Sonia Badene


2021

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Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)
Amir Zeldes | Yang Janet Liu | Mikel Iruskieta | Philippe Muller | Chloé Braud | Sonia Badene
Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)

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The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
Amir Zeldes | Yang Janet Liu | Mikel Iruskieta | Philippe Muller | Chloé Braud | Sonia Badene
Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)

In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking). Adding to the 2019 tasks on Elementary Discourse Unit Segmentation and Connective Detection, this iteration of the Shared Task included for the first time a track on discourse relation classification across three formalisms: RST, SDRT, and PDTB. In this paper we review the data included in the Shared Task, which covers nearly 3 million manually annotated tokens from 16 datasets in 11 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.

2019

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Weak Supervision for Learning Discourse Structure
Sonia Badene | Kate Thompson | Jean-Pierre Lorré | Nicholas Asher
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper provides a detailed comparison of a data programming approach with (i) off-the-shelf, state-of-the-art deep learning architectures that optimize their representations (BERT) and (ii) handcrafted-feature approaches previously used in the discourse analysis literature. We compare these approaches on the task of learning discourse structure for multi-party dialogue. The data programming paradigm offered by the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probability distributions of the class labels given the data. We show that on our task the generative model outperforms both deep learning architectures as well as more traditional ML approaches when learning discourse structure—it even outperforms the combination of deep learning methods and hand-crafted features. We also implement several strategies for “decoding” our generative model output in order to improve our results. We conclude that weak supervision methods hold great promise as a means for creating and improving data sets for discourse structure.

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Apprentissage faiblement supervisé de la structure discursive (Learning discourse structure using weak supervision )
Sonia Badene | Catherine Thompson | Nicholas Asher | Jean-Pierre Lorré
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts

L’avènement des techniques d’apprentissage automatique profond a fait naître un besoin énorme de données d’entraînement. De telles données d’entraînement sont extrêmement coûteuses à créer, surtout lorsqu’une expertise dans le domaine est requise. L’une de ces tâches est l’apprentissage de la structure sémantique du discours, tâche très complexe avec des structures récursives avec des données éparses, mais qui est essentielle pour extraire des informations sémantiques profondes du texte. Nous décrivons nos expérimentations sur l’attachement des unités discursives pour former une structure, en utilisant le paradigme du data programming dans lequel peu ou pas d’annotations sont utilisées pour construire un ensemble de données d’entraînement “bruité”. Le corpus de dialogues utilisé illustre des contraintes à la fois linguistiques et non-linguistiques intéressantes qui doivent être apprises. Nous nous concentrons sur la structure des règles utilisées pour construire un modèle génératif et montrons la compétitivité de notre approche par rapport à l’apprentissage supervisé classique.

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Data Programming for Learning Discourse Structure
Sonia Badene | Kate Thompson | Jean-Pierre Lorré | Nicholas Asher
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probability distributions of the class labels given the training candidates. These results are later generalized using a discriminative model. Snorkel’s attractive promise to create a large amount of annotated data from a smaller set of training data by unifying the output of a set of heuristics has yet to be used for computationally difficult tasks, such as that of discourse attachment, in which one must decide where a given discourse unit attaches to other units in a text in order to form a coherent discourse structure. Although approaching this problem using Snorkel requires significant modifications to the structure of the heuristics, we show that weak supervision methods can be more than competitive with classical supervised learning approaches to the attachment problem.