Aarne Talman

Language Technology Researcher

Aarne I am a language technology researcher and AI consultant with expertise in language understanding, reasoning, and large language models (LLM). Currently, I work as a Data & AI Strategy consultant at Accenture, where I leverage my 20 years of experience in research, software engineering, consulting, and leadership to drive technological advancements. In addition to my role at Accenture, I hold the position of Visiting Scholar in Language Technology at the University of Helsinki.

My research primarily centres around natural language understanding, reasoning, and natural language inference, employing machine learning techniques to address these challenges. I am particularly fascinated by the intricacies of language comprehension, the development of AI models to represent it, and the methodologies for evaluating these models.

Throughout my career, I have made contributions to the field of natural language processing and language technology. Notably, I have developed production-grade machine learning models that have had a substantial impact, being employed by millions of end users in diverse applications such as machine translation, speech recognition, and natural language understanding. These real-world applications have allowed me to bridge the gap between research and practical, scalable solutions.

My educational background includes a PhD in Language Technology from University of Helsinki, an MSc in Computational Linguistics and Formal Grammar from King's College London, which I completed in 2007, and a BSc in Philosophy from the London School of Economics, obtained in 2005.

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News

Papers

  1. Risto Luukkonen, Jonathan Burdge, Elaine Zosa, Aarne Talman, Ville Komulainen, Väinö Hatanpää, Peter Sarlin, Sampo Pyysalo. 2024. Poro 34B and the Blessing of Multilinguality. arXiv. [bibtex] [pdf] [model and code]
  2. Jussi Karlgren, Luise Dürlich, Evangelia Gogoulou, Liane Guillou, Joakim Nivre, Magnus Sahlgren, Aarne Talman. 2024. ELOQUENT CLEF shared tasks for evaluation of generative language model quality. Advances in Information Retrieval. ECIR 2024. [bibtex]
  3. Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann. 2023. Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa). [bibtex] [pdf] [code]
  4. Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann. 2022. How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets. Proceedings of The 11th Joint Conference on Lexical and Computational Semantics (*SEM). [bibtex] [pdf] [data and code]
  5. Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann. 2021. NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance. Proceedings of NoDaLiDa 2021. [bibtex] [pdf] [data and code]
  6. Aarne Talman, Antti Suni, Hande Celikkanat, Sofoklis Kakouros, Jörg Tiedemann and Martti Vainio. 2019. Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations. Proceedings of NoDaLiDa 2019. [bibtex] [pdf] [data and code]
  7. Aarne Talman, Umut Sulubacak, Raúl Vázquez, Yves Scherrer, Sami Virpioja, Alessandro Raganato, Arvi Hurskainen, and Jörg Tiedemann. 2019. The University of Helsinki submissions to the WMT19 news translation task. Proceedings of the Fourth Conference on Machine Translation: Shared Task Papers. [bibtex] [pdf]
  8. Aarne Talman and Stergios Chatzikyriakidis. 2019. Testing the Generalization Power of Neural Network Models Across NLI Benchmarks. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. [bibtex] [pdf]
  9. Aarne Talman, Anssi Yli-Jyrä and Jörg Tiedemann. 2019. Sentence Embeddings in NLI with Iterative Refinement Encoders. Natural Language Engineering 25(4). [bibtex] [pdf] [code]

Theses

Curriculum Vitae

Download the full CV [pdf]

Education

Employment