SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation

• URL: http://arxiv.org/abs/2307.04907v1
• Date: Mon, 10 Jul 2023 21:16:46 GMT
• Title: SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation
• Authors: Bhathiya Hemanthage, Christian Dondrup, Phil Bartie, Oliver Lemon
• Abstract summary: SimpleMTOD recasts several sub-tasks in multimodal task-oriented dialogues as sequence prediction tasks. We introduce both local and de-localized tokens for objects within a scene. The model does not rely on task-specific architectural changes such as classification heads.
• Score: 2.4469484645516837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: SimpleMTOD is a simple language model which recasts several sub-tasks in multimodal task-oriented dialogues as sequence prediction tasks. SimpleMTOD is built on a large-scale transformer-based auto-regressive architecture, which has already proven to be successful in uni-modal task-oriented dialogues, and effectively leverages transfer learning from pre-trained GPT-2. In-order to capture the semantics of visual scenes, we introduce both local and de-localized tokens for objects within a scene. De-localized tokens represent the type of an object rather than the specific object itself and so possess a consistent meaning across the dataset. SimpleMTOD achieves a state-of-the-art BLEU score (0.327) in the Response Generation sub-task of the SIMMC 2.0 test-std dataset while performing on par in other multimodal sub-tasks: Disambiguation, Coreference Resolution, and Dialog State Tracking. This is despite taking a minimalist approach for extracting visual (and non-visual) information. In addition the model does not rely on task-specific architectural changes such as classification heads.

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