Papers by Zineb Bennis

1 papers
A simple but effective model for attachment in discourse parsing with multi-task learning for relation labeling (2023.eacl-main)

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Challenge: a discourse parsing model for conversation trained on the STAC is hard due to the complexity of discourse graphs and the frequent lack of surface cues provided by EDUs.
Approach: They propose a discourse parsing model for conversation trained on the STAC that encodes discourse units and uses a multitask setting to predict relation labels.
Outcome: The proposed model outperforms state-of-the-art models for discourse attachment prediction with no loss in performance for attachment.

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