Causal knowledge extraction is the task of extracting relevant causes and
effects from text by detecting the causal relation. Although this task is
important for language understanding and knowledge discovery, recent works in
this domain have largely focused on binary classification of a text segment as
causal or non-causal. In this regard, we perform a thorough analysis of three
sequence tagging models for causal knowledge extraction and compare it with a
span based approach to causality extraction. Our experiments show that
embeddings from pre-trained language models (e.g. BERT) provide a significant
performance boost on this task compared to previous state-of-the-art models
with complex architectures. We observe that span based models perform better
than simple sequence tagging models based on BERT across all 4 data sets from
diverse domains with different types of cause-effect phrases.

从文本中提取相关原因和效果，检测因果关系的任务中，通过对三种序列标记模型进行彻底分析，并将其与基于跨度的因果性抽取方法进行比较，我们发现，与以往的复杂架构的最新模型相比，预训练语言模型（如 BERT）提供了显著的性能提升。我们观察到，基于跨度的模型在所有 4 个来自不同领域的数据集中，对于不同类型的因果短语表现更好。

跨领域评估因果知识提取方法

A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction

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.

提出了一种从语言模型中提取因果知识的框架 CaKE-LM，用于解决视频因果问答问题（CVidQA），并在 NExT-QA 和 Causal-VidQA 数据集上取得了显著的优势。