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.
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 • 3 Dec 2024 • Ramesh Manuvinakurike, Elizabeth Watkins, Celal Savur, Anthony Rhodes, Sovan Biswas, Gesem Gudino Mejia, Richard Beckwith, Saurav Sahay, Giuseppe Raffa, Lama Nachman
In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system.
no code implementations • 7 Aug 2024 • Shachi H Kumar, Saurav Sahay, Sahisnu Mazumder, Eda Okur, Ramesh Manuvinakurike, Nicole Beckage, Hsuan Su, Hung-Yi Lee, Lama Nachman
However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text.
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, Max Bartolo, 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, Srijan Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Sarah Luger, 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 • 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 • 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 • 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 • 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 • 17 May 2022 • Javier Felip Leon, David Gonzalez-Aguirre, Lama Nachman
The combination of collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses.
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 • 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 • 15 Nov 2020 • Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Madhulika Srikumar, Adrian Weller, Alice Xiang
Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders.
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.