our model respectively. Our results on MS-MARCO demonstrate it's superiority over baselines and we also show qualitative examples where we improved in terms of correctness and readability