activity
20222025
most citedHow to Engage Your Readers? Generating Guiding Questions to Promote Active Reading

1 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Preliminary Ranking of WMT25 General Machine Translation Systems

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden +25

We present the preliminary rankings of machine translation (MT) systems submitted to the WMT25 General Machine Translation Shared Task, as determined by automatic evaluation metric…

cs.CL2024

Distributional Properties of Subword Regularization

Marco Cognetta, Vilém Zouhar, Naoaki Okazaki

Subword regularization, used widely in NLP, improves model performance by reducing the dependency on exact tokenizations, augmenting the training corpus, and exposing the model to…

cs.CL20241 cited

How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading

Peng Cui, Vilém Zouhar, Xiaoyu Zhang +1

Using questions in written text is an effective strategy to enhance readability. However, what makes an active reading question good, what the linguistic role of these questions is…

cs.CL2024

Two Counterexamples to Tokenization and the Noiseless Channel

Marco Cognetta, Vilém Zouhar, Sangwhan Moon +1

In Tokenization and the Noiseless Channel (Zouhar et al., 2023a), Rényi efficiency is suggested as an intrinsic mechanism for evaluating a tokenizer: for NLP tasks, the tokenizer w…

cs.CL2024

AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails

Sankalan Pal Chowdhury, Vilém Zouhar, Mrinmaya Sachan

Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of u…

cs.CL2024

Stolen Subwords: Importance of Vocabularies for Machine Translation Model Stealing

Vilém Zouhar

In learning-based functionality stealing, the attacker is trying to build a local model based on the victim's outputs. The attacker has to make choices regarding the local model's…