10 citations · 24 across the 19 of their papers we have counts for
19 papers
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
Menglong Lu, Zhen Huang, Yunxiang Zhao +3
Self-training emerges as an important research line on domain adaptation. By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the…
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification
Menglong Lu, Zhen Huang, Zhiliang Tian +3
Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. Self-training generates ps…
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
Qingyue Wang, Yanhe Fu, Yanan Cao +3
Recently, large language models (LLMs), such as GPT-4, stand out remarkable conversational abilities, enabling them to engage in dynamic and contextually relevant dialogues across…
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
Peijie Dong, Lujun Li, Zimian Wei +3
Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate tr…
Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain
Chunxi Guo, Zhiliang Tian, Jintao Tang +4
Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has…
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng, Qihuang Zhong, Liang Ding +4
Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effect…
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval
Chunxi Guo, Zhiliang Tian, Jintao Tang +4
Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database. Large language models (LLMs) work we…
Hashtag-Guided Low-Resource Tweet Classification
Shizhe Diao, Sedrick Scott Keh, Liangming Pan +3
Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous.…
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Peijie Dong, Xin Niu, Zhiliang Tian +5
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field. Conventi…
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search (NAS) has made tremendous progress in the automatic design of effective neural network structures but suffers from a heavy computational burden. One-shot…
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer
Kangchen Zhu, Zhiliang Tian, Ruifeng Luo +1
Text style transfer aims to alter the style of a sentence while preserving its content. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and of…
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian +6
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed…
Semi-Supervised Lifelong Language Learning
Yingxiu Zhao, Yinhe Zheng, Bowen Yu +6
Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when learning tasks sequentially. However, existing lifelong language learning methods only foc…
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model
Dongkyu Lee, Zhiliang Tian, Yingxiu Zhao +2
In knowledge distillation, a student model is trained with supervisions from both knowledge from a teacher and observations drawn from a training data distribution. Knowledge of a…
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao +5
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available. Optimization-based meta-learning algorithms ach…
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization
Dongkyu Lee, Zhiliang Tian, Lanqing Xue +1
Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation tha…
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks
Zhiliang Tian, Wei Bi, Zihan Zhang +3
Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker pr…
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Yu Cao, Liang Ding, Zhiliang Tian +1
Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore…
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
Zhiliang Tian, Wei Bi, Dongkyu Lee +4
Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing…