A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System

• URL: http://arxiv.org/abs/2305.17433v1
• Date: Sat, 27 May 2023 10:06:03 GMT
• Title: A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
• Authors: Mauajama Firdaus, Avinash Madasu, Asif Ekbal
• Abstract summary: Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system. We propose an end-to-end framework with the capability to extract necessary slot values from the utterance. We employ a multimodal hierarchical encoder using pre-trained DialoGPT to provide a stronger context for both tasks.
• Score: 25.17100881568308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system that handles the task of understanding the user by capturing the necessary information in the form of slots and generating an appropriate response in accordance with the extracted information. Recently, dialogue systems integrated with complementary information such as images, audio, or video have gained immense popularity. In this work, we propose an end-to-end framework with the capability to extract necessary slot values from the utterance and generate a coherent response, thereby assisting the user to achieve their desired goals in a multimodal dialogue system having both textual and visual information. The task of extracting the necessary information is dependent not only on the text but also on the visual cues present in the dialogue. Similarly, for the generation, the previous dialog context comprising multimodal information is significant for providing coherent and informative responses. We employ a multimodal hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge base (Kb) to provide a stronger context for both the tasks. Finally, we design a slot attention mechanism to focus on the necessary information in a given utterance. Lastly, a decoder generates the corresponding response for the given dialogue context and the extracted slot values. Experimental results on the Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms the baselines approaches in both the tasks. The code is available at https://github.com/avinashsai/slot-gpt.

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