Arshit Gupta


2019

pdf bib
CASA-NLU : Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented ChatbotsCASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
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)

Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks-Intent Classification (IC) and Slot Labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context management to DM. However, contextual information is critical to the correct prediction of intents in a conversation. Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals over a variable context window, such as previous intents, slots, dialog acts and utterances, in addition to the current user utterance. CASA-NLU outperforms a recurrent contextual NLU baseline on two conversational datasets, yielding a gain of up to 7 % on the IC task. Moreover, a non-contextual variant of CASA-NLU achieves state-of-the-art performance on standard public datasets-SNIPS and ATIS.

pdf bib
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
Yi-An Lai | Arshit Gupta | Yi Zhang
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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 interlocutor-level 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.