1 citations · 1 across the 3 of their papers we have counts for
7 papers
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation
Prakhar Gupta, Harsh Jhamtani, Jeffrey P. Bigham
Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence. Such control is useful for design…
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation
Prakhar Gupta, Yulia Tsvetkov, Jeffrey P. Bigham
Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. These tasks are formulated as a binary classification of responses given…
Controlling Dialogue Generation with Semantic Exemplars
Prakhar Gupta, Jeffrey P. Bigham, Yulia Tsvetkov +1
Dialogue systems pretrained with large language models generate locally coherent responses, but lack the fine-grained control over responses necessary to achieve specific goals. A…
Using Image Captions and Multitask Learning for Recommending Query Reformulations
Gaurav Verma, Vishwa Vinay, Sahil Bansal +3
Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhan…
WriterForcing: Generating more interesting story endings
Prakhar Gupta, Vinayshekhar Bannihatti Kumar, Mukul Bhutani +1
We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence…
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
Prakhar Gupta, Shikib Mehri, Tiancheng Zhao +3
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to…