JHU IWSLT 2023 Multilingual Speech Translation System Description (2023)

Abstract: We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.

## introduction in this work, we describe the 2023 jhu 60-60 multilingual speech translation track submissions and their development @xcite . this multilingual task involved the translation of acl conference oral presentations, given in english, into 10 different target languages. high quality translation systems that can assist in translating highly technical and scientific information helps in the dissemination of knowledge to more people, which in turn can help make our field more inclusive and accessible. we briefly describe the task in section 2. in section 3 we describe the collection and preparation of in-domain acl data to improve asr and mt performance by addressing the domain-specificity of the task. we then describe our systems in section 4, including their motivation and design in context of this shared task. technical details of our experiments are in 5. we present our results and a discussion of our contributions in section 6. in 2022, the acl began the 60-60 initiative, a diversity and inclusion initiative to translate the acl anthology into 60 languages for its 60th anniversary. the initiative provided evaluation data for the iwslt 2023 multilingual track on speech translation of talks from english into 10 major languages. it was further split into constrained and unconstrained subtracks. the constrained subtrack allowed the use of only certain datasets and pretrained models, whereas the unconstrained subtrack had no such restrictions. we submitted systems to both subtracks and describe them in section 4. ## systems in this section, we separately describe our unconstrained and constrained submissions. since we built cascaded models, we describe the automatic speech recognition (asr) and machine translation (mt) components of each system. ## experimental setup in this section, we provide technical details of our experiments and our evaluation practices. ## conclusion we present a constrained and unconstrained system for the iwslt 2023 multilingual speech translation task. we address some of the major challenges of this dataset with our design choices: asr robust to speaker accents, adaptation to match the domain specificity, and asr prompting to incorporate context in this academic talk-level translation task. we additionally release a supplemental acl audio and text corpus to encourage further work in high quality speech translation of acl content.