7 citations · 10 across the 5 of their papers we have counts for
5 papers
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
Xinnian Liang, Jing Li, Shuangzhi Wu +4
Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider ef…
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
Xinnian Liang, Shuangzhi Wu, Mu Li +1
Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) ha…
Task-guided Disentangled Tuning for Pretrained Language Models
Jiali Zeng, Yufan Jiang, Shuangzhi Wu +2
Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned on task-specific downstream datasets, which have produced state-of-the-art result…
Learning Confidence for Transformer-based Neural Machine Translation
Yu Lu, Jiali Zeng, Jiajun Zhang +2
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success. A well-calibrated confidence estimate enables accurate failure p…
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
Yufan Jiang, Shuangzhi Wu, Jing Gong +5
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. Due to task specific of MMRC, it is…