Inspicio is an open‑vocabulary pipeline that links tokens in historical or low‑resource languages to synsets in the Open English WordNet without needing a source‑language sense inventory. It uses an instruction‑tuned LLM to generate two English translations, candidate dictionary definitions, and English lemmas, then performs hybrid retrieval combining dense definition similarity, sparse lemma matching, and Maximal Marginal Relevance re‑ranking. Evaluated on Latin, Ancient Greek, PREMOVE, and Italian data, the best configuration achieves 96% Recall@50 on a perception‑verb test set and remains competitive in out‑of‑domain and cross‑lingual scenarios.
By Michele Ciletti
Word Sense Disambiguation has advanced rapidly for English and a handful of well-resourced modern languages, but it continues to assume the existence of a sense inventory and a word-to-sense mapping i...
The paper investigates cross‑lingual transfer for sequential sentence classification (SSC) in research papers, focusing on 13 non‑English languages. Experiments show that linguistic proximity does not reliably predict transfer success, whereas structural similarity in rhetorical organization—particularly label distribution similarity—correlates positively with performance. The authors introduce three generative‑model methods that exploit structural cues, achieving parity with strong encoder baselines on‑domain and outperforming them when transferring to unseen languages.
By Kazuhiro Yamauchi, Marie Katsurai
arXiv:2607.00171v2 Announce Type: replace
Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...
By Andrianos Michail, Stylianos Psychias, Michelle Wastl, Simon Clematide, Rico Sennrich, Juri Opitz
The paper explores how to improve literary machine translation by using datasets that contain multiple valid translations of the same source text. It introduces a filtering framework that selects source texts whose references show meaningful variation while staying faithful, based on semantic similarity. Experiments show that fine‑tuning on medium to high similarity data outperforms low similarity data, and that using only this filtered subset can match or exceed performance achieved with the full unfiltered set. Additionally, the study compares synthetic translations generated by large language models with human expert translations, finding that fine‑tuning on human expert data yields better results in both automatic metrics and human evaluations, underscoring the continued importance of expert translations for literary MT.
By Si Wu, John Wieting, David A. Smith
arXiv:2606.13218v2 Announce Type: replace
Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and m...
By Junhong Liang, Noor Abo Mokh, Bashar Alhafni