资源分类
》 资源 》论文》Generating Logical Forms from Graph Representations of Text and Entities

Generating Logical Forms from Graph Representations of Text and Entities

2019-09-19 | |  120 |   48 |   0 0 0
Abstract Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the stateof-the-art across several tasks without pretraining, and outperforms existing approaches when combined with BERT pre-training.

上一篇：Few-Shot Representation Learning for Out-Of-Vocabulary Words

下一篇：Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...