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20202026
most citedImproving Fluency of Non-Autoregressive Machine Translation

6 citations · 7 across the 5 of their papers we have counts for

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cs.CL2026

Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors

Adnan Al Ali, Jindřich Helcl, Jindřich Libovický

LLM-based assistants have been widely popularised after the release of ChatGPT. Concerns have been raised about their misuse in academia, given the difficulty of distinguishing bet…

cs.CL2023★ 1 cited

OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models

Nikolay Bogoychev, Jelmer van der Linde, Graeme Nail +7

Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and…

cs.CL2023

CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval

Jindřich Helcl, Jindřich Libovický

We present the Charles University system for the MRL~2023 Shared Task on Multi-lingual Multi-task Information Retrieval. The goal of the shared task was to develop systems for name…

cs.CL2022

CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task

Jindřich Helcl

We present a non-autoregressive system submission to the WMT 22 Efficient Translation Shared Task. Our system was used by Helcl et al. (2022) in an attempt to provide fair comparis…

cs.CL2020★ 6 cited

Improving Fluency of Non-Autoregressive Machine Translation

Zdeněk Kasner, Jindřich Libovický, Jindřich Helcl

Non-autoregressive (nAR) models for machine translation (MT) manifest superior decoding speed when compared to autoregressive (AR) models, at the expense of impaired fluency of the…