  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.
