33 citations · 128 across the 39 of their papers we have counts for
40 papers
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…
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Zhangyin Feng, Yong Dai, Fan Zhang +6
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper p…
Soft Language Clustering for Multilingual Model Pre-training
Jiali Zeng, Yufan Jiang, Yongjing Yin +5
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language…
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
Shaoxiang Wu, Damai Dai, Ziwei Qin +4
Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. Howeve…
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
Zefan Cai, Xin Zheng, Tianyu Liu +7
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the…
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing
Linzheng Chai, Dongling Xiao, Jian Yang +5
Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implic…
Enhancing Continual Relation Extraction via Classifier Decomposition
Heming Xia, Peiyi Wang, Tianyu Liu +3
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements hav…
Large Language Models are not Fair Evaluators
Peiyi Wang, Lei Li, Liang Chen +7
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of respo…
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Shoujie Tong, Heming Xia, Damai Dai +5
Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and…
Finding Similar Exercises in Retrieval Manner
Tongwen Huang, Xihua Li, Chao Yi +2
When students make a mistake in an exercise, they can consolidate it by ``similar exercises'' which have the same concepts, purposes and methods. Commonly, for a certain subject an…
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
He Zhu, Xihua Li, Xuemin Zhao +2
Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning material…
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Xin Zheng, Tianyu Liu, Haoran Meng +6
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or…
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Jiali Zeng, Yongjing Yin, Yufan Jiang +2
Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive…
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
Jiali Zeng, Yufan Jiang, Yongjing Yin +3
We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named…
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…
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
Haoran Meng, Zheng Xin, Tianyu Liu +6
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUS…
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…
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.…
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…
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Ef…
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…
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
Dongling Xiao, Linzheng Chai, Qian-Wen Zhang +3
Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history cont…