265 citations · 1.1k across the 50 of their papers we have counts for
60 papers
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Christian Huber, Tu Anh Dinh, Carlos Mullov +10
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…
KIT's Multilingual Speech Translation System for IWSLT 2023
Danni Liu, Thai Binh Nguyen, Sai Koneru +7
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…
Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation
Lena Cabrera, Jan Niehues
Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for wh…
Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages
Zhong Zhou, Jan Niehues, Alex Waibel
In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation en…
Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation
Tu Anh Dinh, Jan Niehues
Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE…
Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation
Danni Liu, Jan Niehues
The cornerstone of multilingual neural translation is shared representations across languages. Given the theoretically infinite representation power of neural networks, semanticall…
Efficient Speech Translation with Pre-trained Models
Zhaolin Li, Jan Niehues
When building state-of-the-art speech translation models, the need for large computational resources is a significant obstacle due to the large training data size and complex model…
Towards continually learning new languages
Ngoc-Quan Pham, Jan Niehues, Alexander Waibel
Multilingual speech recognition with neural networks is often implemented with batch-learning, when all of the languages are available before training. An ability to add new langua…
Adaptive multilingual speech recognition with pretrained models
Ngoc-Quan Pham, Alex Waibel, Jan Niehues
Multilingual speech recognition with supervised learning has achieved great results as reflected in recent research. With the development of pretraining methods on audio and text d…
CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
Peter Polák, Ngoc-Quan Ngoc, Tuan-Nam Nguyen +5
In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without…
LibriS2S: A German-English Speech-to-Speech Translation Corpus
Pedro Jeuris, Jan Niehues
Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the a…
Multilingual Simultaneous Speech Translation
Shashank Subramanya, Jan Niehues
Applications designed for simultaneous speech translation during events such as conferences or meetings need to balance quality and lag while displaying translated text to deliver…
Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniques
Tu Anh Dinh, Danni Liu, Jan Niehues
Recently, end-to-end speech translation (ST) has gained significant attention as it avoids error propagation. However, the approach suffers from data scarcity. It heavily depends o…
Cost-Effective Training in Low-Resource Neural Machine Translation
Sai Koneru, Danni Liu, Jan Niehues
While Active Learning (AL) techniques are explored in Neural Machine Translation (NMT), only a few works focus on tackling low annotation budgets where a limited number of sentence…
Impact of jet-production data on the next-to-next-to-leading-order determination of HERAPDF2.0 parton distributions
H1, ZEUS Collaborations, : +234
The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA da…
Unsupervised Machine Translation On Dravidian Languages
Sai Koneru, Danni Liu, Jan Niehues
Unsupervised neural machine translation (UNMT) is beneficial especially for low resource languages such as those from the Dravidian family. However, UNMT systems tend to fail in re…
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries
Jan Niehues
While recent advances in deep learning led to significant improvements in machine translation, neural machine translation is often still not able to continuously adapt to the envir…
Improving Zero-Shot Translation by Disentangling Positional Information
Danni Liu, Jan Niehues, James Cross +2
Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. Despite being concep…
The Large Hadron-Electron Collider at the HL-LHC
P. Agostini, H. Aksakal, S. Alekhin +333
The Large Hadron electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting ener…
Adapting End-to-End Speech Recognition for Readable Subtitles
Danni Liu, Jan Niehues, Gerasimos Spanakis
Automatic speech recognition (ASR) systems are primarily evaluated on transcription accuracy. However, in some use cases such as subtitling, verbatim transcription would reduce out…
Relative Positional Encoding for Speech Recognition and Direct Translation
Ngoc-Quan Pham, Thanh-Le Ha, Tuan-Nam Nguyen +5
Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for…
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
Danni Liu, Gerasimos Spanakis, Jan Niehues
Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on qualit…
Machine Translation with Unsupervised Length-Constraints
Jan Niehues
We have seen significant improvements in machine translation due to the usage of deep learning. While the improvements in translation quality are impressive, the encoder-decoder ar…
Low Latency ASR for Simultaneous Speech Translation
Thai Son Nguyen, Jan Niehues, Eunah Cho +6
User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques f…