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20192022
most citedA Hierarchy of Graph Neural Networks Based on Learnable Local Features

7 citations · 20 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CL20221 cited

Inducing and Using Alignments for Transition-based AMR Parsing

Andrew Drozdov, Jiawei Zhou, Radu Florian +4

Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a compl…

cs.CL2021

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

Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo +3

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing b…

cs.CL20213 cited

AMR Parsing with Action-Pointer Transformer

Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo +1

Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are sema…

cs.CL2020

Improving Non-autoregressive Neural Machine Translation with Monolingual Data

Jiawei Zhou, Phillip Keung

Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. Under this framework, we leverage large monolingua…

cs.CL2019

Simple Unsupervised Summarization by Contextual Matching

Jiawei Zhou, Alexander M. Rush

We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the…