2024

  • KV Aditya Srivatsa, Kaushal Kumar Maurya, and Ekaterina Kochmar (2024). Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing. Accepted to the 5th Workshop on Insights from Negative Results in NLP (at NAACL 2024) [paper] [github]
  • Kseniia Petukhova, Roman Kazakov, and Ekaterina Kochmar (2024). PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text? Accepted to SemEval 2024 [paper] [github]
  • Roman Kazakov, Kseniia Petukhova, and Ekaterina Kochmar (2024). PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations. Accepted to SemEval 2024 [paper] [github]
  • KV Aditya Srivatsa and Ekaterina Kochmar (2024). What Makes Math Word Problems Challenging for LLMs? Accepted to NAACL 2024 [paper] [github]
  • Yichen Huang and Ekaterina Kochmar (2024). REFeREE: A REference-FREE Model-Based Metric for Text Simplification. Accepted to LREC-COLING 2024 [paper] [github]
  • Sabina Elkins, Ekaterina Kochmar, Jackie Chi Kit Cheung, and Iulian Vlad Serban (2024). How Teachers Can Use Large Language Models and Bloom’s Taxonomy to Create Educational Quizzes. In Proceedings of the 14th Symposium on Educational Advances in Artificial Intelligence (EAAI-24) [paper]

2023

  • Joseph Marvin Imperial and Ekaterina Kochmar (2023). BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages. In Proceedings of EMNLP 2023 (main conference) [paper] [data]
  • Anaïs Tack, Ekaterina Kochmar, Zheng Yuan, Serge Bibauw, and Chris Piech (2023). The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) [paper]
  • Joseph Marvin Imperial and Ekaterina Kochmar (2023). Automatic Readability Assessment for Closely Related Languages. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023) [paper] [github]
  • Sabina Elkins, Ekaterina Kochmar, Iulian Vlad Serban, and Jackie Cheung (2023). How Useful are Educational Questions Generated by Large Language Models? In Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2023) [paper] [github]

[To the top]

2022

  • Ekaterina Kochmar (2022). Getting Started with Natural Language Processing. Manning Publications, ISBN 9781617296765. Available on Manning and Amazon. Book’s GitHub page
  • Devang Kulshreshtha, Muhammad Shayan, Robert Belfer, Siva Reddy, Iulian Vlad Serban, and Ekaterina Kochmar (2022). Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems. In Proceedings of the 11th International Conference on Prestigious Applications of Intelligent Systems (PAIS 2022) [paper]
  • Sabina Elkins, Robert Belfer, Ekaterina Kochmar, Iulian Vlad Serban, and Jackie Cheung (2022). Question Personalization in an Intelligent Tutoring System. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) [paper]
  • Robert Belfer, Ekaterina Kochmar, and Iulian Vlad Serban (2022). Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) [paper]

[To the top]

2021

  • Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, and Joelle Pineau (2021). Automated Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems. In International Journal of Artificial Intelligence in Education (IJAIED). A system based on this research is deployed by Korbit. [paper]
  • Rebecca Watson and Ekaterina Kochmar (2021). Read & Improve: A Novel Reading Tutoring System. In Proceedings of Educational Data Mining (EDM 2021) [paper]
  • Francois St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, and Ekaterina Kochmar. A Comparative Study of Learning Outcomes for Online Learning Platforms. In Proceedings of the 22nd International Conference on Artificial Intelligence in Education (AIED 2021) [paper]
  • Sian Gooding, Ekaterina Kochmar, Seid Muhie Yimam, and Chris Biemann (2021). Word Complexity is in the Eye of the Beholder. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT 2021) [paper]
  • Matt Grenander, Robert Belfer, Ekaterina Kochmar, Iulian Serban, François St-Hilaire, and Jackie Cheung (2021). Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems. In Proceedings of the 11th Symposium on Educational Advances in Artificial Intelligence (EAAI-21) [paper]

[To the top]

2020

  • Shiva Taslimipoor, Sara Bahaadini, and Ekaterina Kochmar (2020). MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models. In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons (MWE-LEX 2020, COLING 2020). The system presented in this paper ranked first in the open track of the shared task on semi-supervised identification of verbal multiword expressions across 14 languages. [paper]
  • Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung D. Vu, Robert Belfer, Joelle Pineau, Aaron Courville, Laurent Charlin, and Yoshua Bengio (2020). A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. In Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020) [paper]
  • Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, and Joelle Pineau (2020). Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System. In Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020). A system based on this research is deployed by Korbit. [paper]
  • Sian Gooding, Shiva Taslimipoor, and Ekaterina Kochmar (2020). Incorporating Multiword Expressions in Phrase Complexity Estimation. In Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI 2020) [paper]
  • Ekaterina Kochmar, Sian Gooding, and Matthew Shardlow (2020). Detecting Multiword Expression Type Helps Lexical Complexity Assessment. In Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) [paper] [data]
  • David Strohmaier, Sian Gooding, Shiva Taslimipoor, and Ekaterina Kochmar (2020). SeCoDa: Sense Complexity Dataset. In Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) [paper] [data]

[To the top]

2019

  • Sian Gooding and Ekaterina Kochmar (2019). Recursive Context-Aware Lexical Simplification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019) [paper] [code]
  • Sian Gooding and Ekaterina Kochmar (2019). Complex Word Identification as a Sequence Labelling Task. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) [paper] [code]
  • Sian Gooding, Ekaterina Kochmar, Advait Sarkar, and Alan Blackwell (2019). Comparative judgments are more consistent than binary classification for labelling word complexity. In Proceedings of the 13th Linguistic Annotation Workshop (LAW XIII, ACL 2019) [paper]
  • Menglin Xia, Ekaterina Kochmar, and Ted Briscoe (2019). Automatic learner summary assessment for reading comprehension. In Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) [paper] [data]

[To the top]

2018

  • Sian Gooding and Ekaterina Kochmar (2018). CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting. In Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications (BEA13, NAACL HLT 2018). This paper presents the winning submission to the shared task on Complex Word Identification. [paper] [code]

2017

  • Ekaterina Kochmar and Ekaterina Shutova (2017). Modelling semantic acquisition in second language learning. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA12, EMNLP 2017) [paper]
  • Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft (2017). Classification of Twitter Accounts into Automated Agents and Human Users. In Proceedings of the 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017). This research was featured in University of Cambridge research news, International Business Times and Scientific American. [paper]

[To the top]

2016

  • Aurelie Herbelot and Ekaterina Kochmar (2016). ‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English. In Proceedings of the 26th International Conference on Computational Linguistics (COLING 2016) [paper]
  • Ekaterina Kochmar and Ekaterina Shutova (2016). Cross-Lingual Lexico-Semantic Transfer in Language Learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016) [paper] [data]
  • Menglin Xia, Ekaterina Kochmar, and Ted Briscoe (2016). Text Readability Assessment for Second Language Learners. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications (BEA11, NAACL 2016). A system based on this research is deployed in Read & Improve [paper] [data]

[To the top]

2015

  • Ekaterina Kochmar (2015). Error Detection in Content Word Combinations. Technical report UCAM-CL- TR-886, Computer Laboratory, University of Cambridge, ISSN 1476-2986 [report]
  • Ekaterina Kochmar and Ted Briscoe (2015). Using Learner Data to Improve Error Correction in Adjective–Noun Combinations. In Proceedings of the 10th Workshop on Innovative Use of NLP for Building Educational Applications (BEA10, NAACL 2015) [paper]

2014

  • Ekaterina Kochmar and Ted Briscoe (2014). Detecting Learner Errors in the Choice of Content Words Using Compositional Distributional Semantics. In Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers (COLING 2014) [paper] [data]
  • Mariano Felice, Zheng Yuan, Øistein Andersen, Helen Yannakoudakis, and Ekaterina Kochmar (2014). Grammatical error correction using hybrid systems and type filtering. In Proceedings of the 17th Conference on Computational Natural Language Learning (CoNLL 2014): Shared Task. This paper presents the winning submission to the shared task on Grammatical Error Correction. [paper]

[To the top]

2013

  • Ekaterina Kochmar and Ted Briscoe (2013). Capturing Anomalies in the Choice of Content Words in Compositional Distributional Semantic Space. In Proceedings of the Recent Advances in Natural Language Processing (RANLP 2013) [paper] [data]

2012

  • Ekaterina Kochmar, Øistein Andersen, and Ted Briscoe (2012). HOO 2012 Error Recognition and Correction Shared Task: Cambridge University Submission Report. In Proceedings of the 7th Workshop on Innovative Use of NLP for Building Educational Applications (BEA7, NAACL-HLT 2012) [paper]

2011

  • Ekaterina Kochmar (2011). Identification of a Writer’s Native Language by Error Analysis. MPhil Dissertation, Computer Laboratory, University of Cambridge, UK [dissertation]

2010

  • Ekaterina Kochmar (2010). Ensemble-Based Learning for Morphological Analysis of German. MA Thesis, University of Tuebingen, Germany

[To the top]