Global Textual Relation Embedding for Relational Understanding

Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at https://github.com/czyssrs/GloREPlus

PDF Abstract ACL 2019 PDF ACL 2019 Abstract

Datasets


Introduced in the Paper:

GloREPlus

Used in the Paper:

Kinetics

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Action Classification Kinetics-400 GloRe Acc@1 76.1 # 110

Methods


No methods listed for this paper. Add relevant methods here