4 research outputs found
Saarland at MRP 2019: Compositional parsing across all graphbanks
We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL)
Graph-based broad-coverage semantic parsing
Many broad-coverage meaning representations can be characterized as directed graphs,
where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning
representation from a sentence. It is quite natural to adopt a graph-based approach for
parsing, where nodes are identified conditioning on the individual words, and edges
are labeled conditioning on the pairs of nodes. However, there are two issues with
applying this simple and interpretable graph-based approach for semantic parsing:
first, the anchoring of nodes to words can be implicit and non-injective in several
formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes
should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical;
second, graph-based parsers typically predict edge labels independent from each other.
Such an independence assumption, while being sensible from an algorithmic point of
view, could limit the expressiveness of statistical modeling. Consequently, it might fail
to capture the true distribution of semantic graphs.
In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to
model the implicit anchoring as a latent variable in a probabilistic model. We induce
such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation
(AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a
directed acyclic graph, where the anchoring of nodes to words is implicit and could be
many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats
the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our
graph-based parsers are parameterized by neural modules that require gradient-based
optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable
sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative
refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on
semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task
of predicting the predicate-argument structure. In particular, semantic roles between
the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming
strong factorized baseline models
Saarland at MRP 2019: Compositional parsing across all graphbanks
We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL)