4 citations · 7 across the 11 of their papers we have counts for
11 papers
BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion
Sai Koneru, Fabian Retkowski, Christian Huber +5
The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge. Lecture materials are inherently multimodal, combi…
OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion
Sai Koneru, Matthias Huck, Jan Niehues
There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. However, these models can be only u…
KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025
Sai Koneru, Maike Züfle, Thai-Binh Nguyen +3
The scope of the International Workshop on Spoken Language Translation (IWSLT) has recently broadened beyond traditional Speech Translation (ST) to encompass a wider array of tasks…
Quality-Aware Decoding: Unifying Quality Estimation and Decoding
Sai Koneru, Matthias Huck, Miriam Exel +1
Quality Estimation (QE) models for Neural Machine Translation (NMT) predict the quality of the hypothesis without having access to the reference. An emerging research direction in…
Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies
Sai Koneru, Matthias Huck, Miriam Exel +1
Recent advancements in NLP have resulted in models with specialized strengths, such as processing multimodal inputs or excelling in specific domains. However, real-world tasks, lik…
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024
Sai Koneru, Thai-Binh Nguyen, Ngoc-Quan Pham +4
Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech T…
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing
Sai Koneru, Miriam Exel, Matthias Huck +1
Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks, but they have yet to attain state-of-the-art performance in Neura…
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…
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…
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…