Julie Hunter


A simple but effective model for attachment in discourse parsing with multi-task learning for relation labeling
Zineb Bennis | Julie Hunter | Nicholas Asher
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

In this paper, we present a discourse parsing model for conversation trained on the STAC. We fine-tune a BERT-based model to encode pairs of discourse units and use a simple linear layer to predict discourse attachments. We then exploit a multi-task setting to predict relation labels. The multitask approach effectively aids in the difficult task of relation type prediction; our f1 score of 57 surpasses the state of the art with no loss in performance for attachment, confirming the intuitive interdependence of these two tasks. Our method also improves over previous discourse parsing models in allowing longer input sizes and in permitting attachments in which one node has multiple parents, an important feature of multiparty conversation.


Weakly supervised discourse segmentation for multiparty oral conversations
Lila Gravellier | Julie Hunter | Philippe Muller | Thomas Pellegrini | Isabelle Ferrané
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text. Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous. We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information. The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score. Evaluation is performed on data like those used to define our heuristic rules, but also on transcripts from two other corpora.


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Proceedings of the IWCS workshop on Foundations of Situated and Multimodal Communication
Nicholas Asher | Julie Hunter | Alex Lascarides
Proceedings of the IWCS workshop on Foundations of Situated and Multimodal Communication


Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus
Nicholas Asher | Julie Hunter | Mathieu Morey | Benamara Farah | Stergos Afantenos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.


Integrating Non-Linguistic Events into Discourse Structure
Julie Hunter | Nicholas Asher | Alex Lascarides
Proceedings of the 11th International Conference on Computational Semantics


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Because We Say So
Julie Hunter | Laurence Danlos
Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)