Log In Sign Up

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

by   Yuanhe Tian, et al.

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information. Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly. The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing. Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.


page 1

page 2

page 3

page 4


Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition

A collection of approaches based on graph convolutional networks have pr...

Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks

We study the problem of integrating syntactic information from constitue...

VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification

Much progress has been made recently on text classification with methods...

Business Entity Matching with Siamese Graph Convolutional Networks

Data integration has been studied extensively for decades and approached...

Relational Fusion Networks: Graph Convolutional Networks for Road Networks

The application of machine learning techniques in the setting of road ne...

Main Product Detection with Graph Networks for Fashion

Computer vision has established a foothold in the online fashion retail ...

A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation

Pun location is to identify the punning word (usually a word or a phrase...