Papers by Jan Alexandersson
HUMAN: Hierarchical Universal Modular ANnotator (2020.emnlp-demos)
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| Challenge: | HUMAN is a web-based annotation tool that covers a variety of annotation tasks on textual and image data. |
| Approach: | They propose a web-based annotation tool that covers a variety of annotation tasks on textual and image data. |
| Outcome: | HUMAN covers a variety of annotation tasks on textual and image data and uses an internal deterministic state machine to chain different tasks in an interdependent manner. |
The Metalogue Debate Trainee Corpus: Data Collection and Annotations (L18-1)
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Volha Petukhova, Andrei Malchanau, Youssef Oualil, Dietrich Klakow, Saturnino Luz, Fasih Haider, Nick Campbell, Dimitris Koryzis, Dimitris Spiliotopoulos, Pierre Albert, Nicklas Linz, Jan Alexandersson
| Challenge: | Argumentation is an important component of human intelligence and is used to train lawyers and citizens in legal domains. |
| Approach: | They describe the Metalogue Debate Trainee Corpus (DTC) which contains data on motion and speech capture devices and semantic annotations. |
| Outcome: | The metalogue Debate Trainee Corpus (DTC) was developed to facilitate the design of instructional and interactive models for the Virtual Debate Coach application. |
M3TCM: Multi-modal Multi-task Context Model for Utterance Classification in Motivational Interviews (2024.lrec-main)
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| Challenge: | Motivational interviews have two distinct roles, namely client and therapist . previous approaches did not fully incorporate all of these characteristics into utterance classification . |
| Approach: | They propose a multi-modal, multi-task context model for utterance classification that integrates text and speech as well as conversation context. |
| Outcome: | The proposed model outperforms the state-of-the-art in utterance classification on the AnnoMI dataset with a relative improvement of 20% for the client- and by 15% for therapist utterrance classification. |
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
| Approach: | They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls. |
| Outcome: | The proposed model improves classification performance in English and French separately. |