Assessing Quality in Human- and Machine-Generated Subtitles and Captions

Doherty, Stephen; Kruger, Jan‐Louis

doi:10.1007/978-3-319-91241-7_9

Cited by 13 publications

(12 citation statements)

References 34 publications

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“…Under "acceptability", errors of grammar, spelling and idiomaticity are evaluated; and for "readability" considerations include segmentation and spotting, punctuation, and reading speed and line length. Doherty and Kruger (2018) have pointed out that "[u]nlike traditional TQA models and metrics, assessment in AVT is largely based on prescriptive industry guidelines" (p. 182). Summarizing the commonalities found in the various reviews, reports, and guidelines, they discovered that the general principles that underpin good quality in AVT are accuracy, presentation, and timing.…”

Section: Subtitling Quality Assessmentmentioning

confidence: 99%

“…Applying Sperber and Wilson's Relevance Theory to subtitling, Bogucki (2022) has proposed that the rationale behind the process of decision-making in the practice of AVT can be explained by the interplay between relevance, communicative intention, and processing effort. Empirical research in this field focuses on the audience's visual attention and cognitive load and also their reception of subtitles (Doherty & Kruger, 2018). The research methods employed include surveys, questionnaires, eye-tracking, and electroencephalography (EEG).…”

Section: Subtitling Quality Assessmentmentioning

confidence: 99%

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Applying machine translation to Chinese–English subtitling: Constraints and challenges

Tian

2023

LANS-TTS

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Focusing on the topic of applying machine translation (MT) to Chinese–English subtitling, the subject of the present study, this article first analyses the current literature and introduces the general constraints in Chinese–English subtitling from three perspectives: technical, cultural, and textual. Technical constraints are imposed by the limited time and space available for each subtitle; cultural constraints relate to the disparities in the beliefs, values, customs, behaviours, and artefacts of different cultural groups; and textual constraints predominantly manifest in the differences between the source and target languages and the segmented nature of subtitles. In order to respond to these constraints and to achieve conciseness, comprehensibility, and coherence in the translated subtitles, this study highlighted condensation, context, and coordination as the key strategies to adopt. However, these strategies pose considerable challenges for MT in Chinese–English subtitling. First, the common practice of full translation by most MT tools tends to work against making subtitles concise. Second, the lack of relevant contextual knowledge limits the ability of MT to generate appropriate translations. Third, the segmented display of subtitles makes it challenging for MT to capture and reflect the logic in the source text, on the one hand, and to produce coherent output in self-contained segments, on the other. To illustrate these constraints and challenges, this article provides examples of bilingual subtitles from American Factory (Bognar & Reichert, 2019), an Oscar Winner for Best Documentary Feature, and compares the official subtitles with those generated by three popular MT tools in Chinese–English translation. The study investigated the efficacy of MT subtitling and its potential to produce quality subtitles; and this article proposes possible solutions and suggestions for improving the quality of MT in subtitling.

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Section: Subtitling Quality Assessmentmentioning

confidence: 99%

Section: Subtitling Quality Assessmentmentioning

confidence: 99%

Applying machine translation to Chinese–English subtitling: Constraints and challenges

Tian

2023

LANS-TTS

View full text Add to dashboard Cite

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“…(Figure 1). For consistent generation of keywords for the abbreviated transcript, the full transcript was shortened using the linguistic techniques of reformulation, condensation, and omission as suggested by Doherty and Kruger [11]. For example, the sentence "One more concept to be familiar with is called look ahead and look behind matching" from the full transcript was abbreviated as "look ahead and look behind matching. "…”

Section: Experimental Designmentioning

confidence: 99%

Design Recommendations for Using Textual Aids in Data-Science Programming Courses

Choi

Mills

Brooks

et al. 2022

Proceedings of the 53rd ACM Technical Symposium on Computer Science Education

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Despite a recent shift towards online learning, recommendations for multimedia design principles in programming-based instruction remain unclear. Specifically, how can we teach people to code, a text-heavy medium, properly in online instruction? This question is especially important since the text-based format of screencasts may interact with psychological mechanisms known to affect cognitive processing and learning. We investigate this question, and find that previous results from other domains do not necessarily hold in the programming education. We also explore how design changes in textual aids affect learners' performance in programming-based multimedia learning. Our results suggest that the redundancy effect does not significantly hinder learning, which conflicts with previous findings, and that the spatial contiguity effect occurs even between textual components. This work contributes to an evidence-based understanding of how to design more effective multimedia learning environments for programming-based instruction.

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“…This study strengthens the point that the socio-linguistic level of the speaker may affect their ability to use speech recognition accurately. Doherty and Kruger (2018) looked into the quality of human and machine-generated captioning systems. They examined the various industry standards developed to guarantee that viewers are presented with high-quality subtitling and captioning regardless of language or disability.…”

Section: Auto-generated Caption Errorsmentioning

confidence: 99%

An Analysis of the Errors in the Auto-Generated Captions of University Commencement Speeches on YouTube

Lee¹,

Cha²

2020

The Journal of AsiaTEFL

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Auto-generated captions on YouTube have proven useful in helping viewers better understand the words being spoken. However, at times they fail to contain accurate captions. In these cases, they lead to confusion. The aim of this paper is to identify and analyze errors in the auto-generated captions of 20 commencement speeches on YouTube. These speeches were presented over a period of 12 years by speakers from different walks of life. The researchers selected ten male and ten female icons. Only the first 10 minutes of the speeches were utilized for this investigation. All the captioned errors were collected and analyzed. Upon completion of the analysis, it was discovered that the frequency of errors in each speech ranged between 10 and 46 cases, with an average of one error occurring about every 26 seconds. Among the different error categories, nouns record the highest number with 144 cases (31.3%). The second is verbs with 93 cases (20.2%), then prepositions with 37 cases (8.1%). Among the four subcategories, namely omission, addition, substitution, and word order, substitution recorded the highest amount of errors with 357 cases (77.6%). Furthermore, the errors were classified into two major groups. The first, involving function words, appeared in 169 cases (36.7%). The second, involving content words, appeared in 291 cases (63.3%). The results of this research suggest that a continuous development of the voice recognition software that automatically generates captions is necessary for more efficient and accurate data that will help viewers and listeners better comprehend the video contents.

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Assessing Quality in Human- and Machine-Generated Subtitles and Captions

Cited by 13 publications

References 34 publications

Applying machine translation to Chinese–English subtitling: Constraints and challenges

Applying machine translation to Chinese–English subtitling: Constraints and challenges

Design Recommendations for Using Textual Aids in Data-Science Programming Courses

An Analysis of the Errors in the Auto-Generated Captions of University Commencement Speeches on YouTube

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