Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task
FST: نظام ترجمة الكلام العادلة للمهمة المشتركة متعددة اللغات IWSLT21
355
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نصف نظام ترجمة الكلام متعددة اللغات نهاية إلى نهاية المقدمة إلى حملة تقييم IWSLT 2021 في مهمة مشتركة من خطابات الكلام متعددة اللغات. بنيت نظامنا من خلال الاستفادة من التعلم النقل عبر الطرائق والمهام واللغات. أولا، نحن نستفيد الوحدات متعددة اللغات للأغراض العامة مسببة اللغات مع كميات كبيرة من البيانات غير المسماة والمصدرة. ونحن كذلك تمكين نقل المعرفة من مهمة النص إلى مهمة خطاب من خلال التدريب بمهامتين بالاشتراك. أخيرا، يتم تصوير نموذجنا متعدد اللغات في البيانات الخاصة ببيانات المهام الخاصة بترجمة الكلام لتحقيق أفضل نتائج الترجمة. تظهر النتائج التجريبية أن نظامنا يتفوق على الأنظمة المبلغ عنها، بما في ذلك النهج القائمة على المناسبة والمتوسطة، بتهامش كبير. في بعض اتجاهات الترجمة، تعد نتائج ترجمة الكلام التي تم تقييمها على مجموعة اختبار TEDX متعددة اللغات متعددة اللغات مقارنة مع تلك الموجودة من نظام ترجمة نصية قوية للنص، والذي يستخدم النصوص أوراكل الكلام كإدخال.
In this paper, we describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign on the Multilingual Speech Translation shared task. Our system is built by leveraging transfer learning across modalities, tasks and languages. First, we leverage general-purpose multilingual modules pretrained with large amounts of unlabelled and labelled data. We further enable knowledge transfer from the text task to the speech task by training two tasks jointly. Finally, our multilingual model is finetuned on speech translation task-specific data to achieve the best translation results. Experimental results show our system outperforms the reported systems, including both end-to-end and cascaded based approaches, by a large margin. In some translation directions, our speech translation results evaluated on the public Multilingual TEDx test set are even comparable with the ones from a strong text-to-text translation system, which uses the oracle speech transcripts as input.
References used
https://aclanthology.org/
rate research
Read More
This paper describes Maastricht University's participation in the IWSLT 2021 multilingual speech translation track. The task in this track is to build multilingual speech translation systems in supervised and zero-shot directions. Our primary system
is an end-to-end model that performs both speech transcription and translation. We observe that the joint training for the two tasks is complementary especially when the speech translation data is scarce. On the source and target side, we use data augmentation and pseudo-labels respectively to improve the performance of our systems. We also introduce an ensembling technique that consistently improves the quality of transcriptions and translations. The experiments show that the end-to-end system is competitive with its cascaded counterpart especially in zero-shot conditions.
This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is
unconstrained and the latter two are fully constrained. Our model submissions to the shared task were initialized with DeltaLM, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the vast collected parallel data and allowed data sources according to track settings, together with applying progressive learning and iterative back-translation approaches to further improve the performance. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign. Our main approach is to develop both cascade and end-to-end systems an
d eventually combine them together to achieve the best possible results for this extremely low-resource setting. The report also confirms certain consistent architectural improvement added to the Transformer architecture, for all tasks: translation, transcription and speech translation.
In this paper, we describe Zhejiang University's submission to the IWSLT2021 Multilingual Speech Translation Task. This task focuses on speech translation (ST) research across many non-English source languages. Participants can decide whether to work
on constrained systems or unconstrained systems which can using external data. We create both cascaded and end-to-end speech translation constrained systems, using the provided data only. In the cascaded approach, we combine Conformer-based automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems use ASR pretrained encoder and multi-task decoders. The submitted systems are ensembled by different cascaded models.
This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription. We use the Transformer-based
model architecture and enhance it by Conformer, relative position encoding, and stacked acoustic and textual encoding. To augment the training data, the English transcriptions are translated to German translations. Finally, we employ ensemble decoding to integrate the predictions from several models trained with the different datasets. Combining these techniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which shows the enormous potential of the end-to-end model.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
