The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset–VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.

Character-level BERT pre-trained in Chinese suffers a limitation of lacking lexicon information, which shows effectiveness for Chinese NER.To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semisupervised entity enhanced BERT pre-training method.In particular, we first extract an entity lexicon from the relevant raw text using a newword discovery method.We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the selfattention using a combination of character and entity representations.In addition, an entity classification task helps inject the entity information into model parameters in pre-training.The pre-trained models are used for NER finetuning.Experiments on a news dataset and two datasets annotated by ourselves for NER in long-text show that our method is highly effective and achieves the best results.

Qingkai Min, Yuefeng Shi, Yue Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.