Automatic Argument Quality Assessment - New Datasets and Methods
Automatic Argument Quality Assessment - New Datasets and Methods
复制标题
自动论证质量评估 - 新数据集和方法
DOI：
10.18653/v1/d19-1564
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
N. Slonim
中科院分区：
文献类型：
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作者：
Assaf Toledo;Shai Gretz;Edo Cohen;Roni Friedman;Elad Venezian;Dan Lahav;Michal Jacovi;R. Aharonov;N. Slonim
我们探讨的任务自动评估的论点质量。为此，我们积极收集了6.3k个参数，与之前检查的数据相比，超过了五倍。每一个论点都明确而仔细地说明了其质量。此外，14 k对参数被独立注释，识别每对参数中质量更高的参数。尽管任务固有的主观性质，这两个注释方案导致令人惊讶的一致结果。我们将标记的数据集发布给社区。此外，我们建议神经方法的基础上，最近发布的语言模型，参数排名以及参数对分类。在前一项任务中，我们的结果与最先进的方法相当;在后一项任务中，我们的结果明显优于早期的方法。
We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.
DOI：
10.18653/v1/e17-1017
发表时间：
2017-04
期刊：
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影响因子：
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作者：
Henning Wachsmuth;Nona Naderi;Yufang Hou;Yonatan Bilu;Vinodkumar Prabhakaran;Tim Alberdingk Thijm;Graeme Hirst;Benno Stein
通讯作者：
Henning Wachsmuth;Nona Naderi;Yufang Hou;Yonatan Bilu;Vinodkumar Prabhakaran;Tim Alberdingk Thijm;Graeme Hirst;Benno Stein