290 citations · 414 across the 24 of their papers we have counts for
25 papers
Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners
Haidong Yuan, Haokun Zhao, Wanshi Xu +4
Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. To address this widespread chal…
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction
Yuanzhen Luo, Qingyu Zhou, Feng Zhou
Keyphrase extraction (KPE) is an important task in Natural Language Processing for many scenarios, which aims to extract keyphrases that are present in a given document. Many exist…
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang +3
Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or s…
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Yinghui Li, Haojing Huang, Shirong Ma +5
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. Benefiting from their emergent abilities, LLMs have…
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Jingheng Ye, Yinghui Li, Qingyu Zhou +4
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possi…
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Lizhao Liu, Kunyang Lin, Shangxin Huang +4
Stroke is the basic element of Chinese character and stroke extraction has been an important and long-standing endeavor. Existing stroke extraction methods are often handcrafted an…
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Shirong Ma, Yinghui Li, Rongyi Sun +9
Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the…
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Yinghui Li, Shirong Ma, Qingyu Zhou +7
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal inform…
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
Yusen Zhang, Zhongli Li, Qingyu Zhou +5
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers. The OCR model easily gets con…
Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Ding Zhang, Yinghui Li, Qingyu Zhou +4
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to e…
Automatic Context Pattern Generation for Entity Set Expansion
Yinghui Li, Shulin Huang, Xinwei Zhang +6
Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) an…
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Shaopeng Lai, Qingyu Zhou, Jiali Zeng +4
Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performi…
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Yinghui Li, Qingyu Zhou, Yangning Li +7
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity. Recently, pre-trained language mo…
A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems
Rongyi Sun, Borun Chen, Qingyu Zhou +3
Existing text- and image-based multimodal dialogue systems use the traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has an utterance-level encoder to mode…
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Zhongli Li, Wenxuan Zhang, Chao Yan +4
Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives. Recent work shows that existing models memorize procedures from c…
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Peiyi Wang, Runxin Xu, Tianyu Liu +4
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce dom…
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Heng-Da Xu, Zhongli Li, Qingyu Zhou +5
Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language. Most of the Chinese spelling errors are misused semant…
Improving BERT with Syntax-aware Local Attention
Zhongli Li, Qingyu Zhou, Chao Li +2
Pre-trained Transformer-based neural language models, such as BERT, have achieved remarkable results on varieties of NLP tasks. Recent works have shown that attention-based models…
Dialogue Response Selection with Hierarchical Curriculum Learning
Yixuan Su, Deng Cai, Qingyu Zhou +6
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-wo…
Learning to Summarize Passages: Mining Passage-Summary Pairs from Wikipedia Revision Histories
Qingyu Zhou, Furu Wei, Ming Zhou
In this paper, we propose a method for automatically constructing a passage-to-summary dataset by mining the Wikipedia page revision histories. In particular, the method mines the…
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
Qingyu Zhou, Furu Wei, Ming Zhou
Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However,…
Neural Document Summarization by Jointly Learning to Score and Select Sentences
Qingyu Zhou, Nan Yang, Furu Wei +3
Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper…
Sequential Copying Networks
Qingyu Zhou, Nan Yang, Furu Wei +1
Copying mechanism shows effectiveness in sequence-to-sequence based neural network models for text generation tasks, such as abstractive sentence summarization and question generat…
Selective Encoding for Abstractive Sentence Summarization
Qingyu Zhou, Nan Yang, Furu Wei +1
We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate netwo…