mlsanityData to 7 Oct 2026

Implicit Discourse Relation Classification For Nigerian Pidgin

arXiv 2406.18776 ↗First posted 26 Jun 2024cs.CLcited by 6

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Abstract (arXiv)

Despite attempts to make Large Language Models multi-lingual, many of the world's languages are still severely under-resourced. This widens the performance gap between NLP and AI applications aimed at well-financed, and those aimed at less-resourced languages. In this paper, we focus on Nigerian Pidgin (NP), which is spoken by nearly 100 million people, but has comparatively very few NLP resources and corpora. We address the task of Implicit Discourse Relation Classification (IDRC) and systematically compare an approach translating NP data to English and then using a well-resourced IDRC tool and back-projecting the labels versus creating a synthetic discourse corpus for NP, in which we translate PDTB and project PDTB labels, and then train an NP IDR classifier. The latter approach of learning a "native" NP classifier outperforms our baseline by 13.27% and 33.98% in f1 score for 4-way and 11-way classification, respectively.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 59 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

18 of 59 references.

References · 18

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6 more
  1. 2021DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detectioncited by 15
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Citing papers per year: 2024: 1; 2025: 5.

6 papers.