565 citations · 582 across the 11 of their papers we have counts for
11 papers
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
Chao Zhao, Spandana Gella, Seokhwan Kim +7
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely…
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
Prakhar Gupta, Yang Liu, Di Jin +6
Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability…
A Systematic Evaluation of Response Selection for Open Domain Dialogue
Behnam Hedayatnia, Di Jin, Yang Liu +1
Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2
Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, w…
Multi-Sentence Knowledge Selection in Open-Domain Dialogue
Mihail Eric, Nicole Chartier, Behnam Hedayatnia +4
Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research. The existing literature on open-domain knowledge s…
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2
Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of op…
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5
Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting u…
Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation
Sashank Santhanam, Behnam Hedayatnia, Spandana Gella +4
Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this…
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Seokhwan Kim, Yang Liu, Di Jin +4
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the natu…
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9
Seokhwan Kim, Mihail Eric, Behnam Hedayatnia +4
Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by t…