Aarne Talman

PhD Student in Language Technology at University of Helsinki

I'm a PhD student in Language Technology at University of Helsinki. My research focuses on natural language understanding, natural language inference, computational semantics and deep learning. I work in Jörg Tiedemann's research group and the ERC & Academy of Finland funded project Found in Translation: Natural Language Understanding with Cross-Lingual Grounding (FoTran). The goal is to build language-independent abstract meaning representations by training neural networks with massively parallel multilingual datasets. Prior to my academic career, I worked in the industry for 12 years in various positions ranging from software development, product management and consulting to leading a consulting practice at a global management consultancy.

I'm also a co-founder and CEO of Basement AI, a natural language processing consultancy.


My research focuses on Natural Language Processing and Machine Learning.

Natural Language Semantics

I study computational models of natural language meaning - especially sentence-level meaning representations and natural language inference.

Multilingual NLP

I conduct research on representation learning of natural language meaning in a multilingual setting, utilizing multilingual sentence representations in various transfer learning tasks.

Neural Machine Translation

I develop machine translation models and neural machine translation systems.


I'm involved in two large-scale research projects.

FoTran: Found in Translation

Found in Translation: Natural Language Understanding with Cross-lingual Grounding is an ERC funded project led by Jörg Tiedemann. With this project, we propose a line of research that will focus on the development of novel data-driven models that can learn language-independent abstract meaning representations from indirect supervision provided by human translations covering a substantial proportion of the linguistic diversity in the world. A guiding principle is cross-lingual grounding, the effect of resolving ambiguities through translation. Eventually, this will lead to language-independent meaning representations and we will test our ideas with multilingual machine translation and tasks that require semantic reasoning and inference.

MeMad: Methods for Managing Audiovisual Data

MeMAD project provides novel methods for efficient re-use and re-purpose of multilingual audiovisual content. These methodologies revolutionize video management and digital storytelling in broadcasting and media production. We go far beyond the state-of-the-art automatic video description methods by making the machine learn from the human. The resulting description is thus not only a time-aligned semantic extraction of objects but makes use of the audio and recognizes action sequences. In the MeMad project my role is to study multimodal and document-level machine translation.


  1. 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. To appear in NoDaLiDa 2019.
  2. 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]
  3. 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]
  4. Aarne Talman, Anssi Yli-Jyrä and Jörg Tiedemann. 2019. Sentence Embeddings in NLI with Iterative Refinement Encoders. Natural Language Engineering. [bibtex, pdf, code]


  1. Neural Network models of NLI fail to capture the general notion of inference, 8 March 2019, CLASP Seminar, University of Gothenburg. [pdf]
  2. State-of-the-Art Natural Language Inference Systems Fail to Capture the Semantics of Inference, 25 October 2018, Research Seminar in Language Technology, University of Helsinki. [pdf]
  3. Natural Language Inference with Hierarchical BiLSTM’s, 28 September 2018, FoTran 2018. [pdf]
  4. Natural Language Inference - Another Triumph for Deep Learning?, 23 November 2017, Research Seminar in Language Technology, University of Helsinki. [pdf]




  • 2018 - present , Doctoral Candidate, Language Technology, University of Helsinki
    Working on computational semantics and natural language processing.
  • 2019 - present, Founder & CEO, Basement AI
    Basement AI is a Nordic artificial intelligence research lab and consulting company specializing in natural language processing and machine learning.
  • 2015 - 2018, Associate Director, Consulting, Gartner
    Market Analytics and business strategy consulting.
  • 2012 - 2015, Consultant, Accenture
    Technology Strategy consultant.
  • 2011 - 2012, Research Student, London School of Economics
    Research on the reliability of non-linear mathematical models used in economics and climate science.
  • 2009 - 2011, Product Manager, Nokia
    Product Manager with end-to-end responsibility for Nokia's Enterprise Search platform and service.
  • 2008 - 2009, Manager, Nokia
    Responsible for managing the development of architecture management and system design solutions and services in Nokia R&D.
  • 2006 - 2008, Systems Analyst, Tieto
    Software development for banking and insurance clients.
  • 2006 - 2006 (2 months), Software Developer, Valuatum
    Development of Valuatum's financial analysis solution.