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

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.Comment: Published in the journal Multimedia Tools and Application

Learning to Retrieve Videos by Asking Questions

The majority of traditional text-to-video retrieval systems operate in static environments, i.e., there is no interaction between the user and the agent beyond the initial textual query provided by the user. This can be sub-optimal if the initial query has ambiguities, which would lead to many falsely retrieved videos. To overcome this limitation, we propose a novel framework for Video Retrieval using Dialog (ViReD), which enables the user to interact with an AI agent via multiple rounds of dialog, where the user refines retrieved results by answering questions generated by an AI agent. Our novel multimodal question generator learns to ask questions that maximize the subsequent video retrieval performance using (i) the video candidates retrieved during the last round of interaction with the user and (ii) the text-based dialog history documenting all previous interactions, to generate questions that incorporate both visual and linguistic cues relevant to video retrieval. Furthermore, to generate maximally informative questions, we propose an Information-Guided Supervision (IGS), which guides the question generator to ask questions that would boost subsequent video retrieval accuracy. We validate the effectiveness of our interactive ViReD framework on the AVSD dataset, showing that our interactive method performs significantly better than traditional non-interactive video retrieval systems. We also demonstrate that our proposed approach generalizes to the real-world settings that involve interactions with real humans, thus, demonstrating the robustness and generality of our framewor

End-user programming of a social robot by dialog

One of the main challenges faced by social robots is how to provide intuitive, natural and enjoyable usability for the end-user. In our ordinary environment, social robots could be important tools for education and entertainment (edutainment) in a variety of ways. This paper presents a Natural Programming System (NPS) that is geared to non-expert users. The main goal of such a system is to provide an enjoyable interactive platform for the users to build different programs within their social robot platform. The end-user can build a complex net of actions and conditions (a sequence) in a social robot via mixed-initiative dialogs and multimodal interaction. The system has been implemented and tested in Maggie, a real social robot with multiple skills, conceived as a general HRI researching platform. The robot's internal features (skills) have been implemented to be verbally accessible to the end-user, who can combine them into others that are more complex following a bottom-up model. The built sequence is internally implemented as a Sequence Function Chart (SFC), which allows parallel execution, modularity and re-use. A multimodal Dialog Manager System (DMS) takes charge of keeping the coherence of the interaction. This work is thought for bringing social robots closer to non-expert users, who can play the game of "teaching how to do things" with the robot.The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU. The authors also gratefully acknowledge the funds provided by the Spanish Ministry of Science and Innovation (MICINN) through the project named “A New Approach to Social Robots” (AROS) DPI2008-01109