Quantified Reproducibility Assessment of NLP Results (2204.05961v1)

Abstract: This paper describes and tests a method for carrying out quantified reproducibility assessment (QRA) that is based on concepts and definitions from metrology. QRA produces a single score estimating the degree of reproducibility of a given system and evaluation measure, on the basis of the scores from, and differences between, different reproductions. We test QRA on 18 system and evaluation measure combinations (involving diverse NLP tasks and types of evaluation), for each of which we have the original results and one to seven reproduction results. The proposed QRA method produces degree-of-reproducibility scores that are comparable across multiple reproductions not only of the same, but of different original studies. We find that the proposed method facilitates insights into causes of variation between reproductions, and allows conclusions to be drawn about what changes to system and/or evaluation design might lead to improved reproducibility.

• The paper introduces a novel method, QRA, to objectively measure NLP reproducibility by applying metrology principles.
• It quantifies reproducibility using the coefficient of variation and confidence intervals, highlighting differences across evaluation settings.
• The application of QRA in tasks like text simplification and essay scoring demonstrates its potential to standardize reproducibility assessments in NLP research.

Quantified Reproducibility Assessment of NLP Results

The paper "Quantified Reproducibility Assessment of NLP Results" presents a method for evaluating the reproducibility of results in NLP through a framework rooted in metrology. This method, termed Quantified Reproducibility Assessment (QRA), provides a systematic approach to measure and compare the reproducibility of various NLP systems and evaluation measures across different studies.

Introduction to Reproducibility in NLP

Reproducibility is crucial in NLP to validate the reliability of experimental results. The paper addresses the challenges faced in reproducing results from NLP studies, particularly focusing on the absence of standardized methods and the subjective nature of assessing whether reproduction attempts have been successful. The authors propose QRA as a solution, leveraging concepts from metrology to provide a quantified and objective assessment of reproducibility.

Methodology of QRA

QRA is based on the concepts of repeatability and reproducibility from the International Vocabulary of Metrology (VIM). The method distinguishes between repeatability, where conditions are consistent across measurements, and reproducibility, where variations may exist due to differences in evaluation settings or participant roles.

To apply QRA, the following steps are undertaken:

1. Identify the object (e.g., NLP system) and measurand (evaluation metric) of interest.
2. Gather necessary measurement conditions and the associated values.
3. Compute the coefficient of variation (CV) of the measurements to obtain a reproducibility score.
4. Report the reproducibility score and confidence intervals along with all conditions of measurement.

Evaluation of QRA

The method was evaluated using several NLP tasks, including text simplification, essay scoring, and football report generation. Each task involved multiple system evaluations with varied methodologies. QRA was applied to assess the reproducibility of these results, considering variations in system design, evaluation metrics, and participant demographics.

The outcomes demonstrated that QRA could effectively differentiate between studies with varying degrees of reproducibility. Systems with more complex evaluation settings, such as those involving human judgments, displayed larger variabilities. This variability highlighted the need for careful consideration of evaluation conditions in assessing reproducibility.

Implications and Future Work

QRA has profound implications for the field of NLP. It standardizes the assessment of reproducibility across diverse NLP applications, thus enhancing the comparability and reliability of results. The paper suggests that adopting QRA can lead to better-designed studies with clearer expectations for reproducibility. The method could also be extended to new types of evaluations and contribute to establishing benchmarks for reproducible research.

Future work may involve integrating QRA into the inception phase of paper designs, allowing for pre-emptive evaluation of reproducibility prospects. The expansion of this framework to address emerging NLP challenges could further promote robustness and transparency in the field.

Conclusion

The "Quantified Reproducibility Assessment of NLP Results" paper provides a significant contribution to the understanding and evaluation of reproducibility in NLP. Through the application of metrology-based concepts, the QRA method offers a practical and scalable way to assess and compare the reproducibility of NLP systems and their results, potentially transforming reproducibility practices in the field.