UCLA

Abstract Meaning Representation(AMR) parsing converts a natural language sentence into a specially designed semantic graph(AMR), which captures the most essential semantic entities and relations of the input sentence. While the recent introduction of pretrained sequence- to-sequence models have brought performance improvement and pipeline simplification, the problem of how to best encode structural information into seq2seq models remains. This exploratory work proposes joint training of transition-based AMR parsers that incorporates not only the parsing objective, but also a denoising objective into training; it seeks to answer whether the improved understanding of structural alignment can benefit sequence- to-sequence AMR parsers. It also shows potential application of the joint-trained models: the joint-training setup can greatly liberate the transition-based parsers from State Machine’s alignment constraints and allow them to be easily repurposed for a set of related tasks that could theoretically benefit from the structural training, such as paraphrase generation and generation from keywords.