2 code implementations • 4 Jan 2024 • Songbo Hu, Xiaobin Wang, Zhangdie Yuan, Anna Korhonen, Ivan Vulić
We present DIALIGHT, a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems which facilitates systematic evaluations and comparisons between ToD systems using fine-tuning of Pretrained Language Models (PLMs) and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs).
no code implementations • 25 Dec 2023 • Shirong Ma, Shen Huang, Shulin Huang, Xiaobin Wang, Yangning Li, Hai-Tao Zheng, Pengjun Xie, Fei Huang, Yong Jiang
Experimental results demonstrate the effectiveness of continual pre-training of E-commerce LLMs and the efficacy of our devised data mixing strategy.
no code implementations • 18 Nov 2023 • Nanping Luo, Xiaobin Wang, Shenghong Gu, Antti Penttilä, Karri Muinonen, Yisi Liu
For the aim of analysis of the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST.
1 code implementation • 21 Aug 2023 • Tianyu Yu, Chengyue Jiang, Chao Lou, Shen Huang, Xiaobin Wang, Wei Liu, Jiong Cai, Yangning Li, Yinghui Li, Kewei Tu, Hai-Tao Zheng, Ningyu Zhang, Pengjun Xie, Fei Huang, Yong Jiang
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
1 code implementation • 14 Aug 2023 • Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, Yong Jiang
EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews.
no code implementations • 31 May 2023 • Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Hai-Tao Zheng, Zhiyuan Liu, Pengjun Xie
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning.
1 code implementation • 20 Feb 2023 • Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, Yong Jiang, Wenjuan Han
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
1 code implementation • 18 Dec 2022 • Chengyue Jiang, Wenyang Hui, Yong Jiang, Xiaobin Wang, Pengjun Xie, Kewei Tu
We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing.
Ranked #2 on Entity Typing on Open Entity
no code implementations • 10 Nov 2022 • Ning Ding, Yulin Chen, Ganqu Cui, Xiaobin Wang, Hai-Tao Zheng, Zhiyuan Liu, Pengjun Xie
Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere.
1 code implementation • 27 Sep 2022 • Shen Huang, Yuchen Zhai, Xinwei Long, Yong Jiang, Xiaobin Wang, Yin Zhang, Pengjun Xie
Speech Entity Linking aims to recognize and disambiguate named entities in spoken languages.
1 code implementation • COLING 2022 • Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
1 code implementation • ACL 2022 • Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, Min Zhang
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
1 code implementation • ACL 2022 • Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on Nested Named Entity Recognition on GENIA
Chinese Named Entity Recognition named-entity-recognition +5
1 code implementation • SemEval (NAACL) 2022 • Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, Yong Jiang
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition Named Entity Recognition +1
no code implementations • 21 Feb 2022 • Shuqing Shi, Xiaobin Wang, Zhiyou Yang, Fan Zhang, Hong Qu
This algorithm achieves a total regret bound of $\tilde{\mathcal{O}}(D\sqrt{SAT})$in time horizon $T$ with $S$ states, $A$ actions and diameter $D$.
1 code implementation • 17 Feb 2022 • Boli Chen, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Meishan Zhang, Fei Huang
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 29 Sep 2021 • Ning Ding, Yulin Chen, Xiaobin Wang, Hai-Tao Zheng, Zhiyuan Liu, Pengjun Xie
A big prototype could be effectively modeled by two sets of learnable parameters, one is the center of the hypersphere, which is an embedding with the same dimension of training examples.
7 code implementations • ACL 2021 • Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Hai-Tao Zheng, Zhiyuan Liu
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Ranked #5 on Named Entity Recognition (NER) on Few-NERD (SUP)
1 code implementation • ICLR 2021 • Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, Rui Zhang
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
1 code implementation • ACL 2020 • Ning Ding, Dingkun Long, Guangwei Xu, Muhua Zhu, Pengjun Xie, Xiaobin Wang, Hai-Tao Zheng
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
no code implementations • SEMEVAL 2019 • Xiaobin Wang, Chunping Ma, Huafei Zheng, Chu Liu, Pengjun Xie, Linlin Li, Luo Si
This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019.
no code implementations • 25 Jan 2019 • Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria
In this paper we present a method for estimating the distribution of an outcome given a binary exposure that is subject to underreporting.