ACL-ARC dataset

The dataset used in the experiments on the paper "Modeling citation worthiness by using attention‑based bidirectional long short‑term memory networks and interpretable models"<br>For the pre-processing of the dataset, please refer to the paper Bonab et al., 2018 (http://doi.org/10.1145/3209978.3210162)<br>We downloaded a copy of that dataset, adjusted some fields. The data are stored in jsonl format (each row is an json object), we list a couple of rows as example:<br><code>{"cur_sent":"the nespole uses a client server architecture to allow a common user who is initially browsing through the web pages of a service provider on the internet to connect seamlessly to a human agent of the service provider who speaks another language and provides speech to speech translation service between the two parties","cur_scaled_len_features":{"type":1,"values":[0.06936542669584245,0.07202216066481995]},"cur_has_citation":1}</code><code> </code><br> <br><br>For the code using this dataset to modeling citation worthiness, please refer to https://github.com/sciosci/cite-worthiness<br><br><br><br><br>

本数据集用于论文《基于注意力双向长短期记忆网络与可解释模型的引文价值建模》中的实验。
关于该数据集的预处理步骤，请参阅Bonab等人2018年的论文（http://doi.org/10.1145/3209978.3210162）。
我们获取了该数据集的副本，并对部分字段进行了调整。数据以jsonl格式存储（每行均为一个JSON对象），以下列举若干行作为示例：
<code>{"cur_sent":"the nespole uses a client server architecture to allow a common user who is initially browsing through the web pages of a service provider on the internet to connect seamlessly to a human agent of the service provider who speaks another language and provides speech to speech translation service between the two parties","cur_scaled_len_features":{"type":1,"values":[0.06936542669584245,0.07202216066481995]},"cur_has_citation":1}</code>
<code> </code>

若需了解使用该数据集进行引文价值建模的代码实现，请参阅 https://github.com/sciosci/cite-worthiness

搜集汇总

背景与挑战

背景概述

ACL-ARC数据集是一个用于研究引用价值性建模的数据集，数据以jsonl格式存储，每行是一个json对象。该数据集来源于一篇使用注意力机制双向长短期记忆网络和可解释模型的论文，并对原始数据进行了调整。