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 • SIGDIAL (ACL) 2021 • Satwik Kottur, Paul Crook, Seungwhan Moon, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
There is a growing interest in virtual assistants with multimodal capabilities, e. g., inferring the context of a conversation through scene understanding.
no code implementations • 21 Mar 2024 • Rohan Chitnis, Shentao Yang, Alborz Geramifard
In particular, we hypothesize that the objectives under which sequential decision-making can improve autocomplete systems are not tailored solely to text entry speed, but more broadly to metrics such as user satisfaction and convenience.
no code implementations • 3 Nov 2023 • Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum
Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward functions.
1 code implementation • 23 May 2023 • Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
Three popular algorithms for offline RL are Conservative Q-Learning (CQL), Behavior Cloning (BC), and Decision Transformer (DT), from the class of Q-Learning, Imitation Learning, and Sequence Modeling respectively.
no code implementations • 17 Jan 2023 • Khyathi Raghavi Chandu, Alborz Geramifard
Pre-trained models with dual and cross encoders have shown remarkable success in propelling the landscape of several tasks in vision and language in Visual Question Answering (VQA).
1 code implementation • 15 Nov 2022 • Seungwhan Moon, Satwik Kottur, Alborz Geramifard, Babak Damavandi
Recent years have seen an increasing trend in the volume of personal media captured by users, thanks to the advent of smartphones and smart glasses, resulting in large media collections.
no code implementations • 8 Nov 2022 • Satwik Kottur, Seungwhan Moon, Aram H. Markosyan, Hardik Shah, Babak Damavandi, Alborz Geramifard
We collect a new dataset C3 (Conversational Content Creation), comprising 10k dialogs conditioned on media montages simulated from a large media collection.
no code implementations • 30 Oct 2022 • Khyathi Raghavi Chandu, Alborz Geramifard
To this end, we review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling.
no code implementations • 3rd Conversational AI Workshop at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) 2019 • Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh, Bing Liu
Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for real-world applications.
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.
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 • EMNLP 2021 • Satwik Kottur, Seungwhan Moon, Alborz Geramifard, Babak Damavandi
Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment.
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 • 12 Nov 2020 • Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro, Abhinav Rastogi, Yun-Nung Chen, Mihail Eric, Behnam Hedayatnia, Karthik Gopalakrishnan, Yang Liu, Chao-Wei Huang, Dilek Hakkani-Tür, Jinchao Li, Qi Zhu, Lingxiao Luo, Lars Liden, Kaili Huang, Shahin Shayandeh, Runze Liang, Baolin Peng, Zheng Zhang, Swadheen Shukla, Minlie Huang, Jianfeng Gao, Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi, David Traum, Maxine Eskenazi, Ahmad Beirami, Eunjoon, Cho, Paul A. Crook, Ankita De, Alborz Geramifard, Satwik Kottur, Seungwhan Moon, Shivani Poddar, Rajen Subba
Interactive evaluation of dialog, and 4.
no code implementations • COLING 2020 • Zhenpeng Zhou, Ahmad Beirami, Paul Crook, Pararth Shah, Rajen Subba, Alborz Geramifard
We explore the tasks of one-shot learning and zero-shot domain transfer with DILOG on SimDial and MultiWoZ.
no code implementations • 14 Oct 2020 • Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu
Transformers have reached remarkable success in sequence modeling.
2 code implementations • COLING 2020 • Seungwhan Moon, Satwik Kottur, Paul A. Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
Next generation virtual assistants are envisioned to handle multimodal inputs (e. g., vision, memories of previous interactions, in addition to the user's utterances), and perform multimodal actions (e. g., displaying a route in addition to generating the system's utterance).
no code implementations • 7 Nov 2019 • Paul A. Crook, Shivani Poddar, Ankita De, Semir Shafi, David Whitney, Alborz Geramifard, Rajen Subba
To this end, we introduce SIMMC, an extension to ParlAI for multi-modal conversational data collection and system evaluation.
no code implementations • 19 Aug 2019 • Praveen Kumar Bodigutla, Longshaokan Wang, Kate Ridgeway, Joshua Levy, Swanand Joshi, Alborz Geramifard, Spyros Matsoukas
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management.
no code implementations • 11 Dec 2017 • Maryam Fazel-Zarandi, Shang-Wen Li, Jin Cao, Jared Casale, Peter Henderson, David Whitney, Alborz Geramifard
In this paper, we focus on learning robust dialog policies to recover from these errors.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 26 Sep 2013 • Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan How
Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation.
no code implementations • 13 Jun 2012 • Richard S. Sutton, Csaba Szepesvari, Alborz Geramifard, Michael P. Bowling
Our main results are to prove that linear Dyna-style planning converges to a unique solution independent of the generating distribution, under natural conditions.