SEAS: Sentence Extraction and Alignment from Subtitles

OmniAlign: A Unified Multilingual Aligner for Word and Sentence Alignment

Surprisingly, later-stage supervised fine-tuning on short texts further improves alignment quality while retaining the long-context understanding acquired in earlier training, keeping the model robust on long-text word alignment.

Scaling Unsupervised Word Alignment to Documents via Structural Constraints

CTFAlign is introduced, a lightweight, training-free approach for document-level word alignment that applies a coarse-to-fine refinement strategy that restricts the alignment search space to semantically similar regions and introduces MDPAlign, a simpler alternative that constrains alignments by position with a main diagonal prior.

Automated Multilingual Translator Using Neural Translation

The results indicate that a moderately sized, shared self-attention architecture can deliver production-quality multilin-gual translation within the resource constraints of an academic de-ployment, while surfacing clear directions – low-resource language coverage, domain adaptation, and speech-based extension – for con-tinued development.

Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources

Leveraging LLMs to Automatically Construct WordNets as Bilingual Resources

This paper proposes automated methods to construct high-quality WordNets using large language models (LLMs) to generate missing lemmas to address the synset shortfall in non-English and low-resource languages.

Gloss-to-Text Translation for Libras and Portuguese: Evaluating Pretrained and Fine-Tuned Encoder-Decoder Models

We evaluate encoder-decoder models for Gloss-to-Text translation from Brazilian Sign Language (Libras) glosses into Portuguese using a corpus derived from Libras-UFPel. The evaluated models are mT5-small, mT5-base, Flan-T5-base, and PTT5-v2-base. Experiments were conducted with 5-fold cross-validation and evaluated using BLEU and chrF. All models improved after supervised fine-tuning, with PTT5-v2-base achieving the best overall performance. The results suggest that Portuguese-specialized encoder-decoder models are a promising direction for Gloss-to-Text translation in low-resource settings.