15 citations · 27 across the 6 of their papers we have counts for
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cs.CL2021
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Ye Liu, Jian-Guo Zhang, Yao Wan +3
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbers…
cs.CL2021★ 8 cited
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Jianguo Zhang, Kazuma Hashimoto, Yao Wan +4
Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few…
cs.CL2021★ 4 cited
Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation
Ye Liu, Yao Wan, Jian-Guo Zhang +2
The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness when comparing with the autoregre…