Speculation and negation identification via unified Machine Reading Comprehension frameworks with lexical and syntactic data augmentation

Speculation and Negation Identification focuses on the extraction of speculative and negative cues and scopes. Previous work relied on complete syntactic trees or simply fed sentences into pre-trained language models, which were confronted with poor generalization within and across datasets, and the limitations of training samples. Accordingly, we build a complete pipeline framework that firstly detects cues and then extracts scopes, and propose unified Machine Reading Comprehension paradigms for both cue detection and scope resolution on several datasets. To tackle the insufficiency of training sets and produce more useful samples with appropriate amount of lexical and syntactic knowledge, we apply data augmentation integrating lexical and syntactic features for scope resolution. Experimental results show that our model achieves higher performance than baselines on several publicly accessible corpora.