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Found 7 papers, 2 papers with code
Position Offset Label Prediction for Grammatical Error Correction
We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task.
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?
To this end, we explore several ensemble strategies based on strong PLMs with four sophisticated single models.
An Error-Guided Correction Model for Chinese Spelling Error Correction
By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying the correct tokens in generating.
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction
To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence.
A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation
The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer.
A Soft Label Strategy for Target-Level Sentiment Classification
In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word.
A Simple Dual-decoder Model for Generating Response with Sentiment
How to generate human like response is one of the most challenging tasks for artificial intelligence.
