Øistein E. Andersen

Also published as: Øistein Andersen


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CEPOC: The Cambridge Exams Publishing Open Cloze dataset
Mariano Felice | Shiva Taslimipoor | Øistein E. Andersen | Paula Buttery
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Open cloze tests are a standard type of exercise where examinees must complete a text by filling in the gaps without any given options to choose from. This paper presents the Cambridge Exams Publishing Open Cloze (CEPOC) dataset, a collection of open cloze tests from world-renowned English language proficiency examinations. The tests in CEPOC have been expertly designed and validated using standard principles in language research and assessment. They are prepared for language learners at different proficiency levels and hence classified into different CEFR levels (A2, B1, B2, C1, C2). This resource can be a valuable testbed for various NLP tasks. We perform a complete set of experiments on three tasks: gap filling, gap prediction, and CEFR text classification. We implement transformer-based systems based on pre-trained language models to model each task and use our dataset as a test set, providing promising benchmark results.


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The BEA-2019 Shared Task on Grammatical Error Correction
Christopher Bryant | Mariano Felice | Øistein E. Andersen | Ted Briscoe
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write&Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F_0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.


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The Effect of Adding Authorship Knowledge in Automated Text Scoring
Meng Zhang | Xie Chen | Ronan Cummins | Øistein E. Andersen | Ted Briscoe
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance at a global level.


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Neural Sequence-Labelling Models for Grammatical Error Correction
Helen Yannakoudakis | Marek Rei | Øistein E. Andersen | Zheng Yuan
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.


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Grammatical error correction using hybrid systems and type filtering
Mariano Felice | Zheng Yuan | Øistein E. Andersen | Helen Yannakoudakis | Ekaterina Kochmar
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task


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Developing and testing a self-assessment and tutoring system
Øistein E. Andersen | Helen Yannakoudakis | Fiona Barker | Tim Parish
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications


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HOO 2012 Error Recognition and Correction Shared Task: Cambridge University Submission Report
Ekaterina Kochmar | Øistein Andersen | Ted Briscoe
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP


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The BNC Parsed with RASP4UIMA
Øistein E. Andersen | Julien Nioche | Ted Briscoe | John Carroll
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We have integrated the RASP system with the UIMA framework (RASP4UIMA) and used this to parse the XML-encoded version of the British National Corpus (BNC). All original annotation is preserved, and parsing information, mainly in the form of grammatical relations, is added in an XML format. A few specific adaptations of the system to give better results with the BNC are discussed briefly. The RASP4UIMA system is publicly available and can be used to parse other corpora or document collections, and the final parsed version of the BNC will be deposited with the Oxford Text Archive.