Few-shot relation extraction (FSRE) can alleviate the data scarcity problem in relation extraction. However, FSRE models often suffer a significant decline in performance when adapting to new domains. To overcome this issue, many researchers have focused on domain adaption FSRE (DAFSRE). Nevertheless, existing approaches primarily concentrate on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain. Additionally, the lack of distinction between relations further restricts the model performance. In this paper, we propose the domain-aware and co-adaptive feature transformation approach to address these issues. Specifically, we introduce a domain-aware transformation module that leverages the target domain distribution features to guide the domain-aware feature transformations. This can enhance the model’s adaptability across domains, leading to improved target domain performance. Furthermore, we design co-adaptive prototypical networks to perform co-adaptive feature transformation through a transformer mechanism. This results in more robust and distinguishable relation prototypes. Experiments on DAFSRE benchmark datasets demonstrate the effectiveness of our method, which outperforms existing models and achieves state-of-the-art performance.

Over-correction is a critical problem in Chinese grammatical error correction (CGEC) task. Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system. However, these methods still require the output of several GEC systems and inevitably lead to reduced error recall. In this light, we propose the LM-Combiner, a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble. Specifically, we train the model on an over-correction dataset constructed through the proposed K-fold cross inference method, which allows it to directly generate filtered sentences by combining the original and the over-corrected text. In the inference stage, we directly take the original sentences and the output results of other systems as input and then obtain the filtered sentences through LM-Combiner. Experiments on the FCGEC dataset show that our proposed method effectively alleviates the over-correction of the original system (+18.2 Precision) while ensuring the error recall remains unchanged. Besides, we find that LM-Combiner still has a good rewriting performance even with small parameters and few training data, and thus can cost-effectively mitigate the over-correction of black-box GEC systems (e.g., ChatGPT).

“This paper introduces our system at CCL-2023 Task: Chinese Essay Fluency Evaluation (CEFE).The CEFE task aims to study the identification and correction of grammatical errors in primaryand middle school students’ test compositions. The evaluation has three tracks to examine therecognition of wrong sentence types, character-level error correction, and wrong sentence rewrit-ing. According to the task characteristics and data distribution of each track, we propose a token-level discriminative model based on sequence labeling for the multi-label classification task ofwrong sentences, an auto-encoder model based on edited labels for character-level error correc-tion and a seq2seq model obtained by pre-training on pseudo data and fine-tuning on labeleddata to solve the wrong sentence rewriting task. In the final evaluation results, the method weproposed won the first place in all three tracks according to the corresponding evaluation metrics.”