Bi-directional Attention with Agreement for Dependency Parsing
We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsing procedure for each direction is formulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use of {\it soft} headword embeddings, allowing the model to implicitly capture high-order parsing history without dramatically increasing the computational complexity. We conduct experiments on English, Chinese, and 12 other languages from the CoNLL 2006 shared task, showing that the proposed model achieves state-of-the-art unlabeled attachment scores on 6 languages.
Code (1)
Tasks
Dependency ParsingSimilar Papers 제목 키워드 기반
Bidirectional Transition-Based Dependency Parsing
Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a …
Dependency ParsingSentenceTransition-Based Dependency ParsingPotsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing
On the Challenges of Fully Incremental Neural Dependency Parsing
Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language pr…
Dependency ParsingSentenceGraph-based Dependency Parsing with Bidirectional LSTM
Syntactic Substitutability as Unsupervised Dependency Syntax
Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. In this work, we explore the hypothesis that syntactic dependencies can be represented in language model at…
Dependency ParsingLanguage ModelingLanguage Modelling