1 code implementation • SIGDIAL (ACL) 2021 • Satwik Kottur, Chinnadhurai Sankar, Zhou Yu, Alborz Geramifard
Real-world conversational agents must effectively handle long conversations that span multiple contexts.
no code implementations • 11 Nov 2023 • Hsuan Su, Rebecca Qian, Chinnadhurai Sankar, Shahin Shayandeh, Shang-Tse Chen, Hung-Yi Lee, Daniel M. Bikel
In this paper, we propose a diagnosis method to attribute bias to each component of a TOD system.
1 code implementation • 23 May 2023 • Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Raghavi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
no code implementations • 10 Mar 2023 • Prajjwal Bhargava, Pooyan Amini, Shahin Shayandeh, Chinnadhurai Sankar
As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists.
no code implementations • 8 Oct 2022 • Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar, Adithya Sagar, Mike Ross
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks.
no code implementations • Findings (NAACL) 2022 • Zhiyu Chen, Bing Liu, Seungwhan Moon, Chinnadhurai Sankar, Paul Crook, William Yang Wang
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
no code implementations • NAACL 2022 • Kun Qian, Ahmad Beirami, Satwik Kottur, Shahin Shayandeh, Paul Crook, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar
We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.
2 code implementations • 15 Dec 2021 • Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Ram Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami
Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance.
Ranked #4 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)
Dialogue State Tracking Multi-domain Dialogue State Tracking +1
1 code implementation • 21 Oct 2021 • Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami
Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.
Ranked #1 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.2
Multi-domain Dialogue State Tracking Visual Question Answering
no code implementations • SIGDIAL (ACL) 2021 • Kun Qian, Ahmad Beirami, Zhouhan Lin, Ankita De, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar
In this work, we identify an overlooked issue with dialog state annotation inconsistencies in the dataset, where a slot type is tagged inconsistently across similar dialogs leading to confusion for DST modeling.
1 code implementation • ACL 2021 • Hung Le, Chinnadhurai Sankar, Seungwhan Moon, Ahmad Beirami, Alborz Geramifard, Satwik Kottur
A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations.
no code implementations • EACL 2021 • Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT.
1 code implementation • 31 Oct 2019 • Arvind Neelakantan, Semih Yavuz, Sharan Narang, Vishaal Prasad, Ben Goodrich, Daniel Duckworth, Chinnadhurai Sankar, Xifeng Yan
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
1 code implementation • IJCNLP 2019 • Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, Kyu-Young Kim
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.
no code implementations • EACL 2021 • Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory.
no code implementations • WS 2019 • Chinnadhurai Sankar, Sujith Ravi
Open domain dialog systems face the challenge of being repetitive and producing generic responses.
1 code implementation • ACL 2019 • Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal, Sarath Chandar, Yoshua Bengio
Neural generative models have been become increasingly popular when building conversational agents.
2 code implementations • NAACL 2019 • Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Neural word representations are at the core of many state-of-the-art natural language processing models.
2 code implementations • 22 Jan 2019 • Sarath Chandar, Chinnadhurai Sankar, Eugene Vorontsov, Samira Ebrahimi Kahou, Yoshua Bengio
Modelling long-term dependencies is a challenge for recurrent neural networks.
no code implementations • 12 Jul 2018 • Iulian Vlad Serban, Chinnadhurai Sankar, Michael Pieper, Joelle Pineau, Yoshua Bengio
Deep reinforcement learning has recently shown many impressive successes.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • 20 Jan 2018 • Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
no code implementations • 7 Sep 2017 • Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeshwar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
1 code implementation • 20 Nov 2015 • Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio
This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient.