Megha Jhunjhunwala


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Multi-Action Dialog Policy Learning with Interactive Human Teaching
Megha Jhunjhunwala | Caleb Bryant | Pararth Shah
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We present a framework for improving task-oriented dialog systems through online interactive teaching with human trainers. A dialog policy trained with imitation learning on a limited corpus may not generalize well to novel dialog flows often uncovered in live interactions. This issue is magnified in multi-action dialog policies which have a more expressive action space. In our approach, a pre-trained dialog policy model interacts with human trainers, and at each turn the trainers choose the best output among N-best multi-action outputs. We present a novel multi-domain, multi-action dialog policy architecture trained on MultiWOZ, and show that small amounts of online supervision can lead to significant improvement in model performance. We also present transfer learning results which show that interactive learning in one domain improves policy model performance in related domains.


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Exploring System Combination approaches for Indo-Aryan MT Systems
Karan Singla | Anupam Singh | Nishkarsh Shastri | Megha Jhunjhunwala | Srinivas Bangalore | Dipti Misra Sharma
Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants