Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing

by   Yuta Koreeda, et al.

This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of "abstraction" from input tokens. We proposed a unified encoder-to-biaffine network for all five frameworks, which effectively incorporates a shared encoder to extract rich input features, decoder networks to generate anchorless nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was ranked fifth with the macro-averaged MRP F1 score of 0.7604, and consistently outperformed the baseline unified transition-based MRP. Furthermore, post-evaluation experiments showed that we can boost the performance of the proposed system by incorporating multi-task learning, whereas the baseline could not. These imply efficacy of incorporating the biaffine network to the shared architecture for MRP and that learning heterogeneous meaning representations at once can boost the system performance.



There are no comments yet.


page 1

page 2

page 3

page 4


HUJI-KU at MRP 2020: Two Transition-based Neural Parsers

This paper describes the HUJI-KU system submission to the shared task on...

DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs

Discourse Representation Theory (DRT) is a formal account for representi...

ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task

We present a system description of our contribution to the CoNLL 2019 sh...

Cross-lingual Semantic Parsing

We introduce the task of cross-lingual semantic parsing: mapping content...

Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing

Abstract Meaning Representation (AMR) is a rooted, labeled, acyclic grap...

QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs

This paper presents our submission to the SardiStance 2020 shared task, ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.