“少样本命名实体识别旨在用少量的标注数据来识别命名实体。近年来受提示学习在少样本场景中表现良好性能的启发,本文探索了基于提示的少样本命名实体识别的方法。已有的基于提示学习的方法是通过列举所有可能的跨度来进行实体识别,这导致了计算成本高以及对实体边界信息未充分利用的问题。本文提出一种基于提示学习的两阶段框架TSP-Few,在不使用源域数据的情况下,进行少样本命名实体识别。第一阶段对种子跨度进行增强、过滤和扩展,其中种子增强模块能够让种子跨度捕获到更丰富的语义信息,种子过滤器能够减少大量的无关跨度,种子扩展模块能够充分利用实体的边界信息,为实体类型分类提供高质量的候选实体跨度。第二阶段利用提示学习方法预测候选跨度的相应类别。此外,为了缓解跨度检测阶段的错误累积,在实体分类阶段引入了负采样策略。跨度检测和实体类型分类任务的独立训练更容易在少样本情况下取得优异的性能。在三个基准数据集上的实验表明,与先进的方法相比,本文提出的方法在性能上有了进一步的提升,并且实验结果也表明了该文模型各个模块的有效性。”

“Extractive Question Answering (EQA) in the few-shot learning scenario is one of the most chal-lenging tasks of Machine Reading Comprehension (MRC). Some previous works employ exter-nal knowledge for data augmentation to improve the performance of few-shot extractive ques-tion answering. However, there are not always available external knowledge or language- anddomain-specific NLP tools to deal with external knowledge such as part-of-speech taggers, syn-tactic parsers, and named-entity recognizers. In this paper, we present a novel Plug-and-PlayData Augmentation Component (PPDAC) for the few-shot extractive question answering, whichincludes a paraphrase generator and a paraphrase selector. Specifically, we generate multipleparaphrases of the question in the (question, passage, answer) triples using the paraphrase gener-ator and then obtain highly similar statements via paraphrase selector to form more training datafor fine-tuning. Extensive experiments on multiple EQA datasets show that our proposed plug-and-play data augmentation component significantly improves question-answering performance,and consistently outperforms state-of-the-art approaches in few-shot settings by a large margin.”

Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.

Systematic Generalization refers to a learning algorithm’s ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects’ contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.