Few-shot Event Detection (FSED) is a meaningful task due to the limited labeled data and expensive manual labeling. Some prompt-based methods are used in FSED. However, these methods require large GPU memory due to the increased length of input tokens caused by concatenating prompts, as well as additional human effort for designing verbalizers. Moreover, they ignore instance and prompt biases arising from the confounding effects between prompts and texts. In this paper, we propose a prototype-based prompt-instance Interaction with causal Intervention (2xInter) model to conveniently utilize both prompts and verbalizers and effectively eliminate all biases. Specifically, 2xInter first presents a Prototype-based Prompt-Instance Interaction (PPII) module that applies an interactive approach for texts and prompts to reduce memory and regards class prototypes as verbalizers to avoid design costs. Next, 2xInter constructs a Structural Causal Model (SCM) to explain instance and prompt biases and designs a Double-View Causal Intervention (DVCI) module to eliminate these biases. Due to limited supervised information, DVCI devises a generation-based prompt adjustment for instance intervention and a Siamese network-based instance contrasting for prompt intervention. Finally, the experimental results show that 2xInter achieves state-of-the-art performance on RAMS and ACE datasets.

Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations comes with prices: they often result in a specialized model structure, which largely prohibits using large pretrained models in text-to-SQL. To address this problem, we propose RASAT: a Transformer seq2seq architecture augmented with relation-aware self-attention that could leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model effectively. Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario. Experimental results on three widely used text-to-SQL datasets, covering both single-turn and multi-turn scenarios, have shown that RASAT could achieve competitive results in all three benchmarks, achieving state-of-the-art execution accuracy (75.5% EX on Spider, 52.6% IEX on SParC, and 37.4% IEX on CoSQL).

Document-level biomedical relation extraction (Bio-DocuRE) is an important branch of biomedical text mining that aims to automatically extract all relation facts from the biomedical text. Since there are a considerable number of relations in biomedical documents that need to be judged by other existing relations, logical reasoning has become a research hotspot in the past two years. However, current models with reasoning are single-granularity only based on one element information, ignoring the complementary fact of different granularity reasoning information. In addition, obtaining rich document information is a prerequisite for logical reasoning, but most of the previous models cannot sufficiently utilize document information, which limits the reasoning ability of the model. In this paper, we propose a novel Bio-DocuRE model called FILR, based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning. Specifically, FILR presents a multi-dimensional information fusion module MDIF to extract sufficient global document information. Then FILR proposes a multi-granularity reasoning module MGLR to obtain rich inference information through the reasoning of both entity-pairs and mention-pairs. We evaluate our FILR model on two widely used biomedical corpora CDR and GDA. Experimental results show that FILR achieves state-of-the-art performance.

近年来,作为细粒度的属性级别情感分析在商业界和学术界受到越来越多的关注,其目的在于识别一个句子中多个属性词所对应的情感极性。目前,在解决属性级别情感分析问题的绝大多数工作都集中在注意力机制的设计上,以此突出上下文和属性词中不同词对于属性级别情感分析的贡献,同时使上下文和属性词之间相互关联。本文提出使用层次注意力机制和门机制处理属性级别情感分析任务,在得到属性词的隐藏状态之后,通过注意力机制得到属性词新的表示,然后利用属性词新的表示和注意力机制进一步得到上下文新的表示,层次注意力机制的设计使得上下文和属性词的表达更加准确;同时通过门机制选择对属性词而言上下文中有用的信息,以此丰富上下文的表达,在SemEval 2014 Task4和Twitter数据集上的实验结果表明本文提出模型的有效性。