Papers by Chayan Sarkar

2 papers
tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)

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Challenge: Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language.
Approach: They propose a system to extract tasks from natural language instructions and map them to robots' established collection of skills.
Outcome: The proposed system outperforms baseline models in the training and evaluation of a dataset featuring complex instructions.
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent? (2022.emnlp-main)

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Challenge: ASR systems are often unable to recognize speech due to generic datasets and open-vocabulary modeling.
Approach: They propose to incorporate a robot’s visual information into an ASR system and improve the recognition of a spoken utterance containing a visible entity.
Outcome: The proposed method achieves a 59% relative reduction in WER from an unmodified ASR system.

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