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20202024
most citedTackling data scarcity in speech translation using zero-shot multilingual machine translation techniques

4 citations · 10 across the 7 of their papers we have counts for

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cs.CL2024

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…

cs.CL2022

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…

cs.CL2022

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…

cs.CL20224 cited

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…

cs.CL20224 cited

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…

cs.CL20211 cited

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…