The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

Model interpretability methods are often used to explain NLP model decisions on tasks such
as text classification, where the output space is relatively small. However, when applied to …

Establishing whether language models can use contextual information in a human-plausible
way is important to ensure their safe adoption in real-world settings. However, the questions …

This paper addresses the task of contextual translation using multi-segment models.
Specifically we show that increasing model capacity further pushes the limits of this …

Resolving ambiguities through interaction is a hallmark of natural language, and modeling
this behavior is a core challenge in crafting AI assistants. In this work, we study such …

The study investigates the effectiveness of utilizing multimodal information in Neural
Machine Translation (NMT). While prior research focused on using multimodal data in low …

Numerous AI-assisted scholarly applications have been developed to aid different stages of
the research process. We present an analysis of AI-assisted scholarly writing generated with …

Despite the remarkable advancements in machine translation, the current sentence-level
paradigm faces challenges when dealing with highly-contextual languages like Japanese …

Context-aware Machine Translation aims to improve translations of sentences by
incorporating surrounding sentences as context. Towards this task, two main architectures …

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 …