S-EQA: Tackling Situational Queries in Embodied Question Answering

• URL: http://arxiv.org/abs/2405.04732v1
• Date: Wed, 8 May 2024 00:45:20 GMT
• Title: S-EQA: Tackling Situational Queries in Embodied Question Answering
• Authors: Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Dinesh Manocha, Reza Ghanadhan,
• Abstract summary: We present and tackle the problem of Embodied Question Answering with Situational Queries (S-EQA) in a household environment. We first introduce a novel Prompt-Generate-Evaluate scheme that wraps around an LLM's output to create a dataset of unique situational queries. We validate the generated dataset via a large scale user-study conducted on M-Turk, and introduce it as S-EQA, the first dataset tackling EQA with situational queries.
• Score: 48.43453390717167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries that directly reference target objects and quantifiable properties pertaining them, EQA with situational queries (such as "Is the bathroom clean and dry?") is more challenging, as the agent needs to figure out not just what the target objects pertaining to the query are, but also requires a consensus on their states to be answerable. Towards this objective, we first introduce a novel Prompt-Generate-Evaluate (PGE) scheme that wraps around an LLM's output to create a dataset of unique situational queries, corresponding consensus object information, and predicted answers. PGE maintains uniqueness among the generated queries, using multiple forms of semantic similarity. We validate the generated dataset via a large scale user-study conducted on M-Turk, and introduce it as S-EQA, the first dataset tackling EQA with situational queries. Our user study establishes the authenticity of S-EQA with a high 97.26% of the generated queries being deemed answerable, given the consensus object data. Conversely, we observe a low correlation of 46.2% on the LLM-predicted answers to human-evaluated ones; indicating the LLM's poor capability in directly answering situational queries, while establishing S-EQA's usability in providing a human-validated consensus for an indirect solution. We evaluate S-EQA via Visual Question Answering (VQA) on VirtualHome, which unlike other simulators, contains several objects with modifiable states that also visually appear different upon modification -- enabling us to set a quantitative benchmark for S-EQA. To the best of our knowledge, this is the first work to introduce EQA with situational queries, and also the first to use a generative approach for query creation.

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