Error Propagation

Many sequence labeling tasks in NLP require solving a cascade of segmentation and tag- ging subtasks, such as Chinese POS tagging, named entity recognition, and so on. Tradi- tional pipeline …...

We evaluate several popular models of local discourse coherence for domain and task gen- erality by applying them to chat disentangle- ment. Using experiments on synthetic multi- party conversations, we …...

AbstractWe propose a new approach to PoS tagging where in a first step, we assign a coarse-grained tag corresponding to the main syntactic category. Based on this high-precision decision, in …...

AbstractError propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In …...

AbstractMedical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error …...

AbstractRelational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including …...

AbstractExisting studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, …...