no code implementations • 6 Jun 2023 • Abishek Komma, Nagesh Panyam Chandrasekarasastry, Timothy Leffel, Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas, Aram Galstyan
Measurement of interaction quality is a critical task for the improvement of spoken dialog systems.
no code implementations • NAACL 2021 • Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
no code implementations • NAACL 2021 • Petr Marek, Vishal Ishwar Naik, Vincent Auvray, Anuj Goyal
However, collecting the annotated OOD data for a given domain is an expensive process.
Generative Adversarial Network Out of Distribution (OOD) Detection
no code implementations • WS 2019 • Nikolaos Malandrakis, Minmin Shen, Anuj Goyal, Shuyang Gao, Abhishek Sethi, Angeliki Metallinou
Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents.
no code implementations • NAACL 2019 • Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou
Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.
1 code implementation • 13 Nov 2018 • Aditya Siddhant, Anuj Goyal, Angeliki Metallinou
Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer.
no code implementations • NAACL 2018 • Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas
Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions.