Sai Koneru


2023

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End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Christian Huber | Tu Anh Dinh | Carlos Mullov | Ngoc-Quan Pham | Thai Binh Nguyen | Fabian Retkowski | Stefan Constantin | Enes Ugan | Danni Liu | Zhaolin Li | Sai Koneru | Jan Niehues | Alexander Waibel
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The challenge of low-latency speech translation has recently draw significant interest in the research community as shown by several publications and shared tasks. Therefore, it is essential to evaluate these different approaches in realistic scenarios. However, currently only specific aspects of the systems are evaluated and often it is not possible to compare different approaches. In this work, we propose the first framework to perform and evaluate the various aspects of low-latency speech translation under realistic conditions. The evaluation is carried out in an end-to-end fashion. This includes the segmentation of the audio as well as the run-time of the different components. Secondly, we compare different approaches to low-latency speech translation using this framework. We evaluate models with the option to revise the output as well as methods with fixed output. Furthermore, we directly compare state-of-the-art cascaded as well as end-to-end systems. Finally, the framework allows to automatically evaluate the translation quality as well as latency and also provides a web interface to show the low-latency model outputs to the user.

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Analyzing Challenges in Neural Machine Translation for Software Localization
Sai Koneru | Matthias Huck | Miriam Exel | Jan Niehues
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Advancements in Neural Machine Translation (NMT) greatly benefit the software localization industry by decreasing the post-editing time of human annotators. Although the volume of the software being localized is growing significantly, techniques for improving NMT for user interface (UI) texts are lacking. These UI texts have different properties than other collections of texts, presenting unique challenges for NMT. For example, they are often very short, causing them to be ambiguous and needing additional context (button, title text, a table item, etc.) for disambiguation. However, no such UI data sets are readily available with contextual information for NMT models to exploit. This work aims to provide a first step in improving UI translations and highlight its challenges. To achieve this, we provide a novel multilingual UI corpus collection (∼ 1.3M for English German) with a targeted test set and analyze the limitations of state-of-the-art methods on this challenging task. Specifically, we present a targeted test set for disambiguation from English to German to evaluate reliably and emphasize UI translation challenges. Furthermore, we evaluate several state-of-the-art NMT techniques from domain adaptation and document-level NMT on this challenging task. All the scripts to replicate the experiments and data sets are available here.ˆ,

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KIT’s Multilingual Speech Translation System for IWSLT 2023
Danni Liu | Thai Binh Nguyen | Sai Koneru | Enes Yavuz Ugan | Ngoc-Quan Pham | Tuan Nam Nguyen | Tu Anh Dinh | Carlos Mullov | Alexander Waibel | Jan Niehues
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

Many existing speech translation benchmarks focus on native-English speech in high-quality recording conditions, which often do not match the conditions in real-life use-cases. In this paper, we describe our speech translation system for the multilingual track of IWSLT 2023, which focuses on the translation of scientific conference talks. The test condition features accented input speech and terminology-dense contents. The tasks requires translation into 10 languages of varying amounts of resources. In absence of training data from the target domain, we use a retrieval-based approach (kNN-MT) for effective adaptation (+0.8 BLEU for speech translation). We also use adapters to easily integrate incremental training data from data augmentation, and show that it matches the performance of re-training. We observe that cascaded systems are more easily adaptable towards specific target domains, due to their separate modules. Our cascaded speech system outperforms its end-to-end counterpart on scientific talk translation, although their performance remains similar on TED talks.

2021

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Unsupervised Machine Translation On Dravidian Languages
Sai Koneru | Danni Liu | Jan Niehues
Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages

Unsupervised Neural Machine translation (UNMT) is beneficial especially for under-resourced languages such as from the Dravidian family. They learn to translate between the source and target, relying solely on only monolingual corpora. However, UNMT systems fail in scenarios that occur often when dealing with low resource languages. Recent works have achieved state-of-the-art results by adding auxiliary parallel data with similar languages. In this work, we focus on unsupervised translation between English and Kannada by using limited amounts of auxiliary data between English and other Dravidian languages. We show that transliteration is essential in unsupervised translation between Dravidian languages, as they do not share a common writing system. We explore several model architectures that use the auxiliary data in order to maximize knowledge sharing and enable UNMT for dissimilar language pairs. We show from our experiments it is crucial for Kannada and reference languages to be similar. Further, we propose a method to measure language similarity to choose the most beneficial reference languages.