1 code implementation • NAACL (BioNLP) 2021 • Zheng Yuan, Yijia Liu, Chuanqi Tan, Songfang Huang, Fei Huang
To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.
Ranked #1 on Named Entity Recognition (NER) on JNLPBA
2 code implementations • NAACL 2021 • Yuxuan Lai, Yijia Liu, Yansong Feng, Songfang Huang, Dongyan Zhao
Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations.
1 code implementation • ACL 2021 • Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.
no code implementations • 28 Sep 2020 • Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si
Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.
2 code implementations • ACL 2020 • Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, Ting Liu
In this paper, we explore the slot tagging with only a few labeled support sentences (a. k. a.
no code implementations • CONLL 2019 • Wanxiang Che, Longxu Dou, Yang Xu, Yuxuan Wang, Yijia Liu, Ting Liu
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.
Ranked #1 on UCCA Parsing on CoNLL 2019
1 code implementation • IJCNLP 2019 • Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu
In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages.
1 code implementation • IJCNLP 2019 • Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system.
Ranked #6 on Task-Oriented Dialogue Systems on KVRET
no code implementations • 20 Jun 2019 • Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu
It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
1 code implementation • EMNLP 2018 • Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Ranked #2 on AMR Parsing on LDC2014T12:
1 code implementation • CONLL 2018 • Wanxiang Che, Yijia Liu, Yuxuan Wang, Bo Zheng, Ting Liu
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on Dependency Parsing on Universal Dependencies
1 code implementation • COLING 2018 • Yutai Hou, Yijia Liu, Wanxiang Che, Ting Liu
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.
no code implementations • COLING 2018 • Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu
Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base.
Ranked #7 on Task-Oriented Dialogue Systems on KVRET
1 code implementation • ACL 2018 • Yijia Liu, Wanxiang Che, Huaipeng Zhao, Bing Qin, Ting Liu
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
1 code implementation • NAACL 2018 • Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, Noah A. Smith
Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD.
Ranked #2 on Dependency Parsing on Tweebank
1 code implementation • 19 Apr 2016 • Yijia Liu, Wanxiang Che, Jiang Guo, Bing Qin, Ting Liu
Many natural language processing (NLP) tasks can be generalized into segmentation problem.