Negation Triplet Extraction with Syntactic Dependency and Semantic
  Consistency
Yuchen Shi Deqing Yang Jingping Liu Yanghua Xiao Zongyu Wang Huimin Xu
Previous works of negation understanding mainly focus on negation cue
detection and scope resolution, without identifying negation subject which is
also significant to the downstream tasks. In this paper, we propose a new
negation triplet extraction (NTE) task which aims to extract negation subject
along with negation cue and scope. To achieve NTE, we devise a novel
Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is
built based on a generative pretrained language model (PLM) {of Encoder-Decoder
architecture} with a multi-task learning framework. Specifically, the given
sentence's syntactic dependency tree is incorporated into the PLM's encoder to
discover the correlations between the negation subject, cue and scope.
Moreover, the semantic consistency between the sentence and the extracted
triplet is ensured by an auxiliary task learning. Furthermore, we have
constructed a high-quality Chinese dataset NegComment based on the users'
reviews from the real-world platform of Meituan, upon which our evaluations
show that SSENE achieves the best NTE performance compared to the baselines.
Our ablation and case studies also demonstrate that incorporating the syntactic
information helps the PLM's recognize the distant dependency between the
subject and cue, and the auxiliary task learning is helpful to extract the
negation triplets with more semantic consistency.