no code implementations • SemEval (NAACL) 2022 • Ye Wang, Yanmeng Wang, Baishun Ling, Zexiang Liao, Shaojun Wang, Jing Xiao
This paper describes the second-placed system for subtask 2 and the ninth-placed system for subtask 1 in SemEval 2022 Task 4: Patronizing and Condescending Language Detection.
1 code implementation • 29 May 2025 • Yong Zhang, Yanwen Huang, Ning Cheng, Yang Guo, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external context, but retrieved passages are often lengthy, noisy, or exceed input limits.
no code implementations • 15 Mar 2025 • Chong Su, Yingbin Fu, Zheyuan Hu, Jing Yang, Param Hanji, Shaojun Wang, Xuan Zhao, Cengiz Öztireli, Fangcheng Zhong
We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins.
no code implementations • 2 Jan 2025 • Yanwen Huang, Yong Zhang, Ning Cheng, Zhitao Li, Shaojun Wang, Jing Xiao
Large language models (LLMs) often suffer from context faithfulness hallucinations, where outputs deviate from retrieved information due to insufficient context utilization and high output uncertainty.
no code implementations • 31 Dec 2024 • Kainan Liu, Yong Zhang, Ning Cheng, Zhitao Li, Shaojun Wang, Jing Xiao
Layer removal has emerged as a promising approach for compressing large language models (LLMs) by leveraging redundancy within layers to reduce model size and accelerate inference.
no code implementations • 22 Dec 2024 • Zhigen Li, Yanmeng Wang, Rizhao Fan, Ye Wang, Jianfeng Li, Shaojun Wang
LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data.
1 code implementation • 4 Jul 2024 • Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, Yuqian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
Conversational agents powered by Large Language Models (LLMs) show superior performance in various tasks.
no code implementations • Findings (EMNLP) 2021 • Yanmeng Wang, Jun Bai, Ye Wang, Jianfei Zhang, Wenge Rong, Zongcheng Ji, Shaojun Wang, Jing Xiao
To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage.
no code implementations • 8 Apr 2022 • Nick J. C. Wang, Shaojun Wang, Jing Xiao
In this paper, we compare different ways to combine ASR and NLU, in particular using a single Conformer model with different ways to use its components, to better understand the strengths and weaknesses of each approach.
no code implementations • 8 Apr 2022 • Nick J. C. Wang, Zongfeng Quan, Shaojun Wang, Jing Xiao
The Conformer model is an excellent architecture for speech recognition modeling that effectively utilizes the hybrid losses of connectionist temporal classification (CTC) and attention to train model parameters.
no code implementations • SEMEVAL 2021 • Ye Wang, Yanmeng Wang, Haijun Zhu, Bo Zeng, Zhenghong Hao, Shaojun Wang, Jing Xiao
This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning.
no code implementations • ACL 2021 • Li Huang, Junjie Li, Weiwei Jiang, ZhiYu Zhang, Minchuan Chen, Shaojun Wang, Jing Xiao
Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters.
Ranked #1 on
Chinese Spell Checking
on SIGHAN 2015
no code implementations • 1 Jan 2021 • Liming Deng, Long Wang, Binzhu WANG, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao
Controlling the presented forms (or structures) of generated text are as important as controlling the generated contents during neural text generation.
no code implementations • 1 Jan 2021 • Yan Cui, Xi Chen, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao
Embedding logical knowledge information into text generation is a challenging NLP task.
no code implementations • 1 Jul 2020 • Yan Wang, Jiayu Zhang, Jun Ma, Shaojun Wang, Jing Xiao
Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on.
Ranked #2 on
Emotion Recognition in Conversation
on DailyDialog
Emotion Classification
Emotion Recognition in Conversation
+1
no code implementations • 22 Mar 2020 • Zan Shen, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao
One-Shot methods have evolved into one of the most popular methods in Neural Architecture Search (NAS) due to weight sharing and single training of a supernet.
no code implementations • 29 Nov 2019 • Liming Deng, Jie Wang, Hangming Liang, Hui Chen, Zhiqiang Xie, Bojin Zhuang, Shaojun Wang, Jing Xiao
In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation.
no code implementations • 25 Sep 2019 • Xingyu Lou, Kaihe Xu, Zhongliang Li, Tian Xia, Shaojun Wang, Jing Xiao
Text generation is a critical and difficult natural language processing task.
no code implementations • 15 Sep 2019 • Tian Xia, Shaodan Zhai, Shaojun Wang
Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation.
no code implementations • 15 Sep 2019 • Tian Xia, Shaodan Zhai, Shaojun Wang
List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based.
no code implementations • 12 Sep 2019 • Tian Xia, Shaodan Zhai, Shaojun Wang
In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle.
no code implementations • 15 Jun 2019 • Haoshen Fan, Jie Wang, Bojin Zhuang, Shaojun Wang, Jing Xiao
In this paper, we comprehensively study on automatic generation of acrostic couplet with the first characters defined by users.
no code implementations • 15 Jun 2019 • Xu Lu, Jie Wang, Bojin Zhuang, Shaojun Wang, Jing Xiao
This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody.
no code implementations • 15 Jun 2019 • Haoshen Fan, Jie Wang, Bojin Zhuang, Shaojun Wang, Jing Xiao
In this paper, we comprehensively study on context-aware generation of Chinese song lyrics.
no code implementations • 27 Nov 2017 • Zhongliang Li, Raymond Kulhanek, Shaojun Wang, Yunxin Zhao, Shuang Wu
When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models.
no code implementations • JEPTALNRECITAL 2015 • Tian Xia, Shaodan Zhai, Zhongliang Li, Shaojun Wang
Marge infus{\'e} algorithmes d{\'e}tendus (MIRAS) dominent mod{\`e}le de tuning dans la traduction automatique statistique dans le cas des grandes caract{\'e}ristiques de l{'}{\'e}chelle, mais ils sont {\'e}galement c{\'e}l{\`e}bres pour la complexit{\'e} de mise en {\oe}uvre.
no code implementations • NeurIPS 2013 • Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training examples; once the training classification error is reduced to a local coordinatewise minimum, DirectBoost runs a greedy coordinate ascent algorithm that continuously adds weak classifiers to maximize any targeted arbitrarily defined margins until reaching a local coordinatewise maximum of the margins in a certain sense.
no code implementations • NeurIPS 2009 • Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori
We propose a novel information theoretic approach for semi-supervised learning of conditional random fields.