Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Eric, Mihail"'
Autor:
Eric, Mihail, Chartier, Nicole, Hedayatnia, Behnam, Gopalakrishnan, Karthik, Rajan, Pankaj, Liu, Yang, Hakkani-Tur, Dilek
Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research. The existing literature on open-domain knowledge selection is limited and makes certain brittle assumptions on knowledge
Externí odkaz:
http://arxiv.org/abs/2203.00763
Autor:
Kim, Seokhwan, Eric, Mihail, Hedayatnia, Behnam, Gopalakrishnan, Karthik, Liu, Yang, Huang, Chao-Wei, Hakkani-Tur, Dilek
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 the APIs. This challenge track aims to expand the coverage of task-orie
Externí odkaz:
http://arxiv.org/abs/2101.09276
Autor:
Gunasekara, Chulaka, Kim, Seokhwan, D'Haro, Luis Fernando, Rastogi, Abhinav, Chen, Yun-Nung, Eric, Mihail, Hedayatnia, Behnam, Gopalakrishnan, Karthik, Liu, Yang, Huang, Chao-Wei, Hakkani-Tür, Dilek, Li, Jinchao, Zhu, Qi, Luo, Lingxiao, Liden, Lars, Huang, Kaili, Shayandeh, Shahin, Liang, Runze, Peng, Baolin, Zhang, Zheng, Shukla, Swadheen, Huang, Minlie, Gao, Jianfeng, Mehri, Shikib, Feng, Yulan, Gordon, Carla, Alavi, Seyed Hossein, Traum, David, Eskenazi, Maxine, Beirami, Ahmad, Eunjoon, Cho, Crook, Paul A., De, Ankita, Geramifard, Alborz, Kottur, Satwik, Moon, Seungwhan, Poddar, Shivani, Subba, Rajen
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling with unstruct
Externí odkaz:
http://arxiv.org/abs/2011.06486
Autor:
Mehri, Shikib, Eric, Mihail
A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings. In this paper, we focus
Externí odkaz:
http://arxiv.org/abs/2010.08684
A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains. To progress research in this direction, we introduce DialoGLUE (Dialogue Language Understanding Evaluation), a public benchmark c
Externí odkaz:
http://arxiv.org/abs/2009.13570
Autor:
Kim, Seokhwan, Eric, Mihail, Gopalakrishnan, Karthik, Hedayatnia, Behnam, Liu, Yang, Hakkani-Tur, Dilek
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 the APIs. In this paper, we propose to expand coverage of task-oriented
Externí odkaz:
http://arxiv.org/abs/2006.03533
Autor:
Hedayatnia, Behnam, Gopalakrishnan, Karthik, Kim, Seokhwan, Liu, Yang, Eric, Mihail, Hakkani-Tur, Dilek
Open-domain dialogue systems aim to generate relevant, informative and engaging responses. Seq2seq neural response generation approaches do not have explicit mechanisms to control the content or style of the generated response, and frequently result
Externí odkaz:
http://arxiv.org/abs/2005.12529
In the vision and language navigation task, the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. We propose an interactive learning framework to endow the age
Externí odkaz:
http://arxiv.org/abs/1912.00915
Autor:
Eric, Mihail, Goel, Rahul, Paul, Shachi, Kumar, Adarsh, Sethi, Abhishek, Ku, Peter, Goyal, Anuj Kumar, Agarwal, Sanchit, Gao, Shuyang, Hakkani-Tur, Dilek
MultiWOZ 2.0 (Budzianowski et al., 2018) is a recently released multi-domain dialogue dataset spanning 7 distinct domains and containing over 10,000 dialogues. Though immensely useful and one of the largest resources of its kind to-date, MultiWOZ 2.0
Externí odkaz:
http://arxiv.org/abs/1907.01669
Autor:
Eric, Mihail, Manning, Christopher D.
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discou
Externí odkaz:
http://arxiv.org/abs/1705.05414