Biography
Dr Ming Liu is an early career researcher who works on Natural Langauge Processing and Machine Learning. He proposed the "Learn to actively learn" approahch for active learning and developed a few text summarization models/pipelines (e.g. SummPip, SciSummPip), both of which are widely used in low-resource text generation settings. His reseach has attracted multiple grants, including 2022 Deakin MiniARC, 2023 ARC Linkage (LP220200746). Dr Ming has interest in solving real world text mining problems, paticularly in domain specific settings.
Read more on Ming's profileResearch interests
- Natural Langauge Processing
- Small Efficient Language Modelling
- Continual Learning
- Text Generation
- Adversarial Learning
- Scientific Text Mining
- Multimodality
- Conversational Systems
Teaching interests
- Machine Learning
- Natural Language Processing
- Deep Learning
- Applied Data Analysis
- Semi-structured Data Analasis
- Data Wrangling
Publications
Prototype-Guided Memory Replay for Continual Learning
Stella Ho, Ming Liu, Lan Du, Longxiang Gao, Yong Xiang
(2023), pp. 1-11, IEEE Transactions on Neural Networks and Learning Systems, Piscataway, N.J., C1
A graph empowered insider threat detection framework based on daily activities
W Hong, J Yin, M You, H Wang, J Cao, J Li, M Liu, C Man
(2023), Vol. 141, pp. 84-92, ISA Transactions, Amsterdam, The Netherlands, C1
A fault diagnosis algorithm for analog circuits based on self-attention mechanism deep learning
D Yang, J Wei, X Lin, M Liu, S Lu
(2023), Vol. 44, pp. 128-136, Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, Beijing, China, C1-1
Leveraging Natural Language Processing and Clinical Notes for Dementia Detection
Ming Liu, Richard Beare, Taya Collyer, Nadine Andrew, Velandai Srikanth
(2023), pp. 150-155, Clinical Natural Language Processing : Proceedings of the 5th Clinical Natural Language Processing Workshop, Toronto, Canada, E1-1
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
Xinzhe Li, Ming Liu
(2023), pp. 249-259, TrustNLP 2023 : Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing, Toronto, Canada, E1
DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language Models
M Liu, D Zhang, W Tan, H Zhang
(2023), pp. 491-496, BioNLP 2023 : Proceedings 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Task, Toronto, Canada, E1
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?
X Li, M Liu, S Gao
(2023), pp. 165-173, Proceedings of the Annual Meeting of the Association for Computational Linguistics, E1
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
X Li, M Liu, S Gao, W Buntine
(2023), Vol. 2023-August, pp. 6683-6691, IJCAI 2023 : Proceedings of the 32nd International Joint Conference on Artificial Intelligence, Macao, China, E1
An Empirical Study on Active Learning for Multi-label Text Classification
Mengqi Wang, Ming Liu
(2023), pp. 94-102, Proceedings of The Fourth Workshop on Insights from Negative Results in NLP, Dubrovnik, Croatia, E1
Mulan: A Multiple Residual Article-Wise Attention Network for Legal Judgment Prediction
Junyi Chen, Lan Du, Ming Liu, Xiabing Zhou
(2022), Vol. 21, pp. 1-15, ACM Transactions on Asian and Low-Resource Language Information Processing, New York, N.Y., C1
An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
Huan Koh, Jiaxin Ju, Ming Liu, Shirui Pan
(2022), pp. 1-39, ACM Computing Surveys, New York, N.Y., C1
Similarity Calculation via Passage-Level Event Connection Graph
M Liu, L Chen, Z Zheng
(2022), Vol. 12, pp. 9887-9887, Applied Sciences (Switzerland), C1
BiDKT: Deep Knowledge Tracing with BERT
W Tan, Y Jin, M Liu, H Zhang
(2022), Vol. 428, pp. 260-278, Ad Hoc Networks and Tools for IT, Virtual event, E1
Semi-supervised Continual Learning with Meta Self-training
S Ho, M Liu, L Du, Y Li, L Gao, S Gao
(2022), pp. 4024-4028, CIKM '22 : Proceedings of the 31st ACM International Conference on Information and Knowledge Management 2022, Atlanta, Ga., E1
Graph Intelligence Enhanced Bi-Channel Insider Threat Detection
W Hong, J Yin, M You, H Wang, J Cao, J Li, M Liu
(2022), Vol. 13787, pp. 86-102, NSS 2022 : Proceedings of the International Conference on Network and System Security 2022, Denarau Island, Fiji, E1
How Far are We from Robust Long Abstractive Summarization?
H Koh, J Ju, H Zhang, M Liu, S Pan
(2022), pp. 2682-2698, EMNLP 2022 : Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, UAE, E1
Y Jin, M Liu, Y Li, R Xu, L Du, L Gao, Y Xiang
(2021), Vol. 35, pp. 505-532, Data Mining and Knowledge Discovery, C1
Leveraging Information Bottleneck for Scientific Document Summarization
Jiaxin Ju, Ming Liu, Huan Koh, Yuan Jin, Lan Du, Shirui Pan
(2021), pp. 4091-4098, Findings of the Association for Computational Linguistics : EMNLP 2021, Punta Cana, The Dominican Republic & Online, E1
Neural Attention-Aware Hierarchical Topic Model
Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine
(2021), pp. 1042-1052, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, The Dominican Republic & Online, E1
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine
(2021), pp. 3334-3340, Findings of the Association for Computational Linguistics : EMNLP 2021, Punta Cana, The Dominican Republic & Online, E1
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts
Xinzhe Li, Ming Liu, Xingjun Ma, Longxiang Gao
(2021), pp. 138-148, ALTA 2021 : Proceedings of the 19th Workshop of the Australasian Language Technology Association, Online, E1
SummPip: unsupervised multi-document summarization with sentence graph compression
Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari
(2020), pp. 1949-1952, SIGIR 2020 : Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, China, E1
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan
(2020), pp. 318-327, EMNLP 2020 : Proceedings of the First Workshop on Scholarly Document Processing, Online, E1
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall
(2020), pp. 2935-2943, EMNLP 2020 : Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online, E1
Diva Baggio, Trisha Peel, Anton Peleg, Sharon Avery, Madhurima Prayaga, Michelle Foo, Gholamreza Haffari, Ming Liu, Christoph Bergmeir, Michelle Ananda-Rajah
(2019), Vol. 8, JOURNAL OF CLINICAL MEDICINE, Switzerland, C1-1
Learning how to active learn by dreaming
Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari
(2019), pp. 4091-4101, ACL 2019 : Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, E1
Learning how to actively learn: a deep imitation learning approach
Ming Liu, Wray Buntine, Gholamreza Haffari
(2018), Vol. 1, pp. 1874-1883, ACL 2018 : Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Vic., E1-1
Learning to actively learn neural machine translation
Ming Liu, Wray Buntine, Gholamreza Haffari
(2018), pp. 334-344, CoNLL 2018 : Proceedings of the 22nd Conference on Computational Natural Language Learning, Brussels, Belgium, E1-1
Leveraging linguistic resources for improving neural text classification
Ming Liu, Gholamreza Haffari, Wray Buntine, Michelle Ananda-Rajah
(2017), pp. 34-42, ALTA 2017 : Proceedings of the Australasian Language Technology Association Workshop 2017, Brisbane, Qld., E1-1
Learning cascaded latent variable models for biomedical text classification
Ming Liu, Gholamreza Haffari, Wray Buntine
(2016), Vol. 14, pp. 128-132, ALTA 2016 : Proceedings of the Australasian Language Technology Association Workshop 2016, Melbourne, Vic., E1-1
Funded Projects at Deakin
Australian Competitive Grants
Building resilience in at-risk rural communities through improving Media Communication on Climate Change Policies
A/Prof Xiao Liu, Dr Hilya Mudrika Arini, Dr Ming Liu, Dr Fitri Trapsilawati, A/Prof Chathu Ranaweera, A/Prof Kevin Lee, A/Prof Hassan Vally, Dr Anna Klas, Dr Adam Cardilini, Dr Yun Mulyani, Dr Arif Nurwidyantoro, Prof Catherine Bennett, Dr Yunita Sari, Dr Justin Lawson, Dr Gabi Mocatta
KONEKSI Australia-Indonesia Research Collaboration Grants
- 2023: $175,000
Industry and Other Funding
Large Language Models in Engineering.
Dr Shang Gao, Dr Ming Liu, Mr Xinzhe Li
Aurecon Australasia Pty Ltd
- 2024: $2,250
- 2023: $16,000
Supervisions
No completed student supervisions to report