no code implementations • 19 Feb 2024 • Md Arafat Sultan, Jatin Ganhotra, Ramón Fernandez Astudillo
We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language model (LLM).
no code implementations • 15 Nov 2023 • Ankita Gupta, Chulaka Gunasekara, Hui Wan, Jatin Ganhotra, Sachindra Joshi, Marina Danilevsky
We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.
no code implementations • 11 Apr 2022 • Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo, Jatin Ganhotra, Brian Kingsbury, Eric Fosler-Lussier
In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
no code implementations • 18 Aug 2021 • Jatin Ganhotra, Samuel Thomas, Hong-Kwang J. Kuo, Sachindra Joshi, George Saon, Zoltán Tüske, Brian Kingsbury
End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently.
1 code implementation • NAACL 2021 • Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
no code implementations • 24 Jan 2021 • Jatin Ganhotra, Sachindra Joshi
However, there has been little to no research on the impact of this limitation with respect to dialog tasks.
1 code implementation • EMNLP 2020 • Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Jatin Ganhotra, Robert Moore, Sachindra Joshi, Kahini Wadhawan
Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations.
no code implementations • EMNLP 2020 • Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
Customer support agents play a crucial role as an interface between an organization and its end-users.
1 code implementation • TACL 2019 • Janarthanan Rajendran, Jatin Ganhotra, Lazaros Polymenakos
In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently.
no code implementations • 11 Jul 2019 • Jatin Ganhotra, Siva Sankalp Patel, Kshitij Fadnis
Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e. g. flight booking, hotel reservation, technical support, student advising etc.
no code implementations • 26 Dec 2018 • R. Chulaka Gunasekara, David Nahamoo, Lazaros C. Polymenakos, Jatin Ganhotra, Kshitij P. Fadnis
The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation.
3 code implementations • ACL 2019 • Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros Polymenakos, Walter S. Lasecki
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.
1 code implementation • EMNLP 2018 • Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, Lazaros Polymenakos
We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting.
no code implementations • 23 Apr 2018 • Jatin Ganhotra, Lazaros Polymenakos
End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction.
1 code implementation • RANLP 2019 • Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, Lazaros Polymenakos
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
no code implementations • ICLR 2018 • Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh
Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs).