Pierre Lison

• Walker, Nicholas Thomas; Dahl, Torbjørn & Lison, Pierre (2022). Dialogue Management as Graph Transformations, . In Stoyanchev, Svetlana; Ultes, Stephan & Li, Haizhou (Ed.), Conversational AI for Natural Human-Centric Interaction . Springer. ISSN 978-981-19-5537-2.
• Papadopoulou, Anthi; Lison, Pierre; Øvrelid, Lilja & Pilán, Ildikó (2022). Bootstrapping Text Anonymization Models with Distant Supervision. In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of the 13th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. p. 4477–4487.
• Weitzenboeck, Emily Mary; Lison, Pierre; Cyndecka, Malgorzata Agnieszka & Langford, Malcolm (2022). The GDPR and Unstructured Data: Is Anonymisation Possible? . International Data Privacy Law (IDPL). ISSN 2044-3994. doi: 10.1093/idpl/ipac008. Full text in Research Archive Show summary

  Much of the legal and technical literature on data anonymization has focused on structured data such as tables. However, unstructured data such as text documents or images are far more common, and the legal requirements that must be fulfilled to properly anonymize such data formats remain unclear and underaddressed by the literature. In the absence of a definition of the term ‘anonymous data’ in the General Data Protection Regulation (GDPR), we examine its antithesis—personal data—and the identifiability test in Recital 26 GDPR to understand what conditions must be in place for the anonymization of unstructured data. This article examines the two contrasting approaches for determining identifiability that are prevalent today: (i) the risk-based approach and (ii) the strict approach in the Article 29 Working Party’s Opinion on Anonymization Techniques (WP 216). Through two case studies, we illustrate the challenges encountered when trying to anonymize unstructured datasets. We show that, while the risk-based approach offers a more nuanced test consistent with the purposes of the GDPR, the strict approach of WP 216 makes anonymization of unstructured data virtually impossible as long as the original data continues to exist. The concluding section considers the policy implications of the strict approach and technological developments that assist identification, and proposes a way forward.

• Olsen, Joakim; Næss, Arild Brandrud & Lison, Pierre (2021). Assessing the Quality of Human-Generated Summaries with Weakly Supervised Learning. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 112–123.
• Lison, Pierre; Barnes, Jeremy & Hubin, Aliaksandr (2021). skweak: Weak Supervision Made Easy for NLP. In Ji, Heng & Park, Jong (Ed.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. Association for Computational Linguistics. ISSN 978-1-954085-56-5. p. 337–346. doi: 10.18653/v1/2021.acl-demo.40. Full text in Research Archive
• Lison, Pierre; Pilán, Ildikó; Sánchez, David; Batet, Montserrat & Øvrelid, Lilja (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions. In Zong, Chengqing; Xia, Fei; Li, Wenjie & Navigli, Roberto (Ed.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics. ISSN 978-1-954085-52-7. p. 4188–4203. doi: 10.18653/v1/2021.acl-long.323.
• Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr & Touileb, Samia (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach . In Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. p. 1518–1533. Full text in Research Archive
• Jang, Youngsoo; Lee, Jongmin; Park, Jaeyoung; Lee, Kyeng-Hun; Lison, Pierre & Kee-Eung, Kim (2019). PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. In Huang, Ruihong & Padó, Sebastian (Ed.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), System Demonstrations . Association for Computational Linguistics. ISSN 9781950737925. p. 187–192. doi: 10.18653/v1/D19-3032.
• Lison, Pierre & Dogruöz, A. Seza (2018). Detecting Machine-translated Documents in Large Parallel Corpora. In Rapp, Reinhard; Zweigenbaum, Pierre & Sharoff, Serge (Ed.), 11th Workshop on Building and Using Comparable Corpora (BUCC 2018). European Language Resources Association. ISSN 979-10-95546-07-8. p. 25–32.
• Lison, Pierre; Tiedemann, Jörg & Kouylekov, Milen (2018). OpenSubtitles 2018: Statistical rescoring of sentence alignments in large, noisy parallel corpora. In Calzolari, Nicoletta; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Hasida, Koiti; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios & Tokunaga, Takenobu (Ed.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation. European Language Resources Association. ISSN 979-10-95546-00-9. p. 1742–1748.
• Lison, Pierre & Mavroeidis, Vasileios (2017). Neural Reputation Models learned from Passive DNS data, IEEE Big Data 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2017. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2715-0. p. 3662–3671. doi: 10.1109/BigData.2017.8258361.
• Lison, Pierre & Mavroeidis, Vasileios (2017). Automatic Detection of Malware-Generated Domains with Recurrent Neural Models. Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. Show summary

  Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning approach based on recurrent neural networks is able to detect domain names generated by DGAs with high precision. The neural models are estimated on a large training set of domains generated by various malwares. Experimental results show that this data-driven approach can detect malware-generated domain names with a F1 score of 0.971. To put it differently, the model can automatically detect 93 % of malware-generated domain names for a false positive rate of 1:100.

• Lison, Pierre & Kennington, Casey (2017). Incremental Processing for Neural Conversational Models. SemDial Proceedings. ISSN 2308-2275. p. 162–163. Show summary

  We present a simple approach to adapt neural conversation models to incremental processing. The approach is validated with a proof-of-concept experiment in a visual reference resolution task.

• Lison, Pierre & Bibauw, Serge (2017). Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models. In DeVault, David & Louis, Annie (Ed.), 18th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2017). Association for Computational Linguistics. ISSN 978-1-945626-82-1. p. 384–394. doi: 10.18653/v1/w17-5546.
• Lison, Pierre & Kutuzov, Andrei (2017). Redefining Context Windows for Word Embedding Models: An Experimental Study. In Tiedemann, Jörg (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7685-601-7. p. 284–288. Full text in Research Archive
• Lison, Pierre & Meena, Raveesh (2016). Automatic Turn Segmentation of Movie and TV Subtitles. In Dehak, Najim & Torres-Carrasquillo, Pedro (Ed.), 2016 Spoken Language Technology Workshop. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-4902-8. p. 245–252. doi: 10.1109/SLT.2016.7846272.
• Stoyanchev, Svetlana; Lison, Pierre & Bangalore, Srinivas (2016). Rapid Prototyping of Form-driven Dialogue Systems Using an Open-source Framework. In Fernández, Raquel & Minker, Wolfgang (Ed.), Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics. ISSN 978-1-945626-23-4. p. 216–219. doi: 10.18653/v1/w16-3626.
• Lison, Pierre & Kennington, Casey (2016). OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. In Pradhan, Sameer & Apidianaki, Marianna (Ed.), Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-945626-03-6. p. 67–72. doi: 10.18653/v1/p16-4012.
• Dragone, Paolo & Lison, Pierre (2016). Classification and Resolution of Non-Sentential Utterances in Dialogue. Italian Journal of Computational Linguistics. ISSN 2499-4553. 2(1), p. 45–62. Show summary

  This article addresses the problems of classification and resolution of non-sentential utterances (NSUs) in dialogue. NSUs are utterances that do not have a complete sentential form but convey a full clausal meaning given the conversational context, such as "To the contrary!" or "How much?". The presented approach builds upon the work of Fernández, Ginzburg, and Lappin (2007), who provide a taxonomy of NSUs divided in 15 classes along with a small annotated corpus extracted from dialogue transcripts. The main part of this article focuses on the automatic classification of NSUs according to these classes. We show that a combination of novel linguistic features and active learning techniques yields a significant improvement in the classification accuracy over the state-of-the-art, and is able to mitigate the scarcity of labelled data. Based on this classifier, the article also presents a novel approach for the semantic resolution of NSUs in context using probabilistic rules.

• Lison, Pierre & Tiedemann, Jörg (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association. ISSN 978-2-9517408-9-1. p. 923–929. Full text in Research Archive
• Dragone, Paolo & Lison, Pierre (2015). An Active Learning Approach to the Classification of Non-Sentential Utterances. In Bosco, Cristina; Tonelli, Sara & Zanzotto, Fabio Massimo (Ed.), Proceedings of the Second Italian Conference on Computational Linguistics. CLiC-IT 2015. ISSN 978-88-99200-62-6. p. 115–119. doi: 10.4000/books.aaccademia.1464.
• Dragone, Paolo & Lison, Pierre (2015). Non-sentential utterances in dialogue: experiments in classification and interpretation. SemDial Proceedings. ISSN 2308-2275. p. 170–172.
• Lison, Pierre & Kennington, Casey (2015). Developing Spoken Dialogue Systems with the OpenDial Toolkit. SemDial Proceedings. ISSN 2308-2275. p. 194–196.
• Lison, Pierre (2015). A hybrid approach to dialogue management based on probabilistic rules. Computer Speech and Language. ISSN 0885-2308. 34(1), p. 232–255. doi: 10.1016/j.csl.2015.01.001.
• Kosek, Michal Kajetan & Lison, Pierre (2014). An Intelligent Tutoring System for Learning Chinese with a Cognitive Model of the Learner. In Taalas, Peppi (Eds.), Proceedings of EUROCALL 2014. Research-publishing.net. ISSN 978-1-908416-19-3. p. 179–184. doi: 10.14705/rpnet.2014.000214.
• Lison, Pierre (2013). Model-based Bayesian Reinforcement Learning for Dialogue Management. Interspeech. ISSN 1990-9772. Full text in Research Archive
• Lison, Pierre (2013). Towards Online Planning for Dialogue Management with Rich Domain Knowledge. In Mariani, Joseph; Devillers, Laurence; Garnier-Rizet, Martine & Rosset, Sophie (Ed.), Natural Interaction with Robots, Knowbots and Smartphones - Putting Spoken Dialog Systems into practice. Springer. ISSN 978-1-4614-8279-6. doi: 10.1007/978-1-4614-8280-2_11.
• Lison, Pierre (2012). Towards Dialogue Management in Relational Domains. In Dobnik, Simon; Larsson, Staffan & Cooper, Robin (Ed.), Proceedings of the SLTC Workshop on Action, Perception and Language (APL 2012). APL workshop organisers.
• Lison, Pierre (2012). Towards Online Planning for Dialogue Management with Rich Domain Knowledge. In Mariani, Joseph; Devillers, Laurence; Garnier-Rizet, Martine & Rosset, Sophie (Ed.), Proceedings of the Fourth International Workshop on Spoken Dialog Systems (IWSDS 2012): Towards a Natural Interaction with Robots, Knowbots and Smartphones. IWSDS Program committee. doi: 10.1007/978-1-4614-8280-2_11.
• Lison, Pierre (2012). Probabilistic Dialogue Models with Prior Domain Knowledge. In Geunbae Lee, Gary & Ginzburg, Jonathan (Ed.), SIGDIAL 2012: Proceedings of 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics. ISSN 978-1-937284-44-2. p. 179–188. Full text in Research Archive

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• Lison, Pierre (2022). Dis, c'est quoi l'intelligence artificielle? Renaissance Du Livre. ISBN 2507057297. 96 p. Show summary

  L’intelligence artificielle est aujourd’hui sur toutes les lèvres. Certains y voient une révolution technologique qui viendra nous libérer de nombreuses tâches pénibles, répétitives ou dangereuses et transformera notre rapport au monde et au travail. Pour d’autres, elle représente au contraire un danger pour l’équilibre déjà fragile de nos sociétés, voire une menace existentielle pour l’humanité. Mais de quoi s’agit-il au juste ? Cet ouvrage aborde de manière pédagogique les idées-clés qui sous-tendent l’intelligence artificielle. Comment une machine peut-elle apprendre, raisonner et résoudre par elle-même des problèmes complexes ? Quelles sont les applications pratiques de l’intelligence artificielle ? Et en quoi l’intelligence d’une machine se différencie-t-elle du fonctionnement de notre cerveau humain ? Au fil d’une conversation, ce livre permet de mieux comprendre cette discipline au confluent de l’informatique, des mathématiques et des sciences cognitives.

• Lison, Pierre; Nilsson, Mattias & Recasens, Marta (2012). Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. ISBN 978-1-937284-19-0. 101 p.

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• Lund, Bjarte Aarmo; Sandsør, Astrid Marie Jorde & Lison, Pierre (2022). Problemer på kontoret: Alltid jeg som må trakte kaffe. Morgenbladet. ISSN 0805-3847.
• Lison, Pierre (2022). Anonymization of sensitive information.
• Lison, Pierre; Korsvoll, Nils Hallvard & Lundberg, Aase-Kristine Aasen (2022). Kjernekraft -er det farlig, eller er det fremtiden? Morgenbladet. ISSN 0805-3847.
• Lison, Pierre (2021). Vi må snakke om Bitcoin. NRK Ytring.
• Lison, Pierre; Bølstad, Jørgen & Kvellestad, Anders (2021). Forvirrende pandemistatistikk: Hva skal vi med logaritmer i grafer? Morgenbladet. ISSN 0805-3847.
• Korsvoll, Nils Hallvard; Elken, Mari; Sandtorv, Alexander Harald & Lison, Pierre (2021). Welcome to Norway! Khrono.no. ISSN 1894-8995.
• Lison, Pierre (2021). Skweak: Weak Supervision Made Easy for NLP.
• Lison, Pierre (2021). Fremdrift i forskningsprosjekter.
• Lison, Pierre; Pilán, Ildikó; Sánchez Ruenes, David; Batet, Montserrat & Øvrelid, Lilja (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions.
• Lison, Pierre; Pilán, Ildikó; Øvrelid, Lilja; Sánchez Ruenes, David & Batet, Montserrat (2021). Anonymisation Models for Text Data: State of the art, Challenges and Future Directions. Show summary

  This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.

• Lison, Pierre; Barnes, Jeremy Claude & Hubin, Aliaksandr (2021). skweak: weak supervision made easy for NLP. Show summary

  We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machine learning paradigm based on a simple idea: instead of labelling data points by hand, we use labelling functions derived from domain knowledge to automatically obtain annotations for a given dataset. The resulting labels are then aggregated with a generative model that estimates the accuracy (and possible confusions) of each labelling function. The skweak toolkit makes it easy to implement a large spectrum of labelling functions (such as heuristics, gazetteers, neural models or linguistic constraints) on text data, apply them on a corpus, and aggregate their results in a fully unsupervised fashion. skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling. We illustrate the use of skweak for NER and sentiment analysis. skweak is released under an open-source license and is available at https://github.com/NorskRegnesentral/skweak

• Lison, Pierre (2021). Språkteknologi: siste trender og vanlige fallgruver.
• Lison, Pierre (2020). Ethical and social impacts of AI.
• Lison, Pierre & Falkum, Ingrid Lossius (2020). Hva skjedde med «Don’t be evil»? Morgenbladet. ISSN 0805-3847.
• Riegler, Michael; Lison, Pierre; Strümke, Inga & Løland, Anders (2020). For enkelt om kunstig intelligens: – Diskriminerende og fordomsfull AI er ikke alltid lett å løse. Forskning.no. ISSN 1891-635X.
• Lison, Pierre & Falkum, Ingrid Lossius (2020). Kan kunstig intelligens "forstå" språk? Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116.
• Lison, Pierre (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach.
• Løland, Anders; Lison, Pierre & Falkum, Ingrid Lossius (2020). Episode 6: Kan språkteknologi virkelig forstå språk? Med Ingrid Lossius Falkum og Pierre Lison. [Internet]. Sannsynligvis VIKTIG (podkast).
• Løland, Anders & Lison, Pierre (2020). Episode 5: Hva er språkteknologi (eller NLP)? Med Pierre Lison. [Internet]. Sannsynligvis VIKTIG (podkast).
• Lison, Pierre (2020). Developing NLP models without labelled data using weak supervision.
• Lison, Pierre (2019). Open challenges in anonymisation.
• Prévot, Laurent; Magistry, Pierre & Lison, Pierre (2019). Should we use movie subtitles to study linguistic patterns of conversational speech? A study based on French, English and Taiwan Mandarin.
• Lison, Pierre (2019). Data-driven models of reputation for cybersecurity.
• Lison, Pierre (2019). Dialogue Modelling: Small data, Big data.
• Lison, Pierre (2019). Modellering av omdømme i cybersikkerhet med nevralske nettverk.
• Lison, Pierre (2019). Modélisation du dialogue: contrôle du dialogue et corpus multilingues.
• Lison, Pierre; Tiedemann, Jörg & Kouylekov, Milen (2018). OpenSubtitles 2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora. Show summary

  Movie and TV subtitles are a highly valuable resource for the compilation of parallel corpora thanks to their availability in large numbers and across many languages. However, the quality of the resulting sentence alignments is often lower than for other parallel corpora. This paper presents a new major release of the OpenSubtitles collection of parallel corpora, which is extracted from a total of 3.7 million subtitles spread over 60 languages. In addition to a substantial increase in the corpus size (about 30 % compared to the previous version), this new release associates explicit quality scores to each sentence alignment. These scores are determined by a statistical regression model based on simple language-independent features and estimated on a small sample of aligned sentence pairs. Evaluation results show that the model is able predict lexical translation probabilities with a root mean square error of 0.07 (coefficient of determination R2 = 0.47). Based on the scores produced by this regression model, the parallel corpora can be filtered to prune out alignments with a score below a given threshold

• Lison, Pierre (2018). Data-driven models of reputation in cyber-security. Show summary

  In this talk, I will present our work on developing data-driven, predictive models of reputation (such as benign or malicious) for end-point hosts. I'll focus on two particular questions: 1) Malware often relies on so-called domain-generation algorithms (DGAs) to produce "fake" domain names that are used to connect compromised hosts with a command-and-control server. Many types of DGAs are been developed, from simple hashing techniques to more sophisticated approaches based on wordlists. I will show that these malware-generated domain names can be detected through recurrent neural networks such as LSTMs or GRUs. 2) The second part of the talk will focus on neural models of traffic reputation learned from passive DNS data. Passive DNS data are collections of inter-server DNS queries captured by sensors distributed on the network. This data is a goldmine for predicting whether a given domain name or IP address is likely to be benign or malicious. I will describe a deep neural architecture that predicts the reputation of end-point hosts with high accuracy. The neural model is trained on a large passive DNS dataset (745 million entries) and relies on a broad range of features extracted from the DNs graph.

• Lison, Pierre (2018). Tekstmining: En kort innføring.
• Lison, Pierre (2018). Modélisation du dialogue : systèmes de dialogue parlé et corpus multilingues.
• Lison, Pierre (2018). Detecting Machine-translated Subtitles in Large Parallel Corpora. Show summary

  Parallel corpora extracted from online repositories of movie and TV subtitles are employed in a wide range of NLP applications, from language modelling to machine translation and dialogue systems. However, the subtitles uploaded in such repositories exhibit varying levels of quality. A particularly difficult problem stems from the fact that a substantial number of these subtitles are not written by human subtitlers but are simply generated through the use of online translation engines. This paper investigates whether these machine-generated subtitles can be detected automatically using a combination of linguistic and extra-linguistic features. We show that a feedforward neural network trained on a small dataset of subtitles can detect machine-generated subtitles with a F1-score of 0.64. Furthermore, applying this detection model on an unlabelled sample of subtitles allows us to provide a statistical estimate for the proportion of subtitles that are machine-translated (or are at least of very low quality) in the full corpus.

• Lison, Pierre (2018). Neural models for predicting the reputation of end-point hosts.
• Lison, Pierre (2018). SAFERS: Talegjenkjenning og maskinlæring for nødmeldetjenester.
• Lison, Pierre (2017). SAFERS - Speech Analytics for Emergency Response Services. Kan taleteknologi og maskinlæring brukes for å effektivisere nødmeldetjenester?
• Lison, Pierre (2017). Automatic Detection of Malware-Generated Domains with Recurrent Neural Models.
• Lison, Pierre (2017). Neural Reputation Models learned from Passive DNS Data.
• Lison, Pierre (2017). Opptreden i God Morgen Norge (TV2) for å vise Lenny roboten som ble brukt ved Forskningstorget. [TV]. God Morgen Norge (TV2).
• Lison, Pierre & Kennington, Casey (2017). Incremental Processing for Neural Conversational Models. Show summary

  We presented a simple approach to make neural dialogue models 'incremental' - that is, able to operate on incremental units instead of on complete utterances. The model can handle insertions, commit and revoke operations as well as incremental units associated with probabilities. A proof-of-concept experiment on a visual reference resolution task shows the promise of the approach.

• Lison, Pierre & Bibauw, Serge (2017). Not all dialogues are created equal: instance weighting for neural conversational models.
• Lison, Pierre (2016). Automatic Turn Segmentation for Movie and TV Subtitles.
• Lison, Pierre (2016). Dialogue modelling: small data and large data.
• Lison, Pierre (2016). Automatic Turn Segmentation for Movie and TV Subtitles. Show summary

  Movie and TV subtitles contain large amounts of conversational material, but lack an explicit turn structure. This paper present a data-driven approach to the segmentation of subtitles into dialogue turns. Training data is first extracted by aligning subtitles with transcripts in order to obtain speaker labels. This data is then used to build a classifier whose task is to determine whether two consecutive sentences are part of the same dialogue turn. The approach relies on linguistic, visual and timing features extracted from the subtitles themselves and does not require access to the audiovisual material - although speaker diarization can be exploited when audio data is available. The approach also exploits alignments with related subtitles in other languages to further improve the classification performance. The classifier achieves an accuracy of 78 % on a held-out test set. A follow-up annotation experiment demonstrates that this task is also difficult for human annotators.

• Lison, Pierre (2016). OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules.
• Lison, Pierre (2016). Hybrid dialogue management + dialogue modelling for MT.
• Lison, Pierre (2016). A short introduction to statistical machine translation. Show summary

  Machine translation (MT) systems such as Google Translate have become part of our daily life. But how do they work? In this talk, I'll explain how these systems are built. In the first part of my talk, I'll present a general overview of the field and the key ideas driving modern MT systems. In the second part, I'll dig deeper into the statistical techniques used to estimate translation models from data, and discuss some of the current hot topics in the field.

• Lison, Pierre (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. Show summary

  We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

• Lison, Pierre (2015). Structured Probabilistic Modelling for Dialogue Management.
• Lison, Pierre & Kennington, Casey (2015). Developing Spoken Dialogue Systems with the OpenDial toolkit.
• Dragone, Paolo & Lison, Pierre (2015). Non-sentential utterances in dialogue: experiments in classification and interpretation.
• Lison, Pierre (2014). Structured Probabilistic Modelling for Dialogue Management.
• Lison, Pierre & Meena, Raveesh (2014). Spoken Dialogue Systems: A New Frontier in Human-Computer Interaction. ACM Crossroads. ISSN 1528-4972. 21(1).
• Kosek, Michal Kajetan & Lison, Pierre (2014). An Intelligent Tutoring System for Learning Chinese with a Cognitive Model of the Learner.
• Lison, Pierre (2013). Model-based Bayesian Reinforcement Learning for Dialogue Management.
• Lison, Pierre (2013). Kan man snakke med en robot?
• Lison, Pierre (2013). Dr. Utenlansk. [Newspaper]. Dagsavisen.
• Lison, Pierre (2012). Dialogue Management with Probabilistic Rules.
• Lison, Pierre (2012). An Introduction to Machine Learning.
• Lison, Pierre (2012). Kan roboter lære seg selv å snakke med mennesker? [TV]. NRK.
• Lison, Pierre (2012). Lenny, en robot som lærer å snakke.
• Lison, Pierre (2012). Lenny, en robot som lærer å snakke.
• Lison, Pierre (2012). openDial: A toolkit for building dialogue systems based on probabilistic rules.
• Lison, Pierre (2012). openDial: a dialogue systems toolkit based on probabilistic rules.
• Lison, Pierre (2012). Probabilistic Dialogue Models with Prior Domain Knowledge.
• Jokinen, Kristiina; Heckmann, Martin; Lala, Dinesh & Lison, Pierre (2021). Proceedings of the 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction. RobotDial workshop.
• Redelmeier, Annabelle Alice; Lison, Pierre; Løland, Anders & Pilán, Ildikó (2021). Predicting insurance fraud with the help of a sentiment analysis model. Norsk Regnesentral.
• Lison, Pierre (2018). Anonymisering av rettsavgjørelser. Norsk Regnesentral.
• Lison, Pierre (2014). Structured Probabilistic Modelling for Dialogue Management. Universitetet i Oslo. ISSN 1501-7710. Show summary

  This thesis presents a new modelling framework for dialogue management based on the concept of probabilistic rules. Probabilistic rules are defined as if...then...else constructions associating logical conditions on input variables to probabilistic effects over output variables. These rules function as high-level templates for the generation of a directed graphical model. Their expressive power allows them to represent the probabilistic models employed in dialogue management in a compact and efficient manner. As a consequence, they can drastically reduce the amount of interaction data required for parameter estimation as well as enhance the system's ability to generalise over unseen situations. Furthermore, probabilistic rules can also be exploited to encode domain-specific constraints and assumptions into statistical models of dialogue, thereby enabling system designers to incorporate their expert knowledge of the problem structure in a concise and human-readable form. Due to their integration of logical and probabilistic reasoning, we argue that probabilistic rules are particularly well suited to devise hybrid models of dialogue management that can account for both the complexity and uncertainty that characterise many dialogue domains. The thesis also demonstrates how the parameters of probabilistic rules can be efficiently estimated using both supervised and reinforcement learning techniques. In the case of supervised learning, the rule parameters are learned by imitation on the basis of small amounts of Wizard-of-Oz data. Alternatively, rule parameters can also be optimised via trial and error from repeated interactions with a (real or simulated) user. Both learning strategies rely on Bayesian inference to iteratively estimate the parameter values and provide the best fit for the observed interaction data. Three consecutive experiments conducted in a human--robot interaction domain attest to the practical viability of the proposed framework and its advantages over traditional approaches. In particular, the empirical results of a user evaluation with 37 participants show that a dialogue manager structured with probabilistic rules outperforms both purely hand-crafted and purely statistical methods on an extensive range of subjective and objective metrics of dialogue quality. The modelling framework presented in this thesis is implemented in a new software toolkit called Opendial, which is made freely available to the research community and can be used to develop various types of dialogue systems based on probabilistic rules.

• Lison, Pierre; Baumann, Timo; Friedberg, Heather; Götze, Jana; Janarthanam, Srini & Lorenzo, Alejandra [Show all 7 contributors for this article] (2012). Proceedings of the 8th Young Researchers' Roundtable on Spoken Dialogue Systems (YRRSDS 2012). YRRSDS committee.

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