265 citations · 337 across the 10 of their papers we have counts for
10 papers
Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach
Siqi Li, Danni Liu, Jan Niehues
Direct speech translation (ST) models often struggle with rare words. Incorrect translation of these words can have severe consequences, impacting translation quality and user trus…
Augmenting Automatic Speech Recognition Models with Disfluency Detection
Robin Amann, Zhaolin Li, Barbara Bruno +1
Speech disfluency commonly occurs in conversational and spontaneous speech. However, standard Automatic Speech Recognition (ASR) models struggle to accurately recognize these disfl…
Quality Estimation with -nearest Neighbors and Automatic Evaluation for Model-specific Quality Estimation
Tu Anh Dinh, Tobias Palzer, Jan Niehues
Providing quality scores along with Machine Translation (MT) output, so-called reference-free Quality Estimation (QE), is crucial to inform users about the reliability of the trans…
Language-Independent Representations Improve Zero-Shot Summarization
Vladimir Solovyev, Danni Liu, Jan Niehues
Finetuning pretrained models on downstream generation tasks often leads to catastrophic forgetting in zero-shot conditions. In this work, we focus on summarization and tackle the p…
Audience-specific Explanations for Machine Translation
Renhan Lou, Jan Niehues
In machine translation, a common problem is that the translation of certain words even if translated can cause incomprehension of the target language audience due to different cult…
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…