论文标题
日本政治讨论中用于挖掘论证挖掘的级联模型：QA Lab-Poliinfo-3案例研究
A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Study
论文作者
论文摘要
RVRAIN团队解决了预算论点挖掘（BAM）任务，包括分类和信息检索子任务的组合。对于参数分类（AC），团队通过基于五级BERT的级联模型取得了最佳性能，并配有某些手工制作的规则。这些规则用于确定表达式是否为货币。然后，将每个货币表达归类为前提或级联模型第一级的结论。最后，每个前提都被归类为三个前提类别，并将每个结论分为两个结论类别。对于信息检索（即关系ID检测或RED），我们的最佳结果是通过基于BERT的二进制分类器的组合以及由货币表达和预算密集的嵌入组成的成对的余弦相似性来实现的。
The rVRAIN team tackled the Budget Argument Mining (BAM) task, consisting of a combination of classification and information retrieval sub-tasks. For the argument classification (AC), the team achieved its best performing results with a five-class BERT-based cascade model complemented with some handcrafted rules. The rules were used to determine if the expression was monetary or not. Then, each monetary expression was classified as a premise or as a conclusion in the first level of the cascade model. Finally, each premise was classified into the three premise classes, and each conclusion into the two conclusion classes. For the information retrieval (i.e., relation ID detection or RID), our best results were achieved by a combination of a BERT-based binary classifier, and the cosine similarity of pairs consisting of the monetary expression and budget dense embeddings.
