Pierre Lison


2022

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Bootstrapping Text Anonymization Models with Distant Supervision
Anthi Papadopoulou | Pierre Lison | Lilja Øvrelid | Ildikó Pilán
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed to automatically annotate text documents including personal data about a subset of those individuals. More precisely, the method determines which text spans ought to be masked in order to guarantee k-anonymity, assuming an adversary with access to both the text documents and the background information expressed in the knowledge graph. The resulting collection of labeled documents is then used as training data to fine-tune a pre-trained language model for text anonymization. We illustrate this approach using a knowledge graph extracted from Wikidata and short biographical texts from Wikipedia. Evaluation results with a RoBERTa-based model and a manually annotated collection of 553 summaries showcase the potential of the approach, but also unveil a number of issues that may arise if the knowledge graph is noisy or incomplete. The results also illustrate that, contrary to most sequence labeling problems, the text anonymization task may admit several alternative solutions.

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Neural Text Sanitization with Explicit Measures of Privacy Risk
Anthi Papadopoulou | Yunhao Yu | Pierre Lison | Lilja Øvrelid
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a novel approach for text sanitization, which is the task of editing a document to mask all (direct and indirect) personal identifiers and thereby conceal the identity of the individuals(s) mentioned in the text. In contrast to previous work, the approach relies on explicit measures of privacy risk, making it possible to explicitly control the trade-off between privacy protection and data utility. The approach proceeds in three steps. A neural, privacy-enhanced entity recognizer is first employed to detect and classify potential personal identifiers. We then determine which entities, or combination of entities, are likely to pose a re-identification risk through a range of privacy risk assessment measures. We present three such measures of privacy risk, respectively based on (1) span probabilities derived from a BERT language model, (2) web search queries and (3) a classifier trained on labelled data. Finally, a linear optimization solver decides which entities to mask to minimize the semantic loss while simultaneously ensuring that the estimated privacy risk remains under a given threshold. We evaluate the approach both in the absence and presence of manually annotated data. Our results highlight the potential of the approach, as well as issues specific types of personal data can introduce to the process.

2021

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Assessing the Quality of Human-Generated Summaries with Weakly Supervised Learning
Joakim Olsen | Arild Brandrud Næss | Pierre Lison
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

This paper explores how to automatically measure the quality of human-generated summaries, based on a Norwegian corpus of real estate condition reports and their corresponding summaries. The proposed approach proceeds in two steps. First, the real estate reports and their associated summaries are automatically labelled using a set of heuristic rules gathered from human experts and aggregated using weak supervision. The aggregated labels are then employed to learn a neural model that takes a document and its summary as inputs and outputs a score reflecting the predicted quality of the summary. The neural model maps the document and its summary to a shared “summary content space” and computes the cosine similarity between the two document embeddings to predict the final summary quality score. The best performance is achieved by a CNN-based model with an accuracy (measured against the aggregated labels obtained via weak supervision) of 89.5%, compared to 72.6% for the best unsupervised model. Manual inspection of examples indicate that the weak supervision labels do capture important indicators of summary quality, but the correlation of those labels with human judgements remains to be validated. Our models of summary quality predict that approximately 30% of the real estate reports in the corpus have a summary of poor quality.

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Anonymisation Models for Text Data: State of the art, Challenges and Future Directions
Pierre Lison | Ildikó Pilán | David Sanchez | Montserrat Batet | Lilja Øvrelid
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)

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.

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skweak: Weak Supervision Made Easy for NLP
Pierre Lison | Jeremy Barnes | Aliaksandr Hubin
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

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

2020

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Named Entity Recognition without Labelled Data: A Weak Supervision Approach
Pierre Lison | Jeremy Barnes | Aliaksandr Hubin | Samia Touileb
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.

2019

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PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Youngsoo Jang | Jongmin Lee | Jaeyoung Park | Kyeng-Hun Lee | Pierre Lison | Kee-Eung Kim
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-of-the-art performance. The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch. To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning. We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.

2018

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OpenSubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora
Pierre Lison | Jörg Tiedemann | Milen Kouylekov
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Redefining Context Windows for Word Embedding Models: An Experimental Study
Pierre Lison | Andrey Kutuzov
Proceedings of the 21st Nordic Conference on Computational Linguistics

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Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Pierre Lison | Serge Bibauw
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Neural conversational models require substantial amounts of dialogue data to estimate their parameters and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.

2016

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OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Pierre Lison | Casey Kennington
Proceedings of ACL-2016 System Demonstrations

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OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles
Pierre Lison | Jörg Tiedemann
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.

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Rapid Prototyping of Form-driven Dialogue Systems Using an Open-source Framework
Svetlana Stoyanchev | Pierre Lison | Srinivas Bangalore
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2012

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Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Pierre Lison | Mattias Nilsson | Marta Recasens
Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Probabilistic Dialogue Models with Prior Domain Knowledge
Pierre Lison
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2011

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Multi-Policy Dialogue Management
Pierre Lison
Proceedings of the SIGDIAL 2011 Conference

2010

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Towards Relational POMDPs for Adaptive Dialogue Management
Pierre Lison
Proceedings of the ACL 2010 Student Research Workshop

2009

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An Integrated Approach to Robust Processing of Situated Spoken Dialogue
Pierre Lison | Geert-Jan M. Kruijff
Proceedings of SRSL 2009, the 2nd Workshop on Semantic Representation of Spoken Language