Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies

Abstract

To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.

TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning

“…structure), one can repurpose tree-based parsers for producing enhanced UD graphs. This category of approaches include packing the additional edges from an enhanced graph into the basic tree (Kanerva et al, 2020) and using either rule-based or learning-based approaches to convert a basic UD tree into an enhanced UD graph (Heinecke, 2020;Dehouck et al, 2020;Attardi et al, 2020;Ek and Bernardy, 2020). 2 • Graph-based: alternatively, one can directly focus on the enhanced UD graph with a semantic dependency graph parser that predicts the existence and label of each candidate dependency edge.…”

TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning

“…structure), one can repurpose tree-based parsers for producing enhanced UD graphs. This category of approaches include packing the additional edges from an enhanced graph into the basic tree (Kanerva et al, 2020) and using either rule-based or learning-based approaches to convert a basic UD tree into an enhanced UD graph (Heinecke, 2020;Dehouck et al, 2020;Attardi et al, 2020;Ek and Bernardy, 2020). 2 • Graph-based: alternatively, one can directly focus on the enhanced UD graph with a semantic dependency graph parser that predicts the existence and label of each candidate dependency edge.…”

Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

“…Several teams (Orange (Heinecke, 2020), FAST-PARSE (Dehouck et al, 2020), UNIPI (Attardi et al, 2020), CLASP (Ek and Bernardy, 2020), ADAPT (Barry et al, 2020)) concentrate on parsing into standard UD, and then add hand-written enhancement rules, sometimes in combination with data-driven heuristics to improve robustness. TurkuNLP (Kanerva et al, 2020) transforms EUD into a representation that is compatible with standard UD by combining multiple edges into a single edge with a complex label, and compiling edges involving empty nodes into complex edge labels (as is done by the evaluation script as well).…”