no code implementations • SIGDIAL (ACL) 2021 • Ramesh Manuvinakurike, Saurav Sahay, Wenda Chen, Lama Nachman
In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue.
no code implementations • ACL 2022 • Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
Intelligent conversational assistants have become an integral part of our lives for performing simple tasks.
no code implementations • EMNLP (newsum) 2021 • Nicole Beckage, Shachi H Kumar, Saurav Sahay, Ramesh Manuvinakurike
By compressing the previous contexts by ~70%, we achieve better ROUGE scores over our baseline models.
no code implementations • SLPAT (ACL) 2022 • Shachi H. Kumar, Hsuan Su, Ramesh Manuvinakurike, Max Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle.
1 code implementation • 18 Apr 2024 • Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
no code implementations • 29 Nov 2023 • Ramesh Manuvinakurike, Saurav Sahay, Sangeeta Manepalli, Lama Nachman
Large Language Models (LLMs) exhibit powerful summarization abilities.
no code implementations • 17 Oct 2023 • Hsuan Su, Cheng-Chu Cheng, Hua Farn, Shachi H Kumar, Saurav Sahay, Shang-Tse Chen, Hung-Yi Lee
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4.
no code implementations • 1 Jun 2023 • Eda Okur, Roddy Fuentes Alba, Saurav Sahay, Lama Nachman
Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 8 Mar 2023 • Sumanta Bhattacharyya, Ramesh Manuvinakurike, Sahisnu Mazumder, Saurav Sahay
In this work, we develop a prompting approach for incremental summarization of task videos.
no code implementations • 12 Feb 2023 • Hsuan Su, Shachi H Kumar, Sahisnu Mazumder, Wenda Chen, Ramesh Manuvinakurike, Eda Okur, Saurav Sahay, Lama Nachman, Shang-Tse Chen, Hung-Yi Lee
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
no code implementations • 2 Dec 2022 • Shih-Cheng Huang, Shih-Heng Wang, Min-Han Shih, Saurav Sahay, Hung-Yi Lee
To tackle these issues, we propose a general framework to enhance the few-shot adaptation and cross-domain generalization ability of parameter-efficient methods.
no code implementations • 7 Nov 2022 • Eda Okur, Saurav Sahay, Roddy Fuentes Alba, Lama Nachman
The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 3 Nov 2022 • Ramesh Manuvinakurike, Sovan Biswas, Giuseppe Raffa, Richard Beckwith, Anthony Rhodes, Meng Shi, Gesem Gudino Mejia, Saurav Sahay, Lama Nachman
Development of task guidance systems for aiding humans in a situated task remains a challenging problem.
no code implementations • 8 Jun 2022 • Hsuan Su, PoHan Chi, Shih-Cheng Huang, Chung Ho Lam, Saurav Sahay, Shang-Tse Chen, Hung-Yi Lee
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model.
no code implementations • games (LREC) 2022 • Eda Okur, Saurav Sahay, Lama Nachman
Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings.
no code implementations • LREC 2022 • Eda Okur, Saurav Sahay, Lama Nachman
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time.
no code implementations • 15 Mar 2022 • Maximillian Chen, Weiyan Shi, Feifan Yan, Ryan Hou, Jingwen Zhang, Saurav Sahay, Zhou Yu
Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic.
no code implementations • 4 Dec 2021 • Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Saurav Sahay, Lama Nachman
We build models that can suggest relevant cues in the dialog response context which is used to control response generation and can speed up communication.
no code implementations • NAACL (DaSH) 2021 • Saurav Sahay, Eda Okur, Nagib Hakim, Lama Nachman
Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS.
no code implementations • NAACL 2021 • Hsuan Su, Jiun-Hao Jhan, Fan-Yun Sun, Saurav Sahay, Hung-Yi Lee
Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans.
no code implementations • 1 Jan 2021 • Weiyan Shi, Yu Li, Saurav Sahay, Zhou Yu
Despite the recent success of large-scale language models on various downstream NLP tasks, the repetition and inconsistency problems still persist in dialogue response generation.
no code implementations • Findings (EMNLP) 2021 • Weiyan Shi, Yu Li, Saurav Sahay, Zhou Yu
Persuasion dialogue systems reflect the machine's ability to make strategic moves beyond verbal communication, and therefore differentiate themselves from task-oriented or open-domain dialogue systems and have their own unique values.
no code implementations • WS 2020 • Eda Okur, Shachi H. Kumar, Saurav Sahay, Lama Nachman
To this end, understanding passenger intents from spoken interactions and vehicle vision systems is a crucial component for developing contextual and visually grounded conversational agents for AV.
no code implementations • WS 2020 • Saurav Sahay, Eda Okur, Shachi H. Kumar, Lama Nachman
In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers.
no code implementations • 20 Dec 2019 • Shachi H. Kumar, Eda Okur, Saurav Sahay, Jonathan Huang, Lama Nachman
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes.
no code implementations • 20 Dec 2019 • Shachi H. Kumar, Eda Okur, Saurav Sahay, Jonathan Huang, Lama Nachman
Recent progress in visual grounding techniques and Audio Understanding are enabling machines to understand shared semantic concepts and listen to the various sensory events in the environment.
no code implementations • 20 Dec 2019 • Saurav Sahay, Shachi H. Kumar, Eda Okur, Haroon Syed, Lama Nachman
Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms.
no code implementations • 20 Sep 2019 • Eda Okur, Shachi H. Kumar, Saurav Sahay, Lama Nachman
Understanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV).
no code implementations • 23 Apr 2019 • Eda Okur, Shachi H. Kumar, Saurav Sahay, Asli Arslan Esme, Lama Nachman
Understanding passenger intents and extracting relevant slots are important building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 20 Dec 2018 • Shachi H. Kumar, Eda Okur, Saurav Sahay, Juan Jose Alvarado Leanos, Jonathan Huang, Lama Nachman
With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home.
no code implementations • WS 2018 • Saurav Sahay, Shachi H. Kumar, Rui Xia, Jonathan Huang, Lama Nachman
Understanding Affect from video segments has brought researchers from the language, audio and video domains together.
no code implementations • WS 2017 • George Kennedy, Andrew McCollough, Edward Dixon, Alexei Bastidas, John Ryan, Chris Loo, Saurav Sahay
This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams.