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16 papers · 2 filters
Uncertainty-Aware Label Refinement for Sequence Labeling
Tao Gui, Jiacheng Ye, Qi Zhang +4
Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks. However, the local label dependencies and inefficient Viterbi decoding have alw…
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Shining Liang, Linjun Shou, Jian Pei +3
Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC).…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
AutoADR: Automatic Model Design for Ad Relevance
Yiren Chen, Yaming Yang, Hong Sun +7
Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, th…
Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset
Xie Chen, Yu Wu, Zhenghao Wang +2
Recently, Transformer based end-to-end models have achieved great success in many areas including speech recognition. However, compared to LSTM models, the heavy computational cost…
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
Zhihan Zhang, Xiubo Geng, Tao Qin +2
Procedural text describes dynamic state changes during a step-by-step natural process (e.g., photosynthesis). In this work, we focus on the task of procedural text understanding, w…