Existing conversational recommendation (CR) systems usually suffer from insufficient item
information when conducted on short dialogue history and unfamiliar items. Incorporating …

Few-shot relation classification is a natural language processing task that aims to enable
models to recognize new relational categories of query instances by training on base …

Few-shot relation extraction aims to identify the relation type between entities in a given text
in the low-resource scenario. Albeit much progress, existing meta-learning methods still fall …

Few-shot relation classification aims to classify the relation type between two given entities
in a sentence by training with a few labeled instances for each relation. However, most of …

Relation Classification is one of the most important tasks in text mining. Previous methods
either require large-scale manually-annotated data or rely on distant supervision …

Recent advances in relation extraction with deep neural architectures have achieved
excellent performance. However, current models still suffer from two main drawbacks: 1) …

This paper studies few-shot relation classification under domain shift, which is quite a
challenging inductive task in practice. Previous work focusing on few-shot relation …

Few-shot relation extraction (few-shot RE) aims to recognize relations between the entity
pair in a given text by utilizing very few annotated instances. As a simple yet efficient …

Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning
with merely a handful of annotated instances. Meta-learning has been widely adopted for …

Relation classification (RC) is a fundamental task to building knowledge graphs and
describing semantic formalization. It aims to classify a relation between the head and the tail …