Authors: N S Prashanth ([email protected]) Bruno Marchal ([email protected]) Narayanan Devadasan ([email protected]) Guy Kegels ([email protected]) Bart Criel ([email protected])

Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with large amount pseudodata. However, this approach requires time-consuming for GEC because size In study, we explore utility bidirectional and auto-regressive transformers (BART) as generic pretrained encoder-decoder GEC. With use GEC, can be eliminated. We find that monolingual multil...

Authors: N S Prashanth ([email protected]) Bruno Marchal ([email protected]) Jean Macq ([email protected]) Narayanan Devadasan ([email protected]) Guy Kegels ([email protected]) Bart Criel ([email protected])

lated this reduced highway need as 4.45 lane-miles (7), and crediting this saving to BART, the freeway-to-rail efficiency ratio changes from 25.2 to 25.1. Construction-energy is not the entire story, however. One must also consider operating-energy. The operating-energies of BART, buses, and cars are 4740, 2900, and 8310 Btu/PM, respectively, including both propulsion energy and a pro rata shar...

In recent studies, pre-trained models and pseudo data have been key factors in improving the performance of English grammatical error correction (GEC) task. However, few studies examined role Chinese GEC Therefore, we develop based on three models: BERT, T5, BART, then incorporate these with to determine best configuration for On natural language processing computing (NLPCC) 2018 shared task te...