Search Results for author: Behnam Hedayatnia

Found 25 papers, 8 papers with code

Multi-Sentence Knowledge Selection in Open-Domain Dialogue

no code implementations INLG (ACL) 2021 Mihail Eric, Nicole Chartier, Behnam Hedayatnia, Karthik Gopalakrishnan, Pankaj Rajan, Yang Liu, Dilek Hakkani-Tur

Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research.

Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation

no code implementations11 Oct 2021 Sashank Santhanam, Behnam Hedayatnia, Spandana Gella, Aishwarya Padmakumar, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur

We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.

Response Generation

Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense

no code implementations12 May 2021 Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tur

Towards improving language models' social intelligence, we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning.

Pretrained Language Models

Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems

no code implementations26 May 2020 Behnam Hedayatnia, Karthik Gopalakrishnan, Seokhwan Kim, Yang Liu, Mihail Eric, Dilek Hakkani-Tur

In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc.

Response Generation

Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations

no code implementations15 Sep 2019 Karthik Gopalakrishnan, Behnam Hedayatnia, Qinlang Chen, Anna Gottardi, Sanjeev Kwatra, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tür

We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles, to help further research in open-domain conversational AI.

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

no code implementations WS 2019 Sanghyun Yi, Rahul Goel, Chandra Khatri, Alessandra Cervone, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tur

Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches.

Chatbot Open-Domain Dialog +1

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

no code implementations27 Dec 2018 Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan, Kate Bland, Raefer Gabriel, Arindam Mandal, Dilek Hakkani-Tur, Gene Hwang, Nate Michel, Eric King, Rohit Prasad

In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.

Knowledge Graphs Natural Language Understanding +2

Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision

no code implementations30 Nov 2018 Chandra Khatri, Behnam Hedayatnia, Rahul Goel, Anushree Venkatesh, Raefer Gabriel, Arindam Mandal

We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets.

Chatbot

Contextual Topic Modeling For Dialog Systems

no code implementations18 Oct 2018 Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Angeliki Metanillou, Anushree Venkatesh, Raefer Gabriel, Arindam Mandal

On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances.

Chatbot Classification +3

Contextual Language Model Adaptation for Conversational Agents

no code implementations26 Jun 2018 Anirudh Raju, Behnam Hedayatnia, Linda Liu, Ankur Gandhe, Chandra Khatri, Angeliki Metallinou, Anu Venkatesh, Ariya Rastrow

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.

Automatic Speech Recognition

On Evaluating and Comparing Open Domain Dialog Systems

no code implementations11 Jan 2018 Anu Venkatesh, Chandra Khatri, Ashwin Ram, Fenfei Guo, Raefer Gabriel, Ashish Nagar, Rohit Prasad, Ming Cheng, Behnam Hedayatnia, Angeliki Metallinou, Rahul Goel, Shaohua Yang, Anirudh Raju

In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.

Goal-Oriented Dialogue Systems Open-Domain Dialog

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