We present the SemEval 2019 shared task on UCCA parsing in English, German
and French, and discuss the participating systems and results. UCCA is a
cross-linguistically applicable framework for semantic representation, which
builds on extensive typological work and supports rapid annotation. UCCA poses
a challenge for existing parsing techniques, as it exhibits reentrancy
(resulting in DAG structures), discontinuous structures and non-terminal nodes
corresponding to complex semantic units. The shared task has yielded
improvements over the state-of-the-art baseline in all languages and settings.
Full results can be found in the task's website
https://competitions.codalab.org/competitions/19160.

介绍 SemEval 2019 有关英语、德语和法语 UCCA 解析的任务和参与系统和结果。

SemEval-2019 任务 1：使用 UCCA 进行跨语言语义分析

SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

We announce a shared task on UCCA parsing in English, German and French, and
call for participants to submit their systems. UCCA is a cross-linguistically
applicable framework for semantic representation, which builds on extensive
typological work and supports rapid annotation. UCCA poses a challenge for
existing parsing techniques, as it exhibits reentrancy (resulting in DAG
structures), discontinuous structures and non-terminal nodes corresponding to
complex semantic units. Given the success of recent semantic parsing shared
tasks (on SDP and AMR), we expect the task to have a significant contribution
to the advancement of UCCA parsing in particular, and semantic parsing in
general. Furthermore, existing applications for semantic evaluation that are
based on UCCA will greatly benefit from better automatic methods for UCCA
parsing. The competition website is
this https URL

本文宣布发起一项横跨英语、德语和法语的 UCCA 解析共享任务，目的是为构建一种跨语言框架的语义表示，并通过挑战已有的解析技术推动该领域的发展，进而使基于 UCCA 的自然语言处理应用受益。

SemEval 2019 共享任务：基于 UCCA 的跨语言语义分析 - 参与征集

SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA -  Call for Participation

The ability to consolidate information of different types is at the core of
intelligence, and has tremendous practical value in allowing learning for one
task to benefit from generalizations learned for others. In this paper we
tackle the challenging task of improving semantic parsing performance, taking
UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD)
parsing as auxiliary tasks. We experiment on three languages, using a uniform
transition-based system and learning architecture for all parsing tasks.
Despite notable conceptual, formal and domain differences, we show that
multitask learning significantly improves UCCA parsing in both in-domain and
out-of-domain settings.

本文针对语义解析的性能进行改进，使用多任务学习方法，将 UCCA 解析作为测试案例，辅助任务包括 AMR 解析、SDP 解析和 Universal Dependencies 解析。实验表明，多任务学习显著提高了 UCCA 解析的性能。