ExplainaBoard: An Explainable Leaderboard for NLP

1 Introduction

• The platform has been updated to support multilingual multitask benchmarking, meta-evaluation, and machine translation.The paper identifies these as recent upgrades.Source: §1 · p. 1
• Existing leaderboards standardize comparisons but often reduce performance to one opaque score, lack interactive analyses, and provide little information about ranking reliability.The paper notes that leaderboard prestige can also encourage focus on improving scores at the cost of deeper understanding.Source: §1 · pp. 1–2
• ExplainaBoard is a software package and hosted leaderboard designed to address these interpretability, interactivity, and reliability gaps.The authors report deployment for 9 tasks and 41 datasets, with adaptation to new tasks.Source: §1 · p. 2
• Fine-grained results and confidence intervals can help developers diagnose system successes and failures, compare with baselines, and understand where improvements come from.The authors also suggest that this understanding can support more efficient system improvements and reveal advantages of systems that are not holistically state of the art.Source: §1 · p. 2
• The platform supports leaderboard creation across NLP tasks and upgrades traditional leaderboards with more fine-grained analysis.The authors cite an ExplainaBoard implementation for the XTREME benchmark.Source: §1 · p. 2
• ExplainaBoard encourages sharing system outputs so researchers can survey model capabilities and compare the relative advantages of different systems.The authors argue this can help researchers identify remaining questions and future directions.Source: §1 · p. 2

2.1 Interpretability

• ExplainaBoard supports interpretable evaluation across different task types in one software package, using defined attributes to partition test sets and compare bucket-level performance.Applying the approach to a new task involves defining attributes, bucketing the test set, and calculating performance for each bucket.Source: §2.1 · p. 3
• Single-system histograms reveal stronger and weaker attribute buckets, while pairwise comparisons and dataset-bias analysis expose performance gaps and dataset characteristics.Examples include weaker performance on longer entities and comparing average entity length across datasets.Source: §2.1 · pp. 3–4

2.2 Interactivity

• Fine-grained error analysis displays shared errors, bucket-specific errors, and the unique errors of either system in a pairwise comparison.These views can help identify challenging samples or possible annotation errors.Source: §2.2 · p. 4
• System combination lets users vote over multiple systems’ outputs; the authors report that state-of-the-art performance can be further improved for some tasks, including NER and chunking.NER uses SPANNER, summarization uses REFACTOR, and other tasks use majority voting.Source: §2.2 · p. 4

2.3 Reliability

• Confidence analysis uses bootstrapped confidence intervals for holistic and bucket-level metrics, with error-bar widths indicating uncertainty in performance estimates.The intervals help convey uncertainty when bucket sample counts are imbalanced.Source: §2.3 · p. 4
• Calibration analysis lets users evaluate how well a system’s prediction confidence aligns with correctness.Source: §2.3 · p. 4

3 Tasks, Datasets and Systems

• Supported task types include text classification, text-span classification, text-pair classification, and sequence labeling.Sequence-labeling tasks include named entity recognition, part-of-speech tagging, chunking, and Chinese word segmentation.Source: §3 · pp. 4–5
• The system also covers structure prediction and semantic parsing tasks.Source: §3 · p. 5
• Text generation coverage mainly focuses on conditional tasks such as summarization and machine translation; 265 models were implemented by the authors because system outputs are not yet standardly released.Summarization outputs were collected from prior collections and recent systems, while machine-translation outputs came from WMT20.Source: §3 · pp. 4–5

4.1 Experimental Setup

• The setup defines entity length, sentence length, and entity label as attributes, and the collected outputs cover 74 models across six NER datasets.Source: §4.1 · p. 5

4.2 Analysis using ExplainaBoard

• LUKE surpasses FLERT overall but performs worse on PERSON entities, and the top system also does worse on longer entities.The comparison shows how attribute-level patterns can differ from holistic performance.Source: §4.2 · p. 5
• The systems share mispredicted samples whose further analysis may reveal challenging patterns or annotation errors.Box C identifies errors common to all three systems.Source: §4.2 · p. 5, Figure 2 · p. 6
• Calibration analysis is currently limited to classification tasks because defining calibration for structured prediction is relatively challenging.Source: §4.2 · p. 5
• 94.65 F1 on CoNLL-2003 is reported for a new state-of-the-art result achieved with a simple voting ensemble.Figure 2’s Box D presents the combined result of the top three systems.Source: §4.2 · pp. 5–6, Figure 2 · p. 6
• Figure 2’s panels show single-system attribute results, pairwise performance gaps, shared errors, and a combined top-three result; histogram bins open fine-grained error cases.Source: Figure 2 · p. 6

• Published use cases apply single- and pairwise diagnostics, data-bias analysis, system combination, and reliability analysis across several NLP tasks.Source: §5 · p. 6
• Researchers can use the task-specific website or run released backend code on their own system outputs.Source: §5 · p. 6
• Community members can contribute system outputs, task attributes, and datasets or benchmarks for existing or new tasks.Source: §5 · p. 6

• The authors describe ExplainaBoard as a new paradigm for NLP leaderboard development and characterize its development as only beginning.Source: §6 · p. 6
• System outputs could form a central repository for future cross-system analysis and system combination.The platform’s analysis and combination capabilities are presented as incentives for researchers to submit outputs.Source: §6 · p. 7
• Current analysis is black-box only: it uses system outputs without accessing model internals; glass-box analysis is left as future work.Source: §6 · p. 7
• The roadmap targets broader applicability through more leaderboard-organizer collaborations and coverage of tasks, datasets, models, and functionalities.Source: §6 · p. 7