The aim of inductive temporal link prediction is to forecast future edges associated with nodes unseen during training, which is a crucial task in the field of temporal network analysis. Existing methods mainly make predictions by learning from ...

Few-shot relation classification (RC) is a crucial task that aims to identify the relationships between entity pairs with limited mentions. However, this task is challenging due to the insufficient amount of annotated data. Augmenting the quantity ...

Sentence semantic equivalence identification (SSEI) targets to measure the semantic equivalence between two sentences. To supplement limited supervision, existing methods extensively employ contrastive learning to obtain sentence semantics. ...

• The first to propose such a novel concept of pairwise contrastive learning for SSEI.
• An enhanced augmentation combined with a pair mix-up strategy for PairContrast.
• Experiments on two benchmarks to verify PairContrast’s superiority ...

Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires ...

• We apply a prompt-tuning-based method for few-shot event classification, which does not require abundantly seen classes.
• We design a task-specific template initializing strategy that can take all input factors into consideration.
• ...

Event classification (EC) aims to assign the event labels to unlabeled sentences and tends to struggle in real-world applications when only a few annotated samples are available. Previous studies have mainly focused on using meta-learning to ...

• We designed a taxonomy-aware event classification framework for overcoming the classification bottleneck brought by insufficient data volume.
• We apply a prompt-based method for few-shot event classification, which does not require ...

Job-resume matching (JRM) is the core of online recruitment services for predicting the matching degree between a job post and a resume. Most of the existing methods for JRM achieve a promising performance by simplifying this task as a matching between ...

C larification Question Generation (CQG) aims to automatically generate clarification questions to avoid misunderstanding. In this paper, we focus on generating clarification questions in the scenario of ambiguous multi-turn conversation, which ...

Answer selection focuses on selecting the correct answer for a question. Most previous work on answer selection achieves good performance by employing an RNN, which processes all question and answer sentences with the same feature extractor regardless ...