2019

2019, ACL data: TWITTER, WEIBO links: https://www.aclweb.org/anthology/N19-1163, https://github.com/DeepBrainAI/ERD task: Rumour Detection 这篇文章采用GRU编码社交媒体posts stream，作为环境的状态表示；训练一个分类器以GRU的状态输出为输入，对文本做二分类判断是否是rumor。用DQN训练agent，根据状态做出是否启动rumor分类器进行判断，并根据分类结果对错给予奖惩。目标就是尽可能准尽可能早地预测出社交媒体posts是否是rumor。 Focuses on the task of rumour detection; particularly, we are in- terested in understanding how early we can detect them. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum num- ber of posts required before we classify an event as a rumour. Let $E$ denote an event, and it consists of a series of relevant posts $x_i$, where $x_0$ denotes the source message and $x_T$ the last relevant message....

2019, ACL data: KBP37, SemEval 2010 Task 8, TACRED task: Entity and Relation Extraction Build task agnostic relation representations solely from entity-linked text. 缺陷 文章认为网页中, 相同的的实体对一般指代相同的实体关系, 把实体不同的构建为负样本. 这个在单份文件中可能大概率是对的. 但是实体不完全一直不代表这个两对实体的关系不同. 所以这个作为负样本是本质上映射的是实体识别而不是关系. 比较好的方式是把实体不同但是关系一样的也考虑进来. 方法 Define Relation Statement We define a relation statement to be a block of text containing two marked entities. From this, we create training data that contains relation statements in which the entities have been replaced with a special [BLANK]...

2019, ACL data: SemEval 2014, SemEval 2014 ABSA, SemEval 2015, SemEval 2016 task: ABSA propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. 优点： 用指针网络选取target，避免了序列标注的搜索空间过大问题 用span边界+极性的标注方式，解决多极性的target问题 方法 Input: sentence x =(x1,..., xn) with length n, Target list T = {t1,..., tm}： each target ti is annotated with its start, end position, and its sentiment polarity...