Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.

Abstract

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parseto-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -2.204 on automatic and human evaluation respectively.

SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation

“…The participants declined to submit a system description paper. (Lampouras and Vlachos, 2017) This team's method is based on inverting previous work on transition-based parsers, and casts NLG from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a syntactic parse tree. It achieves this by employing a sequence of four classifiers, each focusing on a subset of the transition actions, and finally realizing the syntactic parse tree into the final sentence.…”

A Graph-to-Sequence Model for AMR-to-Text Generation

“…While initial work used statistical approaches (Flanigan et al, 2016b;Pourdamghani et al, 2016;Song et al, 2017;Lampouras and Vlachos, 2017;Mille et al, 2017;Gruzitis et al, 2017), recent research has demonstrated the success of deep learning, and in particular the sequence-to-sequence model (Sutskever et al, 2014), which has achieved the state-of-the-art results on AMR-to-text generation (Konstas et al, 2017). One limitation of sequence-to-sequence models, however, is that they require serialization of input AMR graphs, which adds to the challenge of representing graph structure information, especially when the graph is large.…”

Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language

“…In relation to generation methods from Abstract Meaning Representation, it was possible to highlight approaches based on machine translation (Pourdamghani et al, 2016;Ferreira et al, 2017), on transformation to intermediate representations (Lampouras and Vlachos, 2017;Mille et al, 2017), on deep learning models (Konstas et al, 2017;Song et al, 2018), and on rule extraction (from graphs and trees) (Song et al, 2016;Flanigan et al, 2016). Methods based on transformation into intermediate representations focused on transforming AMR graphs into simpler representations (usually dependency trees) and then using an appropriate surface realization system.…”