Sequence-to-sequence Models for Cache Transition Systems
Xiaochang Peng, Linfeng Song, Daniel Gildea, Giorgio Satta · 2018
In this paper, we present a sequenceto-sequence based approach for mapping natural language sentences to AMR semantic graphs.We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system.To address the sparsity issue of neural AMR parsing, we feed feature embeddings from the transition state to provide relevant local information for each decoder state.We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus.We evaluate our neural transition model on the AMR parsing task, and our parser outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models. 1