Explainable Machine Learning Approaches for Geolinguistic Authorship Profiling in Forensic Linguistics: A Case Study

Core Concepts

Explainable machine learning approaches can provide valuable insights for geolinguistic authorship profiling in forensic linguistics, complementing traditional qualitative methods.

Abstract

This paper explores the use of explainable machine learning approaches for geolinguistic authorship profiling in forensic linguistics. The authors focus on dialect classification as a means of geolinguistic profiling, using a dataset of German social media posts. The key highlights and insights are: The authors fine-tuned BERT-based language models (XLM-RoBERTa and German BERT) on the dialect classification task, achieving high accuracies (up to 75.31% for the 3-class setting). To understand the inner workings of the classifiers, the authors employed a leave-one-word-out (LOO) approach, which extracts the lexical features most relevant for the classification. The extracted lexical features reflect regional linguistic variation, including dialectal items, textualizations of regional pronunciation, and place names. This provides valuable insights for geolinguistic profiling. While a significant proportion of the extracted features are place names, the authors note that the models also rely on other dialectal lexical items, which can complement the expertise of forensic linguists. The explainable nature of the approach allows for verification of the model's decisions and can help introduce the method to legal settings, even if the classifiers themselves may not meet court admissibility standards. Overall, the authors demonstrate that explainable machine learning approaches can provide valuable insights for geolinguistic authorship profiling in forensic linguistics, complementing traditional qualitative methods.

Stats

The corpus consists of approximately 240 million tokens from about 8,500 locations, with 388 locations having a token count of over 10,000.

Quotes

"Even though research in forensic linguistics works more and more with statistical and computational approaches, authorship profiling often remains a manual task. This is at times credited to the black-box approaches in current NLP research, meaning that the lack of explainability precludes these approaches from being used in legal settings (see Nini, 2023)." "While the approach does not fully explain the inner workings of the model, experts can use the extracted features to a) verify that the model indeed reached a sound decision, for example by evaluating the features against previous dialectological findings, and b) use the explanations to introduce the method to law enforcement or jurisprudence."

Key Insights Distilled From

Explainability of Machine Learning Approaches in Forensic Linguistics: A Case Study in Geolinguistic Authorship Profiling

by Dana Roemlin... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18510.pdf

Deeper Inquiries

How can the explainable machine learning approach be further refined to minimize the reliance on place names and focus more on dialectal lexical items?

To reduce the dependence on place names and enhance the focus on dialectal lexical items in the explainable machine learning approach for geolinguistic authorship profiling, several refinements can be implemented: Feature Engineering: Incorporate linguistic features that are more indicative of dialectal variations, such as phonological, morphological, or syntactic features specific to regional dialects. This can involve analyzing linguistic patterns unique to certain dialects rather than just relying on place names. Contextual Analysis: Consider the context in which words or phrases appear within the text. By analyzing the surrounding words and phrases, the model can better understand the dialectal usage of certain terms beyond just geographical references. Domain-Specific Training: Train the model on a more diverse and extensive dataset that includes a wide range of dialectal variations. By exposing the model to a broader spectrum of linguistic features, it can learn to differentiate dialectal items more effectively. Post-Processing Techniques: Implement post-processing techniques to filter out common words or phrases that may not be dialect-specific. By prioritizing unique and regionally distinct terms, the model can focus on extracting dialectal lexical items. Collaborative Approach: Involve domain experts, such as forensic linguists specializing in regional dialects, in the model development process. Their insights can help guide the model to focus on relevant linguistic features beyond just place names.

What are the potential limitations or challenges in using this approach for authorship profiling in cases where the questioned document does not contain sufficient regional linguistic variation?

When dealing with questioned documents that lack significant regional linguistic variation, there are several limitations and challenges in using the explainable machine learning approach for authorship profiling: Limited Discriminatory Features: Without substantial regional linguistic variation in the text, the model may struggle to identify dialectal or regional markers that are crucial for geolinguistic authorship profiling. This can lead to less accurate or inconclusive results. Overreliance on General Features: The model may resort to general linguistic features or common vocabulary present in the text, which may not be specific enough to differentiate between authors based on regional variations. Bias in Training Data: If the training data used to develop the model lacks diversity in regional dialects or linguistic variations, the model may not be equipped to handle cases with subtle or nuanced regional differences. Interpretation Challenges: In cases where the questioned document does not exhibit clear regional linguistic traits, explaining the model's predictions to stakeholders, such as law enforcement or legal professionals, can be challenging and may lack robust evidence to support the conclusions. Need for Supplementary Evidence: In instances of limited regional linguistic variation, additional forms of evidence or traditional forensic linguistic analysis may be required to complement the findings of the machine learning model and ensure a comprehensive authorship analysis.

How can the insights from the explainable machine learning approach be combined with traditional qualitative methods used by forensic linguists to provide a more comprehensive and robust authorship analysis?

Integrating insights from the explainable machine learning approach with traditional qualitative methods in forensic linguistics can enhance the authorship analysis process: Cross-Validation: Validate the findings of the machine learning model with traditional qualitative methods by comparing the extracted dialectal lexical items with known linguistic patterns and dialectal features identified by forensic linguists. Expert Review: Engage forensic linguists to review the results of the machine learning model and provide expert insights into the relevance and accuracy of the extracted features in relation to regional variations and authorship characteristics. Feature Interpretation: Collaborate with linguists to interpret the significance of the extracted features in the context of regional dialects and authorship profiling. Linguistic expertise can help contextualize the findings and identify subtle nuances that may impact the analysis. Case-Specific Analysis: Tailor the combination of machine learning insights and qualitative methods to the specific characteristics of each authorship case, leveraging the strengths of both approaches to address the unique linguistic challenges presented in the questioned documents. Enhanced Reporting: Present a comprehensive analysis that integrates findings from both the machine learning model and traditional methods, providing a detailed and well-rounded explanation of the authorship characteristics identified in the text. This combined approach can offer a more robust and defensible authorship analysis in legal or investigative settings.