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Department of Computer Science and Technology

  • Visiting Researcher

I am a researcher in Machine Learning and Natural Language Processing. My work is focused on improving machine learning architectures for representation learning, transfer learning, autoregressive modeling and multi-task optimization. Most of my research is applied in the area of Natural Language Understanding and on tasks that benefit from capturing the semantics in text, such as structured prediction, language modeling, grammatical error detection, sentiment analysis and text classification.

My main areas of interest include:

  • neural networks and deep learning models
  • transfer learning
  • representation learning
  • multi-task optimization
  • educational applications
  • distributional and compositional semantics
  • unsupervised and semi-supervised learning
  • (bio)medical applications of NLP
  • NLP applications for social and environmental benefit

Elements publications

Conference proceedings

2017

  • Farag, Y., Rei, M. and Briscoe, T., 2017. An Error-Oriented Approach to Word Embedding Pre-Training Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications,
  • Rei, M., 2017. Detecting Off-topic Responses to Visual Prompts
  • Rei, M. and Giannakoudaki, E., 2017. Auxiliary Objectives for Neural Error Detection Models
  • Giannakoudaki, E., Rei, M., Andersen, OE. and Yuan, Z., 2017. Neural Sequence-Labelling Models for Grammatical Error Correction Proceedings of the 2017 Conference on Empirical Methods in natural Language Processing, v. D17-1
    Doi: 10.18653/v1/D17-1297
  • Rei, M., Felice, M., Yuan, Z. and Briscoe, T., 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications,
  • 2016

  • Rei, M. and Cummins, R., 2016. Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays https://aclweb.org/anthology/volumes/proceedings-of-the-11th-workshop-on-innovative-use-of-nlp-for-building-educational-applications/,
    Doi: http://doi.org/10.18653/v1/W16-05
  • Alikaniotis, D., Yannakoudakis, H. and Rei, M., 2016. Automatic text scoring using neural networks 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, v. 2
    Doi: 10.18653/v1/p16-1068
  • Rei, M. and Yannakoudakis, H., 2016. Compositional sequence labeling models for error detection in learner writing 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, v. 2
  • 2015

  • Rei, M., 2015. Online representation learning in recurrent neural language models Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing,
  • 2014

  • Rei, M. and Briscoe, T., 2014. Parser lexicalisation through self-learning NAACL HLT 2013 - 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Main Conference,
  • Rei, M. and Briscoe, T., 2014. Looking for Hyponyms in Vector Space. CoNLL,
  • 2011

  • Rei, M. and Briscoe, T., 2011. Unsupervised Entailment Detection between Dependency Graph Fragments
  • 2010

  • Rei, M. and Briscoe, T., 2010. Combining manual rules and supervised learning for hedge cue and scope detection Proceedings of the Fourteenth Conference on Computational Natural Language Learning: Shared Task,
  • Rei, M. and Cao, K., A Joint Model for Word Embedding and Word Morphology
  • Rei, M., Crichton, G. and Pyysalo, S., Attending to characters in neural sequence labeling models
  • Rei, M., Semi-supervised Multitask Learning for Sequence Labeling
  • Rei, M., Bulat, LT., Kiela, D. and Shutova, E., Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
  • Rei, M. and Søgaard, A., Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens
  • Rei, M., Gerz, D. and Vulic, I., Scoring Lexical Entailment with a Supervised Directional Similarity Network
  • Stathopoulos, YA., Baker, S., Rei, M. and Teufel, S., Variable Typing: Assigning Meaning to Variables in Mathematical Text
  • Barrett, M., Bingel, J., Hollenstein, N., Rei, M. and Søgaard, A., Sequence classification with human attention
  • Rei, M. and Søgaard, A., Jointly Learning to Label Sentences and Tokens
  • Journal articles

    2017

  • Farag, Y., Rei, M. and Briscoe, T., 2017. An Error-Oriented Approach to Word Embedding Pre-Training. CoRR, v. abs/1707.06841
  • Rei, M., Felice, M., Yuan, Z. and Briscoe, T., 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns. CoRR, v. abs/1707.05236
  • 2011

  • Briscoe, T., Harrison, K., Naish, A., Parker, A., Rei, M., Siddharthan, A., Sinclair, D., Slater, M. and Watson, R., 2011. Intelligent Information Access from Scientific Papers Current Challenges in Patent Information Retrieval,
  • Theses / dissertations

    2013

  • Rei, M., 2013. Minimally supervised dependency-based methods for natural language processing
  • Contact Details

    Room: 
    GS22
    Email: 

    marek.rei@cl.cam.ac.uk