Commonsense norms are defeasible by context: reading books is usually great,
but not when driving a car. While contexts can be explicitly described in
language, in embodied scenarios, contexts are often provided visually. This
type of visually grounded reasoning about defeasible commonsense norms is
generally easy for humans, but (as we show) poses a challenge for machines, as
it necessitates both visual understanding and reasoning about commonsense
norms. We construct a new multimodal benchmark for studying visual-grounded
commonsense norms: NORMLENS. NORMLENS consists of 10K human judgments
accompanied by free-form explanations covering 2K multimodal situations, and
serves as a probe to address two questions: (1) to what extent can models align
with average human judgment? and (2) how well can models explain their
predicted judgments? We find that state-of-the-art model judgments and
explanations are not well-aligned with human annotation. Additionally, we
present a new approach to better align models with humans by distilling social
commonsense knowledge from large language models. The data and code are
released at this https URL

解读可废除文明规范的视觉基准下的常识法则对机器来说是一项挑战，并提出一种通过提取大型语言模型中的社会常识知识来改善模型与人类对齐的新方法。

阅读书籍很好，但驾车时不适合！关于非抗辩常识规范的视觉确定性推理

Reading Books is Great, But Not if You Are Driving! Visually Grounded  Reasoning about Defeasible Commonsense Norms

Conversational Causal Emotion Entailment aims to detect causal utterances for
a non-neutral targeted utterance from a conversation. In this work, we build
conversations as graphs to overcome implicit contextual modelling of the
original entailment style. Following the previous work, we further introduce
the emotion information into graphs. Emotion information can markedly promote
the detection of causal utterances whose emotion is the same as the targeted
utterance. However, it is still hard to detect causal utterances with different
emotions, especially neutral ones. The reason is that models are limited in
reasoning causal clues and passing them between utterances. To alleviate this
problem, we introduce social commonsense knowledge (CSK) and propose a
Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two
utterances. As not all CSK is emotionally suitable for utterances, we therefore
propose a sentiment-realized knowledge selecting strategy to filter CSK. To
process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph
networks. Experimental results show that our method outperforms baselines and
infers more causes with different emotions from the targeted utterance.

本文提出一种通过将情感信息与社会常识知识引入图中，构建知识增强会话图（KEC）来增强会话因果推断的方法。实验证明，该方法可以更好地从目标言语中推断出不同情感的原因。