no code implementations • 14 Apr 2022 • Geunseob Oh, Rahul Goel, Chris Hidey, Shachi Paul, Aditya Gupta, Pararth Shah, Rushin Shah
As the top-level intent largely governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search and improves the quality and diversity of top-k outputs.
1 code implementation • ACL 2022 • Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, Shachi Paul
Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.
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 • 5 Jul 2019 • Shachi Paul, Rahul Goel, Dilek Hakkani-Tür
In unsupervised learning experiments we achieve an F1 score of 54. 1% on system turns in human-human dialogues.
5 code implementations • LREC 2020 • Mihail Eric, Rahul Goel, Shachi Paul, Adarsh Kumar, Abhishek Sethi, Peter Ku, Anuj Kumar Goyal, Sanchit Agarwal, Shuyang Gao, Dilek Hakkani-Tur
To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.
Ranked #16 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
no code implementations • 1 Jul 2019 • Rahul Goel, Shachi Paul, Dilek Hakkani-Tür
In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type.
Ranked #18 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
no code implementations • 30 Nov 2018 • Rahul Goel, Shachi Paul, Tagyoung Chung, Jeremie Lecomte, Arindam Mandal, Dilek Hakkani-Tur
This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained.