Xin Cui


2023

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Self-Supervised Sentence Polishing by Adding Engaging Modifiers
Zhexin Zhang | Jian Guan | Xin Cui | Yu Ran | Bo Liu | Minlie Huang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Teachers often guide students to improve their essays by adding engaging modifiers to polish the sentences. In this work, we present the first study on automatic sentence polishing by adding modifiers. Since there is no available dataset for the new task, we first automatically construct a large number of parallel data by removing modifiers in the engaging sentences collected from public resources. Then we fine-tune LongLM to reconstruct the original sentences from the corrupted ones. Considering that much overlap between inputs and outputs may bias the model to completely copy the inputs, we split each source sentence into sub-sentences and only require the model to generate the modified sub-sentences. Furthermore, we design a retrieval augmentation algorithm to prompt the model to add suitable modifiers. Automatic and manual evaluation on the auto-constructed test set and real human texts show that our model can generate more engaging sentences with suitable modifiers than strong baselines while keeping fluency. We deploy the model at http://coai.cs.tsinghua.edu.cn/static/polishSent/. A demo video is available at https://youtu.be/Y6gFHOgSv8Y.

2021

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JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Pei Ke | Haozhe Ji | Yu Ran | Xin Cui | Liwei Wang | Linfeng Song | Xiaoyan Zhu | Minlie Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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BERT Enhanced Neural Machine Translation and Sequence Tagging Model for Chinese Grammatical Error Diagnosis
Deng Liang | Chen Zheng | Lei Guo | Xin Cui | Xiuzhang Xiong | Hengqiao Rong | Jinpeng Dong
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

This paper presents the UNIPUS-Flaubert team’s hybrid system for the NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). As a challenging NLP task, CGED has attracted increasing attention recently and has not yet fully benefited from the powerful pre-trained BERT-based models. We explore this by experimenting with three types of models. The position-tagging models and correction-tagging models are sequence tagging models fine-tuned on pre-trained BERT-based models, where the former focuses on detecting, positioning and classifying errors, and the latter aims at correcting errors. We also utilize rich representations from BERT-based models by transferring the BERT-fused models to the correction task, and further improve the performance by pre-training on a vast size of unsupervised synthetic data. To the best of our knowledge, we are the first to introduce and transfer the BERT-fused NMT model and sequence tagging model into the Chinese Grammatical Error Correction field. Our work achieved the second highest F1 score at the detecting errors, the best F1 score at correction top1 subtask and the second highest F1 score at correction top3 subtask.