Some of the major limitations identified in the areas of argument mining,
argument generation, and natural language argument analysis are related to the
complexity of annotating argumentatively rich data, the limited size of these
corpora, and the constraints that represent the different languages and domains
in which these data is annotated. To address these limitations, in this paper
we present the following contributions: (i) an effective methodology for the
automatic generation of natural language arguments in different topics and
languages, (ii) the largest publicly available corpus of natural language
argumentation schemes, and (iii) a set of solid baselines and fine-tuned models
for the automatic identification of argumentation schemes.

本文提出解决论证挖掘、论证生成和自然语言论证分析领域中的主要限制的方法，包括自动生成不同主题和语言中自然语言论证的有效方法、最大的公开可用自然语言论证方案语料库以及用于自动识别论证方案的可靠基准和微调模型。

NLAS-multi：多语言自动生成自然语言辩证方案的语料库

NLAS-multi: A Multilingual Corpus of Automatically Generated Natural  Language Argumentation Schemes

Several quality dimensions of natural language arguments have been
investigated. Some are likely to be reflected in linguistic features (e.g., an
argument's arrangement), whereas others depend on context (e.g., relevance) or
topic knowledge (e.g., acceptability). In this paper, we study the intrinsic
computational assessment of 15 dimensions, i.e., only learning from an
argument's text. In systematic experiments with eight feature types on an
existing corpus, we observe moderate but significant learning success for most
dimensions. Rhetorical quality seems hardest to assess, and subjectivity
features turn out strong, although length bias in the corpus impedes full
validity. We also find that human assessors differ more clearly to each other
than to our approach.

本文研究自然语言争辩的质量维度及其依赖的语言特征、上下文和主题知识等方面，并通过实验验证了计算机自主评估质量维度的有效性和局限性。