91 citations · 123 across the 24 of their papers we have counts for
15 papers · 1 filter
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations
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 mode…
Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations
Ankita Gupta, Chulaka Gunasekara, Hui Wan +3
Dialogue summarization task involves summarizing long conversations while preserving the most salient information. Real-life dialogues often involve naturally occurring variations…
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo +3
Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in tex…
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
Jatin Ganhotra, Samuel Thomas, Hong-Kwang J. Kuo +4
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…
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Hanjie Chen, Song Feng, Jatin Ganhotra +4
Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural netwo…
Does Dialog Length matter for Next Response Selection task? An Empirical Study
Jatin Ganhotra, Sachindra Joshi
In the last few years, the release of BERT, a multilingual transformer based model, has taken the NLP community by storm. BERT-based models have achieved state-of-the-art results o…
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…
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…
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
Janarthanan Rajendran, Jatin Ganhotra, Lazaros Polymenakos
Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their ina…
Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
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. flig…
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
R. Chulaka Gunasekara, David Nahamoo, Lazaros C. Polymenakos +2
We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and cre…
A Large-Scale Corpus for Conversation Disentanglement
Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph Peper +6
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new datase…
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh +1
In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumptio…
Knowledge-based end-to-end memory networks
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. Retrieval and Generative methods have…
NE-Table: A Neural key-value table for Named Entities
Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo +3
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the…