Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering

• URL: http://arxiv.org/abs/2304.03754v1
• Date: Fri, 7 Apr 2023 17:45:49 GMT
• Title: Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
• Authors: Hung-Ting Su, Yulei Niu, Xudong Lin, Winston H. Hsu, Shih-Fu Chang
• Abstract summary: Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. We propose a novel framework, Causal Knowledge Extraction from Language Models (CaKE-LM), leveraging causal commonsense knowledge from language models to tackle CVidQA. CaKE-LM significantly outperforms conventional methods by 4% to 6% of zero-shot CVidQA accuracy on NExT-QA and Causal-VidQA datasets.
• Score: 60.93164850492871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained question generation (QG) systems on reading comprehension datasets with text descriptions as inputs. However, QG models only learn to ask association questions (e.g., ``what is someone doing...'') and result in inferior performance due to the poor transfer of association knowledge to CVidQA, which focuses on causal questions like ``why is someone doing ...''. Observing this, we proposed to exploit causal knowledge to generate question-answer pairs, and proposed a novel framework, Causal Knowledge Extraction from Language Models (CaKE-LM), leveraging causal commonsense knowledge from language models to tackle CVidQA. To extract knowledge from LMs, CaKE-LM generates causal questions containing two events with one triggering another (e.g., ``score a goal'' triggers ``soccer player kicking ball'') by prompting LM with the action (soccer player kicking ball) to retrieve the intention (to score a goal). CaKE-LM significantly outperforms conventional methods by 4% to 6% of zero-shot CVidQA accuracy on NExT-QA and Causal-VidQA datasets. We also conduct comprehensive analyses and provide key findings for future research.

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