TL;DR
A system that associates a fixed list of 50 words with each WordNet synset, and computes several scores for each of the phrases in the story, to determine how closely the phrase matches the wordlist is inappropriate for this dataset.
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.
It is found that providing candidate senses, similar to what is done in traditional WSD, improves WiC performance in all settings and helps models make more consistent and targeted judgements.
Ufakzeka-1, a 151M-parameter (182M with embeddings) decoder-only Turkish language model pretrained from scratch on 13.5B tokens of openly licensed text and instruction-tuned for chat, is described and three findings are believed to transfer to other small-model efforts.
Terminology-aware translation asks for more than a correct translation: the output must use the exact terms a glossary prescribes. The standard recipe, fine-tuning on glossary-annotated translation pairs, hides an inefficiency: for most examples the glossary prescribes exactly what the model would have produced anyway,...
Inspicio is presented, an open-vocabulary retrieval pipeline that links tokens in context to synsets of the Open English WordNet (McCrae et al., 2020) without requiring any source-language inventory or mapping.
Word Error Rate (WER), the most commonly used metric for Automatic Speech Recognition (ASR), treats every lexical deviation from the reference as equally costly, regardless of whether it changes meaning. This raises the question: does WER actually track how humans judge ASR transcript quality? We introduce HATS-en, an...
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