UvA-DARE (Digital Academic Repository) A Distributed Inflection Model for Translating into Morphologically Rich Languages

UvA-DARE (Digital Academic Repository) A Distributed Inflection Model for Translating into Morphologically Rich Languages

This work introduces soft tags, which are under-specified representations retaining all possible morphological attributes of a word, and adopts a neural network approach in order to learn distributed representations for the soft tags and their interactions.

Bridging the linguistic divide: recent developments in machine translation for Indian languages

This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT) and tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of par...

Neural Machine Translation for Moor´e, a Low-Resource Language

Efficient Adaptation of English Language Models for Morphologically Rich and Underrepresented Languages: The Case of Arabic

A resource-efficient adaptation of the English-pretrained ModernBERT for Arabic, employing continued pretraining on large Arabic corpora followed by lightweight head-only fine-tuning with a frozen encoder, demonstrating that modern English encoder architectures can be efficiently transferred to Arabic through language-...

PARSEME 2.0 Multilingual Corpus of Multiword Expressions

We present edition 2.0 of the PARSEME multilingual corpus annotated for multiword expressions (MWEs), resulting from efforts of the PARSEME community towards universality-driven modeling of idiomaticity. With respect to previous editions, we extend the annotation scope to all syntactic MWE categories: verbal, nominal,...

A semi-automated LLM-based framework for word sense disambiguation in Serbian

LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation is combined with a constrained JSON-formatted output to support the practical construction and refinement of sense-annotated resources in a low-resource setting.