“本文介绍了我们在第二十二届中文计算语言学大会中文抽象语义表示解析评测任务中提交的参赛系统。中文抽象语义表示(Chinese Abstract Meaning Representa-tion,CAMR)不仅以图的方式表示句子的语义,还保证了概念对齐和关系对齐。近期,生成式大规模语言模型在诸多自然语言处理任务上展现了优秀的生成能力和泛化能力。受此启发,我们选择微调Baichuan-7B模型来以端到端的形式从文本直接生成序列化的CAMR。实验结果表明,我们的系统能够在不依赖于词性、依存句法信息以及复杂规则的前提下取得了同现有方法可比的性能。”

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model’s generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods compared to the counterfactual data augmentation and prompt-tuning methods.

To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of at- tack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models.