Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets (1709.09500v1)

Abstract: With the ever-growing amounts of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper, we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.

• The paper introduces a replicability analysis framework that quantifies algorithmic performance across multiple NLP datasets by employing partial conjunction hypothesis testing.
• It compares Bonferroni and Fisher's methods to control type I errors and improve significance testing over standard per-dataset evaluations.
• Experimental results on dependency parsing, POS tagging, sentiment classification, and word similarity highlight the method's robustness in varied settings.

Analysis of "Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets" (1709.09500)

Introduction

The paper "Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets" (1709.09500) addresses the statistical challenges of evaluating NLP algorithms across multiple datasets. With the proliferation of textual data from diverse languages, domains, and genres, comparing algorithm performance using multiple datasets has become standard. However, traditional statistical methods for hypothesis testing are inadequate for such settings, leading to potentially erroneous conclusions. The authors propose a replicability analysis framework to systematically and statistically evaluate multiple comparison scenarios in NLP.

Problem Statement

In the current NLP research landscape, the efficacy of algorithms is often judged based on performance across several datasets. However, the likelihood of identifying spurious patterns increases with the number of datasets due to multiple comparison problems. The existing methods primarily rely on per-dataset significance testing, typically neglecting the increased risk of type I errors when performing multiple tests. This neglect often results in adopting new algorithms based on possibly unfounded claims of superiority.

Theoretical Framework

The paper introduces replicability analysis grounded in the partial conjunction framework. This framework is intended to address two critical questions in multiple comparison contexts:

1. Counting: How many datasets show that an algorithm A outperforms algorithm B?
2. Identification: Which specific datasets demonstrate this superiority?

The authors adopt statistical methodologies from other fields, like genomics and psychology, where replicability is crucial. Particularly, they leverage the work on partial conjunction hypothesis testing, which provides a statistically sound method for determining the number of datasets where a significant difference is observed.

Statistical Methods

The authors discuss two statistical approaches:

1. Bonferroni's Method: A conservative approach that does not assume any dependency among datasets. It controls the family-wise error rate (FWER) by adjusting the significance level for multiple tests.
2. Fisher's Method: Suitable for independent datasets, this method combines p-values from individual tests to evaluate the global null hypothesis. It is more powerful than Bonferroni's method when independence is a valid assumption.

These methodologies are applied to construct partial conjunction p-values that quantify the number of datasets where significant differences exist at a given confidence level.

Experimental Evaluation

The replicability analysis framework is evaluated using four NLP tasks:

• Multi-Domain Dependency Parsing: The authors analyze datasets where different parsers are evaluated across multiple linguistic domains. The results demonstrate the framework's ability to discern meaningful differences that traditional methods might overlook.
• Multilingual POS Tagging: The framework identifies significant differences in model performances across varied linguistic datasets.
• Cross-Domain Sentiment Classification: Demonstrates how the framework can cope with interdependent datasets, where traditional per-dataset analysis might yield overly optimistic conclusions.
• Word Similarity Prediction: The results show how different embedding models perform across diverse datasets, highlighting potential replicability issues.

Practical Implications

The proposed framework shifts the focus from merely counting datasets with significant results to a robust statistical evaluation of multiple comparisons. The authors provide a statistical guarantee that their method will not overestimate the number of datasets with true effects. This approach encourages the NLP community to increase the diversity and number of datasets used for evaluation, fostering advancements in algorithms' generality and applicability.

Conclusion

The replicability analysis framework offers a significant advancement in how NLP algorithm comparisons are conducted. By ensuring statistical soundness in multi-dataset evaluations, the framework mitigates the risk of spurious claims of algorithm superiority. This methodology is especially pertinent as NLP continues to grow in complexity and application scope, demanding rigorous evaluation strategies to support robust, replicable research findings. The paper provides a valuable toolkit that researchers can employ to ensure their findings reflect true algorithmic advancements, rather than statistical artifacts.