Dataset Cartography for Implicit Discourse Relation Recognition: Promises and Pitfalls

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A case study applies dataset cartography, a technique by Swayamdipta (2020), to the DiscoGeM corpus (Scholman et al., 2022) for Implicit Discourse Relation Recognition (IDRR). The analysis reveals that using low confidence scores to identify errors in crowdsourced IDRR data is unreliable, primarily because label rarity significantly influences confidence levels. Conversely, high-confidence datapoints prove valuable for auditing cue-rich sections of the dataset. A lexical probe further demonstrates a strong association between these high-confidence items and intra-argument cue words, predominantly temporal ones. This suggests that dataset cartography can effectively diagnose easily learnable, cue-driven cases, highlighting a need to balance such instances to ensure the robustness of IDRR learning models.

Key takeaway

For NLP engineers curating datasets for Implicit Discourse Relation Recognition, you should reconsider using low confidence scores as a primary indicator for identifying annotation errors, as label rarity skews these metrics. Instead, utilize high-confidence datapoints to audit and understand cue-rich regions within your corpus. This approach helps diagnose easily learnable, cue-driven examples, enabling you to balance your training data for improved model robustness and generalization.

Key insights

Dataset cartography diagnoses cue-driven, easy-to-learn cases in IDRR, but low confidence is unreliable for error detection.

Principles

• Label rarity affects confidence scores.
• High confidence flags cue-rich data.
• Balance cue-driven cases for robustness.

Method

Apply dataset cartography to IDRR corpora to identify cue-driven, easy-to-learn instances and audit cue-rich regions, rather than relying on low confidence for error detection.

In practice

• Audit dataset regions with high confidence.
• Identify temporal intra-argument cues.
• Balance cue-driven examples in training.

Topics

• Implicit Discourse Relation Recognition
• Dataset Cartography
• DiscoGeM Corpus
• Crowdsourced Data
• Lexical Cues
• Data Auditing

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.