| Challenge: | Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs). |
| Approach: | They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations. |
| Outcome: | Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages. |
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| Challenge: | Discourse segmentation is the first step of discourse analysis. |
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| Challenge: | Towards language scalability, major progress has been achieved in multilingual language technology in recent years. |
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Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
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Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification (2021.emnlp-main)
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| Challenge: | Discourse learning is a complex task, and schemas evolve across annotation efforts preventing compilation of smaller datasets into larger ones. |
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
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Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)
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Exploring morphology-aware tokenization: A case study on Spanish language modeling (2025.emnlp-main)
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DISRPT: A Multilingual, Multi-domain, Cross-framework Benchmark for Discourse Processing (2024.lrec-main)
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Chloé Braud, Amir Zeldes, Laura Rivière, Yang Janet Liu, Philippe Muller, Damien Sileo, Tatsuya Aoyama
| Challenge: | DISRPT is a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing. |
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Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization (2020.acl-main)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods and training end-to-end models. |
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