Mitul Tiwari


Improving Dialogue Act Recognition with Augmented Data
Khyati Mahajan | Soham Parikh | Quaizar Vohra | Mitul Tiwari | Samira Shaikh
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

We present our work on augmenting dialog act recognition capabilities utilizing synthetically generated data. Our work is motivated by the limitations of current dialog act datasets, and the need to adapt for new domains as well as ambiguity in utterances written by humans. We list our observations and findings towards how synthetically generated data can contribute meaningfully towards more robust dialogue act recognition models extending to new domains. Our major finding shows that synthetic data, which is linguistically varied, can be very useful towards this goal and increase the performance from (0.39, 0.16) to (0.85, 0.88) for AFFIRM and NEGATE dialog acts respectively.