Dr Jey Han Lau

  • Room: Level: 03 Room: 25
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Natural Language Processing (Deep Learning, Generation)

Personal webpage

people.eng.unimelb.edu.au/laujh

Biography

My research is in the intersection of machine learning and language, and I have a particular interest in language generation — tasks that require machine learning models to produce language, e.g. summarisation and story generation — and unsupervised learning — learning where supervision signal is not available. Prior to joining the University of Melbourne, I spent over 3.5 years as an industry scientist at IBM Research, developing solutions for clients in application domains from education to government. Parallel to this, I was active in research and collaborated with universities around the world, and in recognition of my scientific achievements I was awarded a research accomplishment prize by IBM in 2019.

Recent publications

  1. Zhou, K.; Shu, C.; Li, B.; Lau, JH. Early Rumour Detection. Proceedings of the 2019 Conference of the North. Association for Computational Linguistics. 2019. DOI: 10.18653/v1/n19-1163
  2. Baldwin, T.; Cohn, T.; Brooke, J.; Lau, JH.; Hammond, A. Deep-speare: A joint neural model of poetic language, meter and rhyme. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). ACL Anthology. 2018, Vol. 1, pp. 1948-1958.
  3. Zhang, WE.; Sheng, QZ.; Lau, JH.; Abebe, E.; Ruan, W. Duplicate Detection in Programming Question Answering Communities. ACM Transactions on Internet Technology. Association for Computing Machinery. 2018, Vol. 18, Issue 3. DOI: 10.1145/3169795
  4. Xu, S.; Bennett, A.; Hoogeveen, D.; Lau, JH.; Baldwin, T. Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata. Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text. Association for Computational Linguistics. 2018. DOI: 10.18653/v1/w18-6119
  5. Bernardy, JP.; Lappin, S.; Lau, JH. The influence of context on sentence acceptability judgements. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). 2018, Vol. 2, pp. 456-461.
  6. Bhatia, S.; Lau, JH.; Baldwin, T. Topic Intrusion for Automatic Topic Model Evaluation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2018. DOI: 10.18653/v1/d18-1098
  7. Bhatia, S.; Lau, JH.; Baldwin, T. An Automatic Approach for Document-level Topic Model Evaluation. Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017). Association for Computational Linguistics. 2017. DOI: 10.18653/v1/k17-1022
  8. Xu, Y.; Lau, JH.; Baldwin, T.; Cohn, T. Decoupling Encoder and Decoder Networks for Abstractive Document Summarization. Proceedings of the Workshop on Summarization and Summary Evaluation Across Source Types and Genres, MultiLing@EACL 2017, Valencia, Spain, April 3, 2017. Association for Computational Linguistics. 2017, pp. 7-11.
  9. Zhang, WE.; Sheng, QZ.; Lau, JH.; Abebe, E. Detecting Duplicate Posts in Programming QA Communities via Latent Semantics and Association Rules. Proceedings of the 26th International Conference on World Wide Web - WWW '17. ASSOC COMPUTING MACHINERY. 2017, pp. 1221-1229. DOI: 10.1145/3038912.3052701
  10. Lau, JH.; Chi, L.; Tran, K-N.; Cohn, T. End-to-end Network for Twitter Geolocation Prediction and Hashing. CoRR. ACL Anthology. 2017, Vol. abs/1710.04802, pp. 744-753.
  11. Aletras, N.; Baldwin, T.; Lau, JH.; Stevenson, M. Evaluating topic representations for exploring document collections. Journal of the Association for Information Science and Technology. Association for Information Science and Technology (ASIS&T). 2017, Vol. 68, Issue 1, pp. 154-167. DOI: 10.1002/asi.23574
  12. Lau, JH.; Clark, A.; Lappin, S. Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge. Cognitive Science. WILEY. 2017, Vol. 41, Issue 5, pp. 1202-1241. DOI: 10.1111/cogs.12414
  13. Sorodoc, I.; Lau, JH.; Aletras, N.; Baldwin, T. Multimodal topic labelling. 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference. Unknown. 2017, Vol. 2, pp. 701-706.
  14. Lau, JH.; Baldwin, T.; Cohn, T. Topically Driven Neural Language Model. CoRR. ACL Anthology. 2017, Vol. 1, pp. 355-365. DOI: 10.18653/v1/P17-1033
  15. Lau, JH.; Baldwin, T. The sensitivity of topic coherence evaluation to topic cardinality. 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference. The Association for Computational Linguistics. 2016, pp. 483-487.
  16. Lau, JH.; Clark, A.; Lappin, S. Unsupervised prediction of acceptability judgements. ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. 2015, Vol. 1, pp. 1618-1628.
  17. Cook, P.; Rundell, M.; Lau, JL.; Baldwin, T. Applying a Word-sense Induction System to the Automatic Extraction of Diverse Dictionary Examples. . EURAC Research. 2014, pp. 319-328.
  18. Lui, M.; Lau, JH.; Baldwin, T. Automatic Detection and Language Identification of Multilingual Documents. Transactions of the Association for Computational Linguistics. Association for Computational Linguistics. 2014, Vol. 2, pp. 27-40.
  19. Lau, JH.; Cook, P.; Mccarthy, D.; Gella, S.; Baldwin, T. Learning word sense distributions, detecting unattested senses and identifying novel senses using topic models. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference. Association for Computing Machinery (ACM). 2014, Vol. 1, pp. 259-270.
  20. Lau, JH.; Newman, D.; Baldwin, TJ. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. ACL Anthology. 2014, pp. 530-539.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile