Search Results for author: Maxine Eskenazi

Found 51 papers, 14 papers with code

Understanding the Effectiveness of Very Large Language Models on Dialog Evaluation

no code implementations27 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.

Question Answering

The DialPort tools

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.

Interactive Evaluation of Dialog Track at DSTC9

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.

Interactive Evaluation of Dialog Open-Domain Dialog +1

LAD: Language Models as Data for Zero-Shot Dialog

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).

slot-filling Slot Filling +1

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

1 code implementation25 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.

Dialogue Evaluation Dialogue Generation +3

A Survey of NLP-Related Crowdsourcing HITs: what works and what does not

no code implementations9 Nov 2021 Jessica Huynh, Jeffrey Bigham, Maxine Eskenazi

It also has the effect of giving the requester a bad reputation on the workers' forums.

Schema-Guided Paradigm for Zero-Shot Dialog

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.

Memorization Transfer Learning

GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling

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.

Open-Domain Dialog slot-filling +2

``None of the Above'': Measure Uncertainty in Dialog Response Retrieval

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.

General Classification Retrieval

Unsupervised Evaluation of Interactive Dialog with DialoGPT

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.

Dialogue Evaluation Text Generation

Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges

no code implementations10 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.

"None of the Above":Measure Uncertainty in Dialog Response Retrieval

no code implementations4 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.

General Classification Object Detection +1

CMU GetGoing: An Understandable and Memorable Dialog System for Seniors

no code implementations3 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.

Structured Fusion Networks for Dialog

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.

reinforcement-learning Reinforcement Learning (RL)

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

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.

Decision Making Dialogue Generation +4

Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems

1 code implementation28 Nov 2018 Junki Ohmura, Maxine Eskenazi

Dialog response ranking is used to rank response candidates by considering their relation to the dialog history.

Re-Ranking speech-recognition +1

DialCrowd: A toolkit for easy dialog system assessment

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.

Chatbot Test

Zero-Shot Dialog Generation with Cross-Domain Latent Actions

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.

Dialogue Generation Goal-Oriented Dialog

Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

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.

Decision Making Dialogue Generation

Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context

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.

Graph-Community Detection for Cross-Document Topic Segment Relationship Identification

no code implementations13 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.

Clustering Community Detection +1

DialPort: Connecting the Spoken Dialog Research Community to Real User Data

no code implementations8 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.

metaTED: a Corpus of Metadiscourse for Spoken Language

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.

A Readability Analysis of Campaign Speeches from the 2016 US Presidential Campaign

no code implementations18 Mar 2016 Elliot Schumacher, Maxine Eskenazi

Readability is defined as the reading level of the speech from grade 1 to grade 12.

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