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20182022
most citedEvaluating Explanation Methods for Neural Machine Translation

4 citations · 4 across the 4 of their papers we have counts for

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cs.CL2022

Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics

Jiannan Xiang, Huayang Li, Yahui Liu +4

Current practices in metric evaluation focus on one single dataset, e.g., Newstest dataset in each year's WMT Metrics Shared Task. However, in this paper, we qualitatively and quan…

cs.CL2022

Visualizing the Relationship Between Encoded Linguistic Information and Task Performance

Jiannan Xiang, Huayang Li, Defu Lian +3

Probing is popular to analyze whether linguistic information can be captured by a well-trained deep neural model, but it is hard to answer how the change of the encoded linguistic…

cs.CL2022

Exploring and Adapting Chinese GPT to Pinyin Input Method

Minghuan Tan, Yong Dai, Duyu Tang +5

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to levera…

cs.CL2020

On the Branching Bias of Syntax Extracted from Pre-trained Language Models

Huayang Li, Lemao Liu, Guoping Huang +1

Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing. However, this kind…

cs.CL20204 cited

Evaluating Explanation Methods for Neural Machine Translation

Jierui Li, Lemao Liu, Huayang Li +3

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error…

cs.CL2019

Regularized Context Gates on Transformer for Machine Translation

Xintong Li, Lemao Liu, Rui Wang +2

Context gates are effective to control the contributions from the source and target contexts in the recurrent neural network (RNN) based neural machine translation (NMT). However,…