Prior work has commonly defined argument retrieval from heterogeneous
document collections as a sentence-level classification task. Consequently,
argument retrieval suffers both from low recall and from sentence segmentation
errors making it difficult for humans and machines to consume the arguments. In
this work, we argue that the task should be performed on a more fine-grained
level of sequence labeling. For this, we define the task as Argument Unit
Recognition and Classification (AURC). We present a dataset of arguments from
heterogeneous sources annotated as spans of tokens within a sentence, as well
as with a corresponding stance. We show that and how such difficult argument
annotations can be effectively collected through crowdsourcing with high
interannotator agreement. The new benchmark, AURC-8, contains up to 15% more
arguments per topic as compared to annotations on the sentence level. We
identify a number of methods targeted at AURC sequence labeling, achieving
close to human performance on known domains. Further analysis also reveals
that, contrary to previous approaches, our methods are more robust against
sentence segmentation errors. We publicly release our code and the AURC-8
dataset.

本文提出了 Argument Unit Recognition and Classification (AURC) 任务，通过 fine-grained sequence labeling 的方法识别不同来源中的 argument，并通过众包的方式进行标注，得到了 AURC-8 数据集，并发现相较于基于句子级别的标注方法，使用本文标注方法能够增加 15% 的 argument 数量。作者提出的方法比以往的方法更加鲁棒，并公开了代码和 AURC-8 数据集。