activity
20172020
most citedFinding Dominant User Utterances And System Responses in Conversations

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Conversational Document Prediction to Assist Customer Care Agents

Jatin Ganhotra, Haggai Roitman, Doron Cohen +6

A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents th…

cs.CL2020

Effects of Naturalistic Variation in Goal-Oriented Dialog

Jatin Ganhotra, Robert Moore, Sachindra Joshi +1

Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are c…

cs.CL2020

Mask & Focus: Conversation Modelling by Learning Concepts

Gaurav Pandey, Dinesh Raghu, Sachindra Joshi

Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the…

cs.AI2018

Unsupervised Learning of Interpretable Dialog Models

Dhiraj Madan, Dinesh Raghu, Gaurav Pandey +1

Recently several deep learning based models have been proposed for end-to-end learning of dialogs. While these models can be trained from data without the need for any additional a…

cs.CL20173 cited

Finding Dominant User Utterances And System Responses in Conversations

Dhiraj Madan, Sachindra Joshi

There are several dialog frameworks which allow manual specification of intents and rule based dialog flow. The rule based framework provides good control to dialog designers at th…

cs.CL2017

Dialogue Act Sequence Labeling using Hierarchical encoder with CRF

Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta +2

Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to utterances in a conversation. The problem of associating semantic labels to utterances can be treated as…