91 citations · 187 across the 13 of their papers we have counts for
13 papers
Case-based Reasoning for Natural Language Queries over Knowledge Bases
Rajarshi Das, Manzil Zaheer, Dung Thai +6
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reas…
Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations
Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls Vargas +2
Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ ha…
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors
Longshaokan Wang, Maryam Fazel-Zarandi, Aditya Tiwari +2
Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users' audio signals to text data through automatic speech recognition (A…
Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation
Praveen Kumar Bodigutla, Lazaros Polymenakos, Spyros Matsoukas
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversa…
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…
Dialog System Technology Challenge 7
Koichiro Yoshino, Chiori Hori, Julien Perez +14
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dial…
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…
A Unified Implicit Dialog Framework for Conversational Search
Song Feng, R. Chulaka Gunasekara, Sunil Shashidhara +2
We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain…
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
Rui Zhang, Honglak Lee, Lazaros Polymenakos +1
In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex spea…