Exploring the Correlation between Human and Machine Evaluation of Simultaneous Speech Translation
(2406.10091)Abstract
Assessing the performance of interpreting services is a complex task, given the nuanced nature of spoken language translation, the strategies that interpreters apply, and the diverse expectations of users. The complexity of this task become even more pronounced when automated evaluation methods are applied. This is particularly true because interpreted texts exhibit less linearity between the source and target languages due to the strategies employed by the interpreter. This study aims to assess the reliability of automatic metrics in evaluating simultaneous interpretations by analyzing their correlation with human evaluations. We focus on a particular feature of interpretation quality, namely translation accuracy or faithfulness. As a benchmark we use human assessments performed by language experts, and evaluate how well sentence embeddings and LLMs correlate with them. We quantify semantic similarity between the source and translated texts without relying on a reference translation. The results suggest GPT models, particularly GPT-3.5 with direct prompting, demonstrate the strongest correlation with human judgment in terms of semantic similarity between source and target texts, even when evaluating short textual segments. Additionally, the study reveals that the size of the context window has a notable impact on this correlation.
Overview
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The paper investigates how well automatic metrics, incorporating advanced NLP techniques, correlate with human evaluations in assessing simultaneous speech translation quality.
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Using a combination of professional human interpreters and the KUDO AI Speech Translator, the study measures translation accuracy via both human judgment on a Likert scale and machine evaluations using neural network models.
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GPT-3.5 demonstrated the highest correlation with human assessments, but ethical considerations, such as privacy and fairness, need to be addressed when integrating these tools into professional practices.
Exploring the Correlation between Human and Machine Evaluation of Simultaneous Speech Translation: An Expert Overview
The evaluation of simultaneous speech translation presents a multifaceted challenge due to the intrinsic characteristics of spoken language. Unlike written translations, simultaneous interpretations incorporate a significant degree of non-linearity shaped by the cognitive strategies of human interpreters. The paper “Exploring the Correlation between Human and Machine Evaluation of Simultaneous Speech Translation” by Xiaoman Wang and Claudio Fantinuoli explore this complexity, examining the reliability of automatic metrics to evaluate translation quality akin to human judgment.
Introduction and Objectives
The study confronts the longstanding issue of subjective inconsistencies in human evaluations of translation quality. Manual assessments, while possessing a nuanced understanding of context and fidelity, are labor-intensive and yield varied results dependent on the evaluator's experience and expectations. With advancements in NLP, automated evaluations leveraging metrics such as BLEU, METEOR, and more modern embeddings and LLMs, provide a potential alternative. This paper aims to assess the correlation between human ratings and machine evaluations, focusing specifically on the dimension of translation accuracy in simultaneous interpreting tasks.
Methodology
A notable aspect of this study is its dataset, composed of twelve English speeches translated into Spanish, segmented into manageable portions to facilitate analysis. This dataset features translations produced by professional human interpreters (Translation H) and by the KUDO AI Speech Translator (Translation M).
Human Evaluation
Human assessment adhered to a Likert scale, capturing ratings from both professional interpreters and bilingual individuals, thus reflecting diverse perspectives. Notably, the evaluators were blind to the source of the translation, mitigating potential biases.
Machine Evaluation
The evaluation metrics utilized were based on semantic similarity derived from embeddings using three distinct neural network models: all-MiniLM-L6-v2, GPT-Ada, and Universal Sentence Encoder Multilingual (USEM). Additionally, GPT-3.5’s direct prompting capability was employed to rate semantic similarity on a Likert scale. Cosine similarity calculations of these embeddings measured alignment between source texts and translations.
Contextual Considerations
A critical area of investigation was the impact of context window size on evaluation accuracy. By adjusting the segment window size up to five segments, the study examined how extended context influences the efficacy of semantic similarity computations in correlation with human judgments.
Results
Correlation Analysis
Among the models tested, GPT-3.5 exhibited the highest median correlation with human evaluations, validating its utility in approximating human judgment in terms of translation quality. The performance metric of GPT-3.5 improved with expanding segment window sizes, indicating its capability to incorporate contextual dependencies effectively.
While the all-MiniLM-L6-v2 model showed variable performance with broader interquartile ranges, GPT-Ada and USEM demonstrated more consistent yet moderate correlations. Interestingly, differences emerged when comparing human and machine translations, with human-produced translations generally showing stronger correlations.
Ethical Implications
The paper underscores potential ethical concerns related to the use of automated metrics. Continuous monitoring and evaluation of interpreters could infringe on privacy and affect professional autonomy. Moreover, reliance on automated assessments for employment decisions raises questions about fairness and accountability, emphasizing the need for ethical vigilance as these tools become integrated into professional settings.
Conclusion and Future Directions
This investigation offers valuable insights into the applicability of automated metrics for simultaneous speech translation evaluation. GPT-3.5, especially, shows promise as a robust tool for mirroring human evaluative perspectives on translation accuracy. Despite these findings, the study highlights the limitations posed by low interrater agreement among human evaluators and the intrinsic complexity of spoken translation evaluation. Future research avenues could explore the intricacies of error typologies and refine metrics to accommodate broader contexts and text types more effectively.
In summary, while automated evaluation tools advance our capacity to consistently and rapidly assess translation quality, they must be developed and deployed with careful consideration of their ethical implications and limitations. The findings of this study lay a foundational groundwork for future enhancements in AI-driven interpreting quality assessment, fostering a synergy between human expertise and machine efficiency.
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