Search Results for author: Young-suk Lee

Found 12 papers, 4 papers with code

Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

no code implementations EMNLP 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-suk Lee, Radu Florian, Salim Roukos

We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.

AMR Parsing Fine-tuning

Bootstrapping Multilingual AMR with Contextual Word Alignments

no code implementations EACL 2021 Janaki Sheth, Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.

Multilingual Word Embeddings Word Alignment

Pushing the Limits of AMR Parsing with Self-Learning

no code implementations Findings of the Association for Computational Linguistics 2020 Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.

 Ranked #1 on AMR Parsing on LDC2014T12 (F1 Full metric)

AMR Parsing Machine Translation +3

Nonparametric Deconvolution Models

1 code implementation17 Mar 2020 Allison J. B. Chaney, Archit Verma, Young-suk Lee, Barbara E. Engelhardt

This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors.

Variational Inference

Language Independent Dependency to Constituent Tree Conversion

no code implementations COLING 2016 Young-suk Lee, Zhiguo Wang

We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages.

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