Stack LSTM
Related Papers
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy in- ference …...
Transition-Based Dependency Parsing with Heuristic Backtracking
We introduce a novel approach to the decoding problem in transition-based parsing: heuris- tic backtracking. This algorithm uses a se- ries of partial parses on the sentence to locate the …...
AMR Parsing using Stack-LSTMs
AbstractWe present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we …...
In-Order Transition-based Constituent Parsing
AbstractBoth bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation …...
Stack-based Multi-layer Attention for Transition-based Dependency Parsing
AbstractAlthough sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a …...
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
AbstractOur work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we …...