In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Wordin-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the crosslingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: https://github.com/SapienzaNLP/mcl-wic.

SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) / Martelli, Federico; Kalach, Najla; Tola, Gabriele; Navigli, Roberto. - (2021), pp. 24-36. (Intervento presentato al convegno 15th International Workshop on Semantic Evaluation (SemEval-2021) tenutosi a Bangkok, Thailand) [10.18653/v1/2021.semeval-1.3].

SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC)

Federico Martelli;Gabriele Tola;Roberto Navigli
2021

Abstract

In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Wordin-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the crosslingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: https://github.com/SapienzaNLP/mcl-wic.
2021
15th International Workshop on Semantic Evaluation (SemEval-2021)
word-in-context; multilinguality; word sense disambiguation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) / Martelli, Federico; Kalach, Najla; Tola, Gabriele; Navigli, Roberto. - (2021), pp. 24-36. (Intervento presentato al convegno 15th International Workshop on Semantic Evaluation (SemEval-2021) tenutosi a Bangkok, Thailand) [10.18653/v1/2021.semeval-1.3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1604138
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