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

10/12/2020
by   Ofir Arviv, et al.
0

This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the cross-framework and cross-lingual tracks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/11/2019

HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing

This paper describes a simple UCCA semantic graph parsing approach. The ...
10/03/2019

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

This paper describes the proposed system of the Hitachi team for the Cro...
12/29/2020

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

Discourse Representation Theory (DRT) is a formal account for representi...
06/05/2020

UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings

We present our contribution to the EvaLatin shared task, which is the fi...
08/27/2018

Parameter sharing between dependency parsers for related languages

Previous work has suggested that parameter sharing between transition-ba...
06/09/2021

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

Abstract Meaning Representation (AMR) is a rooted, labeled, acyclic grap...
This week in AI

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