Generating Fact Checking Explanations (2004.05773v1)

Abstract: Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -- generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.

Analyzing Automated Generation of Fact Checking Explanations

The paper "Generating Fact Checking Explanations" by Atanasova et al. investigates the computational challenges associated with providing justifications for fact-checking verdicts. This research extends the current landscape of automated veracity prediction by addressing the crucial step of generating coherent and explanatory justifications, a task that remains largely unautomatized.

Core Contributions

The authors introduce a novel multi-task learning strategy that simultaneously models veracity prediction and explanation generation. The principal hypothesis is that by jointly optimizing these tasks, the quality of explanations and the accuracy of veracity predictions can both be enhanced. The paper indicates that this multi-objective modeling approach yields better performance than training the tasks separately.

Key contributions of the paper are as follows:

1. A new perspective on veracity explanation generation as a summarization task, utilizing claim contexts to produce explanations.
2. The utilization of a DistilBERT-based architecture, adapting it for both extractive summarization of explanation and veracity classification, thereby leveraging transformers in a novel dual-task setup.
3. Empirical validation showing that a joint training model achieves better coverage and informativeness of explanations over models trained solely for explanation extraction.

Methodological Approach

The authors employ a DistilBERT transformer model pre-trained with a language-modelling objective, which is further fine-tuned for two specific tasks: extracting veracity justifications and predicting the veracity of claims. The explanation generation is framed as a summarization task, selecting salient sentences from comprehensive ruling comments to approximate human-generated justifications. They propose a joint optimization strategy where cross-stitch layers allow the interchange of task-specific features and shared features between explaining the prediction and predicting the veracity itself.

The dataset applied, LIAR-PLUS, offers a challenging platform with its real-world claims and accompanying justifications—this allows the model to learn from detailed ruling comments while ensuring that extracted justifications are aligned with actual fact-checking processes.

Evaluation and Findings

Two types of evaluations are conducted: automatic and manual. The automatic evaluation employs ROUGE scores to determine the discrepancy between model-generated explanations and human-authored justifications. The manual evaluation assesses explanations on coverage, redundancy, and informativeness criteria, thus addressing limitations of ROUGE in evaluating semantic content quality. Additionally, veracity predictions were assessed via macro F1 scores.

Key findings from the evaluations include:

• Jointly trained models on explanations and fact-checking show superior veracity prediction performance compared to models trained individually.
• Explain-MT (multi-task model) explanations tend to enhance understanding of the veracity decision more so than Explain-Extractive (separate-task model).
• The paper further elaborates on cases where the multi-task model manages to capture relevant context absent in human annotations, thereby informing the veracity decision effectively.

Implications and Future Directions

This paper highlights significant implications for natural language processing methodologies in the domain of automated fact-checking. By successfully integrating explanation with prediction, the research presents a promising step towards developing systems that not only predict claim veracity but also elucidate the rationale behind predictions.

Future research can extend this work by exploring adaptable systems capable of generating explanations based on dynamically gathered web evidence. Moreover, delving deeper into enhancing the fluency and expressiveness of text-generating models could mitigate redundancy and elevate reader comprehension. Shifting focus to abstractive methods and employing larger models could further advance the precision and quality of explanations.

The paper lends itself to ongoing exploration in the field, paving the way for AI applications that demand high interpretability and accountability, especially crucial as automated fact-checking systems begin to enter practical journalism and policy-making domains.