Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researchers. A common solution is to use textual features of relations (e.g., surface name or textual descriptions) as auxiliary information to bridge the gap between seen and unseen relations. Current approaches learn an embedding for each word token in the text. These methods lack robustness as they suffer from the out-of-vocabulary (OOV) problem. Meanwhile, models built on character n-grams have the capability of generating expressive representations for OOV words. Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP. Our approach works by first constructing a hierarchical n-gram graph on the surface name to model the organizational structure of n-grams that leads to the surface name. A GramTransformer, based on the Transformer is then presented to model the hierarchical n-gram graph to construct the relation embedding for ZSLP. Experimental results show the proposed HNZSLP achieved state-of-the-art performance on two ZSLP datasets.
Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
Institute of Computing and Intelligence, Harbin Institute of Technology（哈尔滨工业大学计算与智能学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Ant Group（蚂蚁集团）
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
Johns Hopkins University（约翰斯·霍普金斯大学）
最近的研究表明，多语言神经机器翻译（NMT）模型可以用来判断一个句子对同一语言中另一个句子的释义程度（Thompson and Post，2020）;然而，试图使用标准束搜索从这样的模型中生成释义会产生微不足道的副本或近似副本。我们引入了一个简单的释义生成算法，该算法不鼓励在输入中产生n元语法。我们的方法可以从一个单一的多语言NMT模型中生成多种语言的释义。此外，输入和输出之间的词汇多样性的量可以在生成时控制。我们进行了一次人工评估，将我们的方法与在大型英语合成释义数据库ParaBank 2上训练的释义器进行比较（Hu等人，2019 c），并发现我们的方法产生的释义，更好地保留意义，更符合语法，为同一水平的词汇多样性。其他较小的人类评估表明，我们的方法也适用于两种非英语语言。
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering
University of Southern California（南加州大学）；Amazon.com Inc（亚马逊公司）；City University of Hong Kong（香港城市大学）；Southern University of Science and Technology（南方科技大学）
