James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-An Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta, Lijia Sun, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, Saab Mansour. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Shamik Roy, Sailik Sengupta, Daniele Bonadiman, Saab Mansour, Arshit Gupta. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead.In this study, we present PPTOD, a unified plug-andplay model for task-oriented dialogue.In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora.We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification.Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and lowresource scenarios.Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators. 1

Hierarchical neural networks are often used to model inherent structures within dialogues.For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors.In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutorlevel disparity while modeling goal-oriented dialogues.Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues.Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.

Arshit Gupta, Peng Zhang, Garima Lalwani, Mona Diab. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

With the advent of conversational assistants like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial settings.These systems typically include a Spoken Language understanding component which consists of two tasks: Intent Classification (IC) and Slot Labeling (SL).Generally, these two tasks are modeled together jointly to achieve best performance.However, this joint modeling adds to model obfuscation.In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency.Then, we explore a number of selfattention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets.Finally, using this framework, we propose a class of 'label-recurrent' models that are nonrecurrent apart from a 10-dimensional representation of the label history, and show that our proposed systems are highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.