1 code implementation • 20 May 2023 • Chao Zhao, Spandana Gella, Seokhwan Kim, Di Jin, Devamanyu Hazarika, Alexandros Papangelis, Behnam Hedayatnia, Mahdi Namazifar, Yang Liu, Dilek Hakkani-Tur
We hope this task and dataset can promote further research on TOD and subjective content understanding.
no code implementations • 10 Feb 2023 • Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on Intent Detection on HWU64 5-shot
1 code implementation • 7 Feb 2023 • Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
no code implementations • 25 Oct 2022 • Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
no code implementations • SIGDIAL (ACL) 2022 • Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur
Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.
no code implementations • 22 Jun 2022 • Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
no code implementations • 1 Jun 2022 • Alexandros Papangelis, Nicole Chartier, Pankaj Rajan, Julia Hirschberg, Dilek Hakkani-Tur
In this work, we conduct a study to better understand how people rate their interactions with conversational agents.
1 code implementation • Findings (ACL) 2022 • Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis, Yang Liu, Dilek Hakkani-Tur
Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect.
no code implementations • 16 Nov 2021 • Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis, Yang Liu, Dilek Hakkani-Tur
Automatic evaluation is beneficial for open-domain dialog system development.
no code implementations • 15 Oct 2021 • Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur
Rich, open-domain textual data available on the web resulted in great advancements for language processing.
1 code implementation • 28 Sep 2021 • Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
no code implementations • SIGDIAL (ACL) 2021 • Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar, Seokhwan Kim, Gokhan Tur, Dilek Hakkani-Tur
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
no code implementations • 29 Dec 2020 • Yi-Chia Wang, Alexandros Papangelis, Runze Wang, Zhaleh Feizollahi, Gokhan Tur, Robert Kraut
The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.
no code implementations • 5 Nov 2020 • Mahdi Namazifar, Alexandros Papangelis, Gokhan Tur, Dilek Hakkani-Tür
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
no code implementations • 28 Jan 2020 • Yue Weng, Sai Sumanth Miryala, Chandra Khatri, Runze Wang, Huaixiu Zheng, Piero Molino, Mahdi Namazifar, Alexandros Papangelis, Hugh Williams, Franziska Bell, Gokhan Tur
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 24 Jan 2020 • Andrea Madotto, Mahdi Namazifar, Joost Huizinga, Piero Molino, Adrien Ecoffet, Huaixiu Zheng, Alexandros Papangelis, Dian Yu, Chandra Khatri, Gokhan Tur
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
4 code implementations • 17 Jan 2020 • Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri, Yi-Chia Wang, Piero Molino, Gokhan Tur
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
4 code implementations • WS 2019 • Alexandros Papangelis, Yi-Chia Wang, Piero Molino, Gokhan Tur
and their own objectives, and can only interact via natural language they generate.
no code implementations • 9 Oct 2017 • Alexandros Papangelis, Panagiotis Papadakos, Margarita Kotti, Yannis Stylianou, Yannis Tzitzikas, Dimitris Plexousakis
In this work we discuss the related challenges and describe an approach towards the fusion of state-of-the-art technologies from the Spoken Dialogue Systems (SDS) and the Semantic Web and Information Retrieval domains.