pylazaro is an open‑source Python package that automatically extracts unassimilated lexical borrowings—primarily anglicisms—from Spanish text. It provides a unified interface to five sequence‑labeling models trained with different libraries, allowing users to switch models without handling library idiosyncrasies. The package outperforms general‑purpose large language models (F1 <0.40) with its best model achieving an F1 of 0.86, and has been downloaded over 58,000 times, powering the Observatorio Lazaro resource that tracks anglicism usage in Spanish media.
arXiv:2603. 26292v2 Announce Type: replace-cross Abstract: Syllable-level units offer compact and linguistically meaningful representations for spoken language modeling and unsupervised word discovery, but research on syllabification remains fragmented across disparate implementations, datasets, and evaluation protocols.
arXiv:2609.09974v1 Announce Type: new Abstract: Grapheme-to-phoneme conversion (G2P) refers to the task of converting a sequence of graphemes to a corresponding sequence of phonemes. While Filipino G...
arXiv:2610.20279v1 Announce Type: new Abstract: Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications....
MUDIDI is a two-stage framework designed to digitize multilingual dictionaries that are currently only available as scanned images. The first stage assesses character recognition and markup preservation, while the second stage segments dictionary entries and maps them into the SIL Multi-Dictionary Formatter schema. The authors also release a dataset of 30 annotated dictionaries and benchmark OCR, LLM, and VLM systems, finding that LLMs generally outperform others and that providing additional context improves digitization quality.
This paper proposed an algorithm for part-of-speech (POS) tagging senses of a bilingual dictionary. The algorithm is applied on the Al-Mawrid Arabic-English dictionary.
The paper presents a framework that combines human-curated vocabularies with a few human-written exemplars to enable large language models (LLMs) to generate coherent text in extremely low-resource or unseen languages. By sampling candidate words from a Zipfian distribution and providing syntactic signals, the method achieves fluent sentence-level generation and promising paragraph-level results across languages with varying resource levels. The study evaluates output quality using lexical diversity, off-target ratio, and human fluency judgments, demonstrating that reasoning-enabled LLMs can effectively bootstrap synthetic data even when digital resources are scarce.
arXiv:2606.27314v2 Announce Type: replace Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive m...
arXiv:2608.10109v2 Announce Type: replace Abstract: Social media has become a major venue for multilingual communication, where users frequently mix multiple languages within a single utterance. Alth...
arXiv:2011.03783v3 Announce Type: replace-cross Abstract: In this work, we introduce the construction of a machine translation (MT) assisted and human-in-the-loop multilingual parallel corpus with an...
arXiv:2510.10774v4 Announce Type: replace-cross Abstract: Persian remains substantially underrepresented in open speech-text resources, limiting progress in multi-speaker text-to-speech (TTS), speech...
Chronoberg is a 250‑year English book corpus from Project Gutenberg, enriched with temporal annotations and affective lexicons (Valence‑Arousal‑Dominance). It allows researchers to track lexical semantic change over time and to evaluate how large language models handle sentiment and discriminatory language across different historical periods. The authors demonstrate that current LLMs struggle with diachronic shifts, underscoring the need for temporally aware training and evaluation pipelines.
The paper introduces SinLlama, the first decoder‑based open‑source large language model with explicit support for Sinhala. By extending Llama‑3‑8B, adding Sinhala‑specific tokenizer vocabulary, and performing continual pre‑training on a cleaned 10‑million‑token Sinhala corpus, the authors created a model that surpasses both the base and instruction‑fine‑tuned variants of Llama‑3‑8B on three text classification tasks. This work addresses the underrepresentation of low‑resource languages in open‑source LLMs.