Multi-Model System for Effective Subtitling Compression (2024)

Abstract: This paper presents RACAI’s system used for the shared task of ‘Subtitling track: Subtitle Compression’ (the English to Spanish language direction), organized as part of ‘the 21st edition of The International Conference on Spoken Language Translation (IWSLT 2024)’. The proposed system consists of multiple models whose outputs are then ensembled using an algorithm, which has the purpose of maximizing the similarity of the initial and resulting text. We present the introduced datasets and the models’ training strategy, along with the reported results on the proposed test set.

## introduction subtitles play a vital role in ensuring accessibility and comprehension of audiovisual (av) content for viewers with diverse needs, including those with hearing impairments or language barriers. however, traditional subtitling methods often generate text exceeding recommended reading speed constraints, hindering comprehension and viewer engagement. this problem becomes particularly pronounced for audiences with slower reading speeds or limited language proficiency. in the context of the 21st edition of the international conference on spoken language translation (iwslt 2024), the subtitle compression task, part of the subtitling track, required participants to propose systems that rephrase subtitles that are non-compliant with the reading speed constraint without limitations on the training data conditions. this paper describes the possibility of using large language models (llms) to achieve this while trying to benefit from the initial content in the source language. sometimes, sentences have formats that make them hard to compress, especially when a translation step has been made. the most fundamental example of such inconvenience is regarding idioms. they might not have perfect equivalents in the target language, and thus, their compression becomes even more challenging to process. problems of this kind can be partially solved by initially compressing the sentence in the source language and then translating it. our contribution is twofold: a) we introduce a new method that is able to combine the predictions of multiple models; b) we explore different parameters for the proposed algorithm and present the results on the shared task dataset. the rest of the paper is structured as follows: section 2 presents related work, section 3 describes the method proposed, including dataset description (in section 3.3), model training (in section 3.4) and ensemble process (in section 3.5); results are given in section 4 and we conclude in section 5. ## related work in this section, we explore the various methodologies and research efforts that have contributed to the development of compression tasks. although the compression task is inherently monolingual, we consider not only the works focused on text summarization but also those addressing automatic subtitling, machine translation (mt), and automatic speech recognition (asr). this is because these domains often employ similar techniques and face comparable challenges in reducing and transforming textual data while maintaining its essential information and coherence. ## results ## conclusion this paper presents racai's system for the "subtitling track: subtitle compression" shared task, focusing on compressing subtitles from english to spanish while maintaining readability within reading speed constraints. our system leverages multiple large language models (llms) to generate alternative compressed sentences for the original text. an ensemble selection algorithm then chooses the most suitable compressed options based on similarity metrics. this approach allows us to benefit from the strengths of various models and address potential shortcomings of individual models. future work could explore the incorporation of additional metrics or quality estimation techniques within the ensemble selection algorithm. additionally, investigating the effectiveness of the system on different language pairs or domains could be valuable, such as including the romanian language. we previously had an interest for processing romanian language speech using whisper @xcite . overall, this work contributes to the development of automatic subtitling systems that ensure accessibility and comprehension for diverse audiences.