no code implementations • IJCNLP 2017 • Shikib Mehri, Giuseppe Carenini
Thread disentanglement is a precursor to any high-level analysis of multiparticipant chats.
no code implementations • NeurIPS 2018 • Shikib Mehri, Leonid Sigal
Despite being virtually ubiquitous, sequence-to-sequence models are challenged by their lack of diversity and inability to be externally controlled.
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.
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.
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.
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.
no code implementations • IJCNLP 2019 • Shikib Mehri, Maxine Eskenazi
Neural models of dialog rely on generalized latent representations of language.
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 • 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.
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
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 • 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.
1 code implementation • 28 Sep 2020 • Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur
A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains.
Ranked #5 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)
1 code implementation • NAACL 2021 • Shikib Mehri, Mihail Eric
Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances.
1 code implementation • 22 Oct 2020 • Johannes E. M. Mosig, Shikib Mehri, Thomas Kober
We present STAR, a schema-guided task-oriented dialog dataset consisting of 127, 833 utterances and knowledge base queries across 5, 820 task-oriented dialogs in 13 domains that is especially designed to facilitate task and domain transfer learning in task-oriented dialog.
no code implementations • EMNLP (nlpbt) 2020 • Muhammad A. Shah, Shikib Mehri, Tejas Srinivasan
While neural models have been shown to exhibit strong performance on single-turn visual question answering (VQA) tasks, extending VQA to a multi-turn, conversational setting remains a challenge.
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.
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.
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.
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.
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 • 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 • 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 • 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 • 24 Nov 2023 • Di Jin, Shikib Mehri, Devamanyu Hazarika, Aishwarya Padmakumar, Sungjin Lee, Yang Liu, Mahdi Namazifar
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations.
no code implementations • 29 Nov 2023 • Taha Aksu, Devamanyu Hazarika, Shikib Mehri, Seokhwan Kim, Dilek Hakkani-Tür, Yang Liu, Mahdi Namazifar
We apply CESAR on InstructDial, a benchmark for instruction-based dialog tasks.