ExplainaBoard: An Explainable Leaderboard for NLP (2104.06387v2)

Abstract: With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g.~what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g.~where does system A outperform system B? What if we combine systems A, B, and C?) and (iii) examine prediction results closely (e.g.~what are common errors made by multiple systems, or in what contexts do particular errors occur?). So far, ExplainaBoard covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. ExplainaBoard keeps updated and is recently upgraded by supporting (1) multilingual multi-task benchmark, (2) meta-evaluation, and (3) more complicated task: machine translation, which reviewers also suggested.} We not only released an online platform on the website \url{http://explainaboard.nlpedia.ai/} but also make our evaluation tool an API with MIT Licence at Github \url{https://github.com/neulab/explainaBoard} and PyPi \url{https://pypi.org/project/interpret-eval/} that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate "output-driven" research in the future.

• The paper introduces ExplainaBoard as a novel framework that deconstructs system performance into interpretable and interactive metrics.
• It evaluates 12 NLP tasks over 50 datasets and 400 models, leveraging fine-grained error analysis and data bias evaluation.
• The work enhances reliability in model comparisons through confidence and calibration metrics, setting a new standard for NLP leaderboards.

ExplainaBoard: An Explainable Leaderboard for NLP

The paper "ExplainaBoard: An Explainable Leaderboard for NLP" introduces a novel approach to enhancing leaderboards in NLP. Traditional leaderboards have been instrumental in tracking the performance of state-of-the-art systems across various NLP tasks. However, these platforms often provide a limited one-dimensional perspective focused on holistic accuracy, which may obscure deeper insights into system performance and their comparative analyses. This paper addresses these limitations by presenting ExplainaBoard, a leaderboard designed to improve interpretability, interactivity, and reliability in evaluating NLP systems.

Limitations of Traditional Leaderboards

The authors identify three critical limitations of existing leaderboards:

1. Interpretability: Conventional leaderboards summarize system performance using a single metric, making it challenging to understand specific strengths and weaknesses.
2. Interactivity: The static nature of traditional leaderboards restricts deeper exploration of results and the ability to evaluate cross-system interactions.
3. Reliability: Current systems often fail to convey the reliability of their rankings, particularly in datasets with limited sample sizes.

Features of ExplainaBoard

ExplainaBoard addresses the aforementioned limitations through a variety of functionalities applicable across numerous NLP tasks:

• Interpretable Analysis: By breaking down system performance into interpretable groups, users can assess strengths and weaknesses along dimensions such as entity length and sentence length in specific tasks. This is achieved through single-system and pairwise analyses, as well as data bias evaluation.
• Interactive Features: ExplainaBoard allows users to engage with results through fine-grained error analysis, system combination, and diagnostic tools, facilitating a deeper understanding of model interactions and performances.
• Reliable Metrics: The integration of confidence and calibration analyses supports a robust evaluation framework, providing insights into the statistical reliability of performance results.

System Implementation and Use Cases

The ExplainaBoard implementation spans 12 NLP tasks, 50 datasets, and 400 models. It applies to text classification, sequence labeling, structure prediction, and text generation tasks. The functionalities are exemplified using different attributes for tasks like Named Entity Recognition (NER), where systems are evaluated on entity and sentence lengths, among other attributes.

A case paper on NER demonstrates ExplainaBoard's analytical capabilities in comparing leading systems, elucidating areas for potential improvement through ensemble methods, and highlighting common mispredictions across models. The platform thus enables researchers to pinpoint specific challenges and advantages in existing models and systems.

Implications and Future Developments

The implications of ExplainaBoard are multifaceted. Practically, it serves as a sophisticated tool for researchers to conduct nuanced analyses of NLP systems, facilitating informed enhancements and innovations. Theoretically, it enriches the interpretability of model evaluations, which could influence future research paradigms in system output analysis.

The future developments of ExplainaBoard include expanding its applicability by incorporating more NLP tasks and datasets, as well as integrating functionalities for glass-box analysis. By collaborating with existing leaderboard organizers, ExplainaBoard aims to broaden its adoption and utility within the NLP research community. As such, it sets a new benchmark for the structure and function of leaderboards in advancing NLP research.