Georgios Pantazopoulos
2023
Multitask Multimodal Prompted Training for Interactive Embodied Task Completion
Georgios Pantazopoulos
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Malvina Nikandrou
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Amit Parekh
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Bhathiya Hemanthage
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Arash Eshghi
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Ioannis Konstas
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Verena Rieser
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Oliver Lemon
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Alessandro Suglia
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and observations, and 2) referential disambiguation. To tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a unified encoder-decoder model that reasons over images and trajectories, and casts action prediction as multimodal text generation. By unifying all tasks as text generation, EMMA learns a language of actions which facilitates transfer across tasks. Different to previous modular approaches with independently trained components, we use a single multitask model where each task contributes to goal completion. EMMA performs on par with similar models on several VL benchmarks and sets a new state-of-the-art performance (36.81% success rate) on the Dialog-guided Task Completion (DTC), a benchmark to evaluate dialog-guided agents in the Alexa Arena.
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Co-authors
- Malvina Nikandrou 1
- Amit Parekh 1
- Bhathiya Hemanthage 1
- Arash Eshghi 1
- Ioannis Konstas 1
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