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Reproduction of Human Evaluation for Generating Fact Checking Explanations

Q: How can the human evaluation process be further improved to increase the reproducibility of findings in NLP?

To enhance the reproducibility of findings in NLP through human evaluation, several improvements can be implemented: Standardized Evaluation Criteria: Establishing clear and standardized evaluation criteria is crucial. This includes defining evaluation metrics, annotation guidelines, and ensuring consistency in the evaluation process across different studies. Inter-Annotator Agreement: Ensuring high inter-annotator agreement by having multiple annotators evaluate the same samples independently can help validate the reliability of human judgments. Training and Calibration: Providing adequate training to evaluators and calibrating their judgments through practice sessions can reduce subjectivity and improve the consistency of evaluations. Diverse Evaluator Panels: Including diverse evaluators in terms of demographics, expertise, and language proficiency can help capture a broader range of perspectives and reduce biases in the evaluation process. Transparency and Reporting: Transparently reporting details of the evaluation process, including participant demographics, evaluation instructions, and raw data, enables better understanding and replication of the study. Feedback Mechanisms: Implementing feedback mechanisms where evaluators can provide comments on the evaluation process can help identify and address potential issues or biases. Automated Evaluation Tools: Integrating automated evaluation tools alongside human evaluation can provide complementary insights and enhance the reproducibility of findings.

Q: What other factors, beyond the joint training framework, could contribute to the improved performance of the Explain-MT model compared to the Explain-Extr model?

Apart from the joint training framework, several factors could contribute to the improved performance of the Explain-MT model compared to the Explain-Extr model: Data Quality and Quantity: The availability of high-quality training data and a sufficient amount of data for the Explain-MT model can enhance its learning capabilities and performance. Model Complexity: The complexity of the Explain-MT model, which integrates explanation generation with veracity prediction, allows it to capture more nuanced relationships and dependencies in the data, leading to improved performance. Feature Representation: The joint training framework may enable the Explain-MT model to learn more informative feature representations by leveraging the interplay between explanation generation and veracity prediction tasks. Task Alignment: The alignment of the Explain-MT model's training objectives with the end goal of generating fact-checking explanations may result in a more focused and effective learning process compared to the Explain-Extr model. Fine-Tuning Strategies: The specific fine-tuning strategies employed for the Explain-MT model, such as optimization techniques or hyperparameter tuning, could contribute to its enhanced performance.

Q: How might the reproduction of human evaluation studies in other domains, such as dialogue systems or text summarization, differ from the findings in this fact-checking domain?

The reproduction of human evaluation studies in other domains like dialogue systems or text summarization may present some differences compared to the findings in the fact-checking domain: Evaluation Criteria: Different domains may require specific evaluation criteria tailored to the nature of the task. For instance, dialogue systems may focus on conversational coherence and engagement, while text summarization may prioritize informativeness and conciseness. Annotation Complexity: The complexity of annotating data for evaluation purposes can vary across domains. Dialogue systems may involve multi-turn interactions, sentiment analysis, or user satisfaction, while text summarization may require assessing content relevance and coverage. Evaluator Expertise: Evaluators in different domains may need domain-specific expertise to evaluate the quality of outputs accurately. For example, evaluating dialogue systems may require knowledge of conversational norms, while text summarization evaluation may benefit from expertise in content extraction and abstraction. Data Variability: The variability and diversity of data in different domains can impact the reproducibility of findings. Dialogue systems may encounter a wide range of user inputs and responses, while text summarization may involve diverse document types and genres. Task Complexity: The complexity of the tasks being evaluated can influence the reproducibility of findings. Dialogue systems with open-ended conversations or text summarization with complex content may present unique challenges in evaluation and reproducibility compared to fact-checking explanations. Overall, while the principles of reproducibility and robust evaluation practices apply across domains, the specific nuances and requirements of each domain can lead to variations in the reproduction of human evaluation studies.

Kernekoncepter

The core message of this paper is that the authors partially reproduced the findings of Atanasova et al. (2020) regarding generating fact checking explanations, focusing on the criterion of Coverage. Their reproduction supports the original findings, with the model trained to generate explanations jointly with veracity prediction outperforming the model trained to generate explanations in isolation.

Resumé

This paper presents a partial reproduction of the work by Atanasova et al. (2020) on generating fact checking explanations, as part of the ReproHum shared task. The authors focused on reproducing the human evaluation results for the criterion of Coverage.

The original study by Atanasova et al. (2020) aimed to address the lack of transparency in fact-checking systems by generating natural language explanations to justify the assigned veracity labels. They used a multi-task learning framework to jointly optimize explanation generation and veracity prediction.

For the human evaluation, the original study assessed the generated explanations on criteria such as Coverage, Non-redundancy, and Non-contradiction. In the reproduction, the authors focused solely on the Coverage criterion, following the instructions provided by the ReproHum team.

The reproduction study involved 3 NLP PhD students as evaluators, who ranked the outputs of 3 systems (a gold standard and two models) on the Coverage criterion. The results of the reproduction were compared to the original findings, and the authors observed some differences in the overall rankings, with one of the proposed models ranking higher than the gold standard in their reproduction. However, the reproduction supported the original finding that the model trained jointly for explanation generation and veracity prediction (Explain-MT) outperformed the model trained to generate explanations in isolation (Explain-Extr).

The authors note that their reproduction covers only one of the multiple human evaluation criteria used in the original work, and the patterns observed may not necessarily hold across all criteria. They emphasize the importance of conducting reproduction studies to assess the reproducibility of findings in the field of NLP.

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Statistik

The gold standard human-authored explanations were preferred by all participants in the original study, but this was not the case in the reproduction or the reanalysis of the original data.
The Explain-MT model, which was trained jointly for explanation generation and veracity prediction, outperformed the Explain-Extr model in both the original study and the reproduction.
The overall rankings assigned to each output were higher (i.e., worse) in the reproduction compared to the original findings.

Citater

"Whilst human evaluation is often seen as the gold standard method of evaluation which takes into account the perceptions of real human end-users, there is much debate over the reproducibility of such evaluation."
"Atanasova et al. (2020) identified an overall research focus on the veracity prediction task of news claims in existing research and a lack of work focusing on generating natural language explanations to justify these veracity predictions."
"Notably, the generated explanations achieve better coverage and overall quality compared to explanations trained solely to mimic human justifications. This suggests that the joint training framework allows the system to capture the knowledge required for accurate fact-checking, leading to more informative and relevant explanations."

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ReproHum #0087-01: Human Evaluation Reproduction Report for Generating Fact Checking Explanations

by Tyler Loakma... kl. arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17481.pdf

ReproHum #0087-01: Human Evaluation Reproduction Report for Generating Fact Checking Explanations

Dybere Forespørgsler

How can the human evaluation process be further improved to increase the reproducibility of findings in NLP?

To enhance the reproducibility of findings in NLP through human evaluation, several improvements can be implemented:

Standardized Evaluation Criteria: Establishing clear and standardized evaluation criteria is crucial. This includes defining evaluation metrics, annotation guidelines, and ensuring consistency in the evaluation process across different studies.

Inter-Annotator Agreement: Ensuring high inter-annotator agreement by having multiple annotators evaluate the same samples independently can help validate the reliability of human judgments.

Training and Calibration: Providing adequate training to evaluators and calibrating their judgments through practice sessions can reduce subjectivity and improve the consistency of evaluations.

Diverse Evaluator Panels: Including diverse evaluators in terms of demographics, expertise, and language proficiency can help capture a broader range of perspectives and reduce biases in the evaluation process.

Transparency and Reporting: Transparently reporting details of the evaluation process, including participant demographics, evaluation instructions, and raw data, enables better understanding and replication of the study.

Feedback Mechanisms: Implementing feedback mechanisms where evaluators can provide comments on the evaluation process can help identify and address potential issues or biases.

Automated Evaluation Tools: Integrating automated evaluation tools alongside human evaluation can provide complementary insights and enhance the reproducibility of findings.

What other factors, beyond the joint training framework, could contribute to the improved performance of the Explain-MT model compared to the Explain-Extr model?

Apart from the joint training framework, several factors could contribute to the improved performance of the Explain-MT model compared to the Explain-Extr model:

Data Quality and Quantity: The availability of high-quality training data and a sufficient amount of data for the Explain-MT model can enhance its learning capabilities and performance.

Model Complexity: The complexity of the Explain-MT model, which integrates explanation generation with veracity prediction, allows it to capture more nuanced relationships and dependencies in the data, leading to improved performance.

Feature Representation: The joint training framework may enable the Explain-MT model to learn more informative feature representations by leveraging the interplay between explanation generation and veracity prediction tasks.

Task Alignment: The alignment of the Explain-MT model's training objectives with the end goal of generating fact-checking explanations may result in a more focused and effective learning process compared to the Explain-Extr model.

Fine-Tuning Strategies: The specific fine-tuning strategies employed for the Explain-MT model, such as optimization techniques or hyperparameter tuning, could contribute to its enhanced performance.

How might the reproduction of human evaluation studies in other domains, such as dialogue systems or text summarization, differ from the findings in this fact-checking domain?

The reproduction of human evaluation studies in other domains like dialogue systems or text summarization may present some differences compared to the findings in the fact-checking domain:

Evaluation Criteria: Different domains may require specific evaluation criteria tailored to the nature of the task. For instance, dialogue systems may focus on conversational coherence and engagement, while text summarization may prioritize informativeness and conciseness.

Annotation Complexity: The complexity of annotating data for evaluation purposes can vary across domains. Dialogue systems may involve multi-turn interactions, sentiment analysis, or user satisfaction, while text summarization may require assessing content relevance and coverage.

Evaluator Expertise: Evaluators in different domains may need domain-specific expertise to evaluate the quality of outputs accurately. For example, evaluating dialogue systems may require knowledge of conversational norms, while text summarization evaluation may benefit from expertise in content extraction and abstraction.

Data Variability: The variability and diversity of data in different domains can impact the reproducibility of findings. Dialogue systems may encounter a wide range of user inputs and responses, while text summarization may involve diverse document types and genres.

Task Complexity: The complexity of the tasks being evaluated can influence the reproducibility of findings. Dialogue systems with open-ended conversations or text summarization with complex content may present unique challenges in evaluation and reproducibility compared to fact-checking explanations.

Overall, while the principles of reproducibility and robust evaluation practices apply across domains, the specific nuances and requirements of each domain can lead to variations in the reproduction of human evaluation studies.