325 citations · 1.4k across the 89 of their papers we have counts for
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Robust Dialogue Utterance Rewriting as Sequence Tagging
Jie Hao, Linfeng Song, Liwei Wang +3
The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this t…
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Xuebo Liu, Longyue Wang, Derek F. Wong +3
Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effe…
Rethinking the Value of Transformer Components
Wenxuan Wang, Zhaopeng Tu
Transformer becomes the state-of-the-art translation model, while it is not well studied how each intermediate component contributes to the model performance, which poses significa…
Context-Aware Cross-Attention for Non-Autoregressive Translation
Liang Ding, Longyue Wang, Di Wu +2
Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence. However, due to the lack of target dependency modelli…
On the Sub-Layer Functionalities of Transformer Decoder
Yilin Yang, Longyue Wang, Shuming Shi +3
There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains…
On the Sparsity of Neural Machine Translation Models
Yong Wang, Longyue Wang, Victor O. K. Li +1
Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of comput…
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation
Wenxiang Jiao, Xing Wang, Shilin He +3
Large-scale training datasets lie at the core of the recent success of neural machine translation (NMT) models. However, the complex patterns and potential noises in the large-scal…
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation
Yongchang Hao, Shilin He, Wenxiang Jiao +3
Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy. The common practice to tackle the…
How Does Selective Mechanism Improve Self-Attention Networks?
Xinwei Geng, Longyue Wang, Xing Wang +3
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underly…
On the Inference Calibration of Neural Machine Translation
Shuo Wang, Zhaopeng Tu, Shuming Shi +1
Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer u…
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
Shilin He, Xing Wang, Shuming Shi +2
Machine translation (MT) systems translate text between different languages by automatically learning in-depth knowledge of bilingual lexicons, grammar and semantics from the train…