Sapienza NLP @ LREC-COLING 2024
2 papers at LREC-COLING!
We are glad to announce that we have 2 paper accepted at LREC-COLING 2024!
We are in Torino to present our works about Latin WSD and homonymy disambiguation.
Here we detail our publications:
Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin
by I. Ghinassi, S. Tedeschi, P. Marongiu, R. Navigli, B. McGillivray
Word Sense Disambiguation (WSD) is an important task in NLP, which serves the purpose of automatically disambiguating a polysemous word with its most likely sense in context. Recent studies have advanced the state of the art in this task, but most of the work has been carried out on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. Although the problem with low-resource languages has recently been mitigated by using existing multilingual resources to propagate otherwise expensive annotations from English to other languages, such techniques have hitherto not been applied to historical languages such as Latin. In this work, we make the following two major contributions. First, we test such a strategy on a historical language and propose a new approach in this framework which makes use of existing bilingual corpora instead of native English datasets. Second, we fine-tune a Latin WSD model on the data produced and achieve state-of-the-art results on a standard benchmark for the task. Finally, we release the dataset generated with our approach, which is the largest dataset for Latin WSD to date. This work opens the door to further research, as our approach can be used for different historical and, generally, under-resourced languages.
Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models
by L. Proietti, S. Perrella, S. Tedeschi, G. Vulpis, L. Lavalle, A. Sanchietti, A. Ferrari, R. Navigli
Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP), aiming to assign the correct meaning (sense) to a word in context. However, traditional WSD systems rely on WordNet as the underlying sense inventory, often differentiating meticulously between subtle nuances of word meanings, which may lead to excessive complexity and reduced practicality of WSD systems in today’s NLP. Indeed, current Pretrained Language Models (PLMs) do seem to be able to perform disambiguation, but it is not clear to what extent, or to what level of granularity, they actually operate. In this paper, we address these points and, firstly, introduce a new large-scale resource that leverages homonymy relations to systematically cluster WordNet senses, effectively reducing the granularity of word senses to a very coarse-grained level; secondly, we use this resource to train Homonymy Disambiguation systems and investigate whether PLMs are inherently able to differentiate coarse-grained word senses. Our findings demonstrate that, while state-of-the-art models still struggle to choose the correct fine-grained meaning of a word in context, Homonymy Disambiguation systems are able to differentiate homonyms with up to 95% accuracy scores even without fine-tuning the underlying PLM. We release our data and code at https://github. com/SapienzaNLP/homonymy-wsd.