Jan Alexandersson


2024

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M3TCM: Multi-modal Multi-task Context Model for Utterance Classification in Motivational Interviews
Sayed Muddashir Hossain | Jan Alexandersson | Philipp Müller
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Accurate utterance classification in motivational interviews is crucial to automatically understand the quality and dynamics of client-therapist interaction, and it can serve as a key input for systems mediating such interactions. Motivational interviews exhibit three important characteristics. First, there are two distinct roles, namely client and therapist. Second, they are often highly emotionally charged, which can be expressed both in text and in prosody. Finally, context is of central importance to classify any given utterance. Previous works did not adequately incorporate all of these characteristics into utterance classification approaches for mental health dialogues. In contrast, we present M3TCM, a Multi-modal, Multi-task Context Model for utterance classification. Our approach for the first time employs multi-task learning to effectively model both joint and individual components of therapist and client behaviour. Furthermore, M3TCM integrates information from the text and speech modality as well as the conversation context. With our novel approach, we outperform the state of the art for utterance classification on the recently introduced AnnoMI dataset with a relative improvement of 20% for the client- and by 15% for therapist utterance classification. In extensive ablation studies, we quantify the improvement resulting from each contribution.

2022

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Generating Synthetic Clinical Speech Data through Simulated ASR Deletion Error
Hali Lindsay | Johannes Tröger | Mario Magued Mina | Philipp Müller | Nicklas Linz | Jan Alexandersson | Inez Ramakers
Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference

Training classification models on clinical speech is a time-saving and effective solution for many healthcare challenges, such as screening for Alzheimer’s Disease over the phone. One of the primary limiting factors of the success of artificial intelligence (AI) solutions is the amount of relevant data available. Clinical data is expensive to collect, not sufficient for large-scale machine learning or neural methods, and often not shareable between institutions due to data protection laws. With the increasing demand for AI in health systems, generating synthetic clinical data that maintains the nuance of underlying patient pathology is the next pressing task. Previous work has shown that automated evaluation of clinical speech tasks via automatic speech recognition (ASR) is comparable to manually annotated results in diagnostic scenarios even though ASR systems produce errors during the transcription process. In this work, we propose to generate synthetic clinical data by simulating ASR deletion errors on the transcript to produce additional data. We compare the synthetic data to the real data with traditional machine learning methods to test the feasibility of the proposed method. Using a dataset of 50 cognitively impaired and 50 control Dutch speakers, ten additional data points are synthetically generated for each subject, increasing the training size for 100 to 1000 training points. We find consistent and comparable performance of models trained on only synthetic data (AUC=0.77) to real data (AUC=0.77) in a variety of traditional machine learning scenarios. Additionally, linear models are not able to distinguish between real and synthetic data.

2020

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HUMAN: Hierarchical Universal Modular ANnotator
Moritz Wolf | Dana Ruiter | Ashwin Geet D’Sa | Liane Reiners | Jan Alexandersson | Dietrich Klakow
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.

2019

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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling
Kathleen C. Fraser | Nicklas Linz | Bai Li | Kristina Lundholm Fors | Frank Rudzicz | Alexandra König | Jan Alexandersson | Philippe Robert | Dimitrios Kokkinakis
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.

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Temporal Analysis of the Semantic Verbal Fluency Task in Persons with Subjective and Mild Cognitive Impairment
Nicklas Linz | Kristina Lundholm Fors | Hali Lindsay | Marie Eckerström | Jan Alexandersson | Dimitrios Kokkinakis
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person’s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.

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Automatic Data-Driven Approaches for Evaluating the Phonemic Verbal Fluency Task with Healthy Adults
Hali Lindsay | Nicklas Linz | Johannes Troeger | Jan Alexandersson
Proceedings of the 3rd International Conference on Natural Language and Speech Processing

2018

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The Metalogue Debate Trainee Corpus: Data Collection and Annotations
Volha Petukhova | Andrei Malchanau | Youssef Oualil | Dietrich Klakow | Saturnino Luz | Fasih Haider | Nick Campbell | Dimitris Koryzis | Dimitris Spiliotopoulos | Pierre Albert | Nicklas Linz | Jan Alexandersson
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Using Neural Word Embeddings in the Analysis of the Clinical Semantic Verbal Fluency Task
Nicklas Linz | Johannes Tröger | Jan Alexandersson | Alexandra König
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers

2015

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Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Ercan Altinsoy | Heidi Christensen | Peter Ljunglöf | François Portet | Frank Rudzicz
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

2014

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Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Dimitra Anastasiou | Cui Jian | Ani Nenkova | Rupal Patel | Frank Rudzicz | Annalu Waller | Desislava Zhekova
Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

2013

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Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Peter Ljunglöf | Kathleen F. McCoy | François Portet | Brian Roark | Frank Rudzicz | Michel Vacher
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

2012

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Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Peter Ljunglöf | Kathleen F. McCoy | Brian Roark | Annalu Waller
Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies

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ISO 24617-2: A semantically-based standard for dialogue annotation
Harry Bunt | Jan Alexandersson | Jae-Woong Choe | Alex Chengyu Fang | Koiti Hasida | Volha Petukhova | Andrei Popescu-Belis | David Traum
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper summarizes the latest, final version of ISO standard 24617-2 ``Semantic annotation framework, Part 2: Dialogue acts"""". Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of `ideal concrete syntax'. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.

2010

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Towards an ISO Standard for Dialogue Act Annotation
Harry Bunt | Jan Alexandersson | Jean Carletta | Jae-Woong Choe | Alex Chengyu Fang | Koiti Hasida | Kiyong Lee | Volha Petukhova | Andrei Popescu-Belis | Laurent Romary | Claudia Soria | David Traum
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes an ISO project which aims at developing a standard for annotating spoken and multimodal dialogue with semantic information concerning the communicative functions of utterances, the kind of semantic content they address, and their relations with what was said and done earlier in the dialogue. The project, ISO 24617-2 ""Semantic annotation framework, Part 2: Dialogue acts"", is currently at DIS stage. The proposed annotation schema distinguishes 9 orthogonal dimensions, allowing each functional segment in dialogue to have a function in each of these dimensions, thus accounting for the multifunctionality that utterances in dialogue often have. A number of core communicative functions is defined in the form of ISO data categories, available at http://semantic-annotation.uvt.nl/dialogue-acts/iso-datcats.pdf; they are divided into ""dimension-specific"" functions, which can be used only in a particular dimension, such as Turn Accept in the Turn Management dimension, and ""general-purpose"" functions, which can be used in any dimension, such as Inform and Request. An XML-based annotation language, ""DiAML"" is defined, with an abstract syntax, a semantics, and a concrete syntax.

2009

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Well-formed Default Unification in Non-deterministic Multiple Inheritance Hierarchies (short paper)
Christian Schulz | Jan Alexandersson | Tilman Becker
Proceedings of the Eight International Conference on Computational Semantics

2007

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A Comprehensive Disfluency Model for Multi-Party Interaction
Jana Besser | Jan Alexandersson
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2006

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Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Jan Alexandersson | Alistair Knott
Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue

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Proceedings of the Third Workshop on Scalable Natural Language Understanding
James Allen | Jan Alexandersson | Jerome Feldman | Robert Porzel
Proceedings of the Third Workshop on Scalable Natural Language Understanding

2004

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Ends-based Dialogue Processing
Jan Alexandersson | Tilman Becker | Ralf Engel | Markus Löckelt | Elsa Pecourt | Peter Poller | Norbert Pfleger | Norbert Reithinger
Proceedings of the 2nd International Workshop on Scalable Natural Language Understanding (ScaNaLU 2004) at HLT-NAACL 2004

2003

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Less is More: Using a single knowledge representation in dialogue systems
Iryna Gurevych | Robert Porzel | Elena Slinko | Norbert Pfleger | Jan Alexandersson | Stefan Merten
Proceedings of the HLT-NAACL 2003 Workshop on Text Meaning

2000

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Some Notes on the Complexity of Dialogues
Jan Alexandersson | Paul Heisterkamp
1st SIGdial Workshop on Discourse and Dialogue

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Multilingual Summary Generation in a Speech-To-Speech Translation System for Multilingual Dialogues
Jan Alexandersson | Peter Poller | Michael Kipp | Ralf Engel
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

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Summarizing Multilingual Spoken Negotiation Dialogues
Norbert Reithinger | Michael Kipp | Ralf Engel | Jan Alexandersson
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

1998

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Towards Multilingual Protocol Generation For Spontaneous Speech Dialogues
Jan Alexandersson | Peter Poller
Natural Language Generation

1997

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Insights into the Dialogue Processing of VERBMOBIL
Jan Alexandersson | Norbert Reithinger | Elisabeth Maier
Fifth Conference on Applied Natural Language Processing

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Clarification Dialogues as Measure to Increase Robustness in a Spoken Dialogue System
Elisabeth Maier | Norbert Reithinger | Jan Alexandersson
Interactive Spoken Dialog Systems: Bringing Speech and NLP Together in Real Applications

1995

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A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System
Jan Alexandersson | Elisabeth Maier | Norbert Reithinger
Seventh Conference of the European Chapter of the Association for Computational Linguistics