[Paper Review] Towards More Fine-grained and Reliable NLP Performance Prediction

Zihuiwen Ye, Pengfei Liu+2 more|arXiv (Cornell University)|Feb 10, 2021

Topic Modeling32 references4 citations

TL;DR

This paper proposes fine-grained performance prediction for NLP tasks, extending beyond holistic metrics like F1 to individual example categories (e.g., entity length in NER), and introduces confidence intervals and calibration analysis to assess prediction reliability. It finds that while some models achieve high accuracy, all overestimate confidence, highlighting the need for reliability analysis in performance prediction systems.

ABSTRACT

Performance prediction, the task of estimating a system's performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explosion of different datasets, languages, tasks, and models. In this paper, we make two contributions to improving performance prediction for NLP tasks. First, we examine performance predictors not only for holistic measures of accuracy like F1 or BLEU but also fine-grained performance measures such as accuracy over individual classes of examples. Second, we propose methods to understand the reliability of a performance prediction model from two angles: confidence intervals and calibration. We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future. We make our code publicly available: \url{https://github.com/neulab/Reliable-NLPPP}

Motivation & Objective

• To extend performance prediction in NLP beyond holistic metrics to fine-grained evaluation metrics such as accuracy per example category (e.g., entity length in NER).
• To develop methods for estimating confidence intervals and assessing calibration of performance prediction models.
• To evaluate the reliability of performance predictions in both holistic and fine-grained settings across multiple NLP tasks.
• To investigate whether high-accuracy prediction models are also well-calibrated and to quantify overconfidence in predicted intervals.

Proposed method

• The paper formulates performance prediction as a regression task, estimating model performance using features of the model, training data, test data, and training strategy.
• It introduces a fine-grained evaluation setting where performance is predicted per subgroup (e.g., by entity length), using model-specific features to predict subgroup-level F1 or accuracy.
• Confidence intervals for predictions are estimated using bootstrap resampling of training data to model prediction uncertainty.
• Calibration is evaluated using reliability diagrams and calibration error (CE), measuring the discrepancy between predicted confidence levels and actual accuracy.
• Four models—XGBoost, LightGBM, MLP, and a baseline linear model—are trained and compared across four NLP tasks: machine translation, POS tagging, NER, and CWS.
• The reliability of predictions is assessed via coverage (fraction of true values within predicted intervals) and calibration error (CE), with a focus on overconfidence.

Experimental results

Research questions

• RQ1 Can performance prediction be extended to fine-grained evaluation metrics such as accuracy over individual example categories (e.g., entity length in NER)?
• RQ2 How can confidence intervals be reliably estimated for performance predictions in low-data regimes?
• RQ3 How well do the predicted confidence intervals calibrate with the actual frequency of correct predictions?
• RQ4 Do models with high prediction accuracy also exhibit well-calibrated uncertainty estimates?
• RQ5 Is overconfidence a systematic issue across different performance prediction models in fine-grained settings?

Key findings

• XGBoost achieved the lowest calibration error (3.75) and highest coverage in holistic settings, outperforming LGBM and other models.
• All models produced over-confident confidence intervals, with predicted intervals failing to cover the true performance more often than expected.
• LGBM achieved the lowest RMSE (2.389) in machine translation but had a higher calibration error (7.23) than XGBoost, indicating poor calibration despite high accuracy.
• In fine-grained NER analysis, XGBoost showed better calibration than LGBM, with its predicted distribution more closely matching the actual distribution.
• The case study revealed that LGBM’s predicted distribution was narrower and more over-confident, while XGBoost showed a leftward bias, indicating systematic under-prediction of performance.
• The results demonstrate that high-accuracy prediction models are not necessarily well-calibrated, and reliability analysis is essential for practical deployment.