Computer Laboratory

Alexander Kuhnle

I am a second-year PhD student supervised by Prof Ann Copestake in the NLIP group at the Computer Laboratory, University of Cambridge, and a member of Queens' College. I am originally from Germany, where I did two bachelor degrees at the Karlsruhe Institute of Technology, the BSc in Informatics and the BSc in Mathematics, before continuing to take the MPhil in Advanced Computer Science course at the Computer Laboratory in Cambridge.

My research focuses on the evaluation of multi-modal deep neural networks with respect to various symbolic/linguistic capabilities. Instead of real-world data, I work on a system which automatically generates artificial toy data by randomly sampling an internal world model representation. Currently, these micro-worlds consist of coloured shapes located on a plane (see an example of such a shape world to the right). Although simple, their structural complexity is still sufficient to generate a broad variety of interesting instances involving many aspects of language. In contrast to the classic image captioning task, a deep network here is asked to decide about the agreement of a given statement and the presented image. By controlling the content of training and test instances, I make sure that achieving good performance clearly indicates the learning of genuine concept understanding and generalisation abilities. Moreover, such a setup makes a more detailed investigation of the process and content of learning in a multi-modal deep neural net possible. As such, it constitutes a first step towards understanding and consequently justifying the reasoning behind deep learning semantics.

I am grateful for being funded by a Qualcomm Award Premium Research Studentship and an Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Studentship.

Publications and activities

Working papers

  • ShapeWorld: A new test methodology for multimodal language understanding. April 2017. Alexander Kuhnle and Ann Copestake. [arxiv] [github]


  • A proposition-based abstractive summariser. December 2016. Yimai Fang, Haoyue Zhu, Ewa Muszyńska, Alexander Kuhnle and Simone Teufel. Proceedings of the 26th International Conference on Computational Linguistics (COLING 2016), in Osaka (Japan). [pdf]
  • Evaluating multi-modal deep learning systems with micro-worlds. November 2016. Alexander Kuhnle and Ann Copestake. Cambridge Language Sciences Annual Symposium 2016, in Cambridge (United Kingdom). [abstract] [poster] [references] [slide]
  • Investigating the effect of controlled context choice in distributional semantics. August 2016. Alexander Kuhnle. ESSLLI Workshop on Distributional Semantics and Linguistic Theory (DSALT 2016), in Bolzano (Italy). [abstract] [poster]
  • Resources for building applications with Dependency Minimal Recursion Semantics. May 2016. Ann Copestake, Guy Emerson, Michael Wayne Goodman, Matic Horvat, Alexander Kuhnle and Ewa Muszyńska. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), in Portorož (Slovenia). [pdf] [github]


  • ShapeWorld: A new testbed and evaluation methodology for multimodal language understanding. [github] [proposal]
  • Pydmrs: A Python library for working with DMRS structures. Collaboration with Ann Copestake, Guy Emerson, Matic Horvat and Ewa Muszyńska. [github]


  • Evaluating multi-modal deep learning systems with micro-worlds. November 2016. Inaugural Postgraduate Studies Open Day, at the Computer Laboratory, University of Cambridge (United Kingdom). [slides]
  • GraphLang: A DMRS graph description language. June 2016. DELPH-IN Annual Meeting, at Stanford University (USA). [overview] [slides] (work in progress; part of the pydmrs library)



Alexander Kuhnle
Queens' College
Silver Street
Cambridge CB3 9ET
United Kingdom

aok25 (at)

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