We propose an attention-based model that treats AMR parsing as sequence-to-graph
transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic …

It is intuitive that semantic representations can be useful for machine translation, mainly
because they can help in enforcing meaning preservation and handling data sparsity (many …

We propose a new end-to-end model that treats AMR parsing as a series of dual decisions
on the input sequence and the incrementally constructed graph. At each time step, our …

Neural models greatly outperform grammar-based models across many tasks in modern
computational linguistics. This raises the question of whether linguistic principles, such as …

In the literature, the research on abstract meaning representation (AMR) parsing is much
restricted by the size of human-curated dataset which is critical to build an AMR parser with …

We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for
Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics …

The goal of compositional generalization benchmarks is to evaluate how well models
generalize to new complex linguistic expressions. Existing benchmarks often focus on …

We show how the AM parser, a compositional semantic parser (Groschwitz et al., 2018) can
solve compositional generalization on the COGS dataset. It is the first semantic parser that …

We combine character-level and contextual language model representations to improve
performance on Discourse Representation Structure parsing. Character representations can …

Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major
evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings …