no code implementations • 27 Jan 2023 • Jessica Huynh, Cathy Jiao, Prakhar Gupta, Shikib Mehri, Payal Bajaj, Vishrav Chaudhary, Maxine Eskenazi
The paper shows that the choice of datasets used for training a model contributes to how well it performs on a task as well as on how the prompt should be structured.
no code implementations • SIGDIAL (ACL) 2022 • Jessica Huynh, Shikib Mehri, Cathy Jiao, Maxine Eskenazi
The DialPort project http://dialport. org/, funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community.
no code implementations • SIGDIAL (ACL) 2022 • Shikib Mehri, Yasemin Altun, Maxine Eskenazi
To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD).
no code implementations • LREC 2022 • Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi, David Traum, Maxine Eskenazi
Our track challenges participants to develop strong response generation models and explore strategies that extend them to back-and-forth interactions with real users.
no code implementations • LREC 2022 • Jessica Huynh, Ting-Rui Chiang, Jeffrey Bigham, Maxine Eskenazi
Dialog system developers need high-quality data to train, fine-tune and assess their systems.
1 code implementation • 25 May 2022 • Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey P. Bigham
We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets.
no code implementations • 18 Mar 2022 • Shikib Mehri, Jinho Choi, Luis Fernando D'Haro, Jan Deriu, Maxine Eskenazi, Milica Gasic, Kallirroi Georgila, Dilek Hakkani-Tur, Zekang Li, Verena Rieser, Samira Shaikh, David Traum, Yi-Ting Yeh, Zhou Yu, Yizhe Zhang, Chen Zhang
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
no code implementations • 9 Nov 2021 • Jessica Huynh, Jeffrey Bigham, Maxine Eskenazi
It also has the effect of giving the requester a bad reputation on the workers' forums.
1 code implementation • SIGDIAL (ACL) 2021 • Shikib Mehri, Maxine Eskenazi
Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research.
1 code implementation • SIGDIAL (ACL) 2021 • Shikib Mehri, Maxine Eskenazi
We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning.
1 code implementation • EANCS 2021 • Yi-Ting Yeh, Maxine Eskenazi, Shikib Mehri
In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets.
no code implementations • 12 Nov 2020 • Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro, Abhinav Rastogi, Yun-Nung Chen, Mihail Eric, Behnam Hedayatnia, Karthik Gopalakrishnan, Yang Liu, Chao-Wei Huang, Dilek Hakkani-Tür, Jinchao Li, Qi Zhu, Lingxiao Luo, Lars Liden, Kaili Huang, Shahin Shayandeh, Runze Liang, Baolin Peng, Zheng Zhang, Swadheen Shukla, Minlie Huang, Jianfeng Gao, Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi, David Traum, Maxine Eskenazi, Ahmad Beirami, Eunjoon, Cho, Paul A. Crook, Ankita De, Alborz Geramifard, Satwik Kottur, Seungwhan Moon, Shivani Poddar, Rajen Subba
Interactive evaluation of dialog, and 4.
no code implementations • ACL 2020 • Yulan Feng, Shikib Mehri, Maxine Eskenazi, Tiancheng Zhao
This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks and presents our experimental results on uncertainty classification on the processed Ubuntu Dialog Corpus.
2 code implementations • SIGDIAL (ACL) 2020 • Shikib Mehri, Maxine Eskenazi
It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research.
no code implementations • 10 Jun 2020 • Maxine Eskenazi, Tiancheng Zhao
This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National Science Foundation.
1 code implementation • ACL 2020 • Shikib Mehri, Maxine Eskenazi
The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research.
Ranked #2 on
Dialogue Evaluation
on USR-PersonaChat
no code implementations • 4 Apr 2020 • Yulan Feng, Shikib Mehri, Maxine Eskenazi, Tiancheng Zhao
This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks, and presents our experimental results on uncertainty classification on the Ubuntu Dialog Corpus.
no code implementations • CONLL 2019 • Pedro Mota, Maxine Eskenazi, Lu{\'\i}sa Coheur
We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain.
no code implementations • 3 Sep 2019 • Shikib Mehri, Alan W. black, Maxine Eskenazi
Voice-based technologies are typically developed for the average user, and thus generally not tailored to the specific needs of any subgroup of the population, like seniors.
no code implementations • IJCNLP 2019 • Shikib Mehri, Maxine Eskenazi
Neural models of dialog rely on generalized latent representations of language.
2 code implementations • WS 2019 • Prakhar Gupta, Shikib Mehri, Tiancheng Zhao, Amy Pavel, Maxine Eskenazi, Jeffrey P. Bigham
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation.
1 code implementation • WS 2019 • Shikib Mehri, Tejas Srinivasan, Maxine Eskenazi
Neural dialog models have exhibited strong performance, however their end-to-end nature lacks a representation of the explicit structure of dialog.
no code implementations • ACL 2019 • Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao, Maxine Eskenazi
This paper examines various unsupervised pretraining objectives for learning dialog context representations.
3 code implementations • NAACL 2019 • Tiancheng Zhao, Kaige Xie, Maxine Eskenazi
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.
no code implementations • 20 Jan 2019 • Maxine Eskenazi, Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao
Most research on intelligent agents centers on the agent and not on the user.
1 code implementation • 28 Nov 2018 • Junki Ohmura, Maxine Eskenazi
Dialog response ranking is used to rank response candidates by considering their relation to the dialog history.
no code implementations • 24 Oct 2018 • Yulun Du, Chirag Raman, Alan W. black, Louis-Philippe Morency, Maxine Eskenazi
Distracted driving is deadly, claiming 3, 477 lives in the U. S. in 2015 alone.
no code implementations • WS 2018 • Kyusong Lee, Tiancheng Zhao, Alan W. black, Maxine Eskenazi
When creating a dialog system, developers need to test each version to ensure that it is performing correctly.
2 code implementations • WS 2018 • Tiancheng Zhao, Maxine Eskenazi
This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data.
2 code implementations • ACL 2018 • Tiancheng Zhao, Kyusong Lee, Maxine Eskenazi
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains.
no code implementations • WS 2017 • Kyusong Lee, Tiancheng Zhao, Yulun Du, Edward Cai, Allen Lu, Eli Pincus, David Traum, Stefan Ultes, Lina M. Rojas-Barahona, Milica Gasic, Steve Young, Maxine Eskenazi
DialPort collects user data for connected spoken dialog systems.
no code implementations • 12 Jul 2017 • Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen
Explainable recommendation is an important task.
no code implementations • WS 2017 • Tiancheng Zhao, Allen Lu, Kyusong Lee, Maxine Eskenazi
This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models.
1 code implementation • ACL 2017 • Tiancheng Zhao, Ran Zhao, Maxine Eskenazi
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses.
no code implementations • EMNLP 2016 • Elliot Schumacher, Maxine Eskenazi, Gwen Frishkoff, Kevyn Collins-Thompson
The problem of accurately predicting relative reading difficulty across a set of sentences arises in a number of important natural language applications, such as finding and curating effective usage examples for intelligent language tutoring systems.
no code implementations • 13 Jun 2016 • Pedro Mota, Maxine Eskenazi, Luisa Coheur
In this context, we study how different weighting mechanisms influence the discovery of word communities that relate to the different topics found in the documents.
no code implementations • 8 Jun 2016 • Tiancheng Zhao, Kyusong Lee, Maxine Eskenazi
This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users.
1 code implementation • WS 2016 • Tiancheng Zhao, Maxine Eskenazi
This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN).
no code implementations • LREC 2016 • Rui Correia, Nuno Mamede, Jorge Baptista, Maxine Eskenazi
This adaptation takes into account both the material to annotate and the setting in which the annotation task is performed.
no code implementations • 18 Mar 2016 • Elliot Schumacher, Maxine Eskenazi
Readability is defined as the reading level of the speech from grade 1 to grade 12.