Word2Vec vs DBnary ou comment (r\'e)concilier repr\'esentations distribu\'ees et r\'eseaux lexico-s\'emantiques ? Le cas de l'\'evaluation en traduction automatique (Word2Vec vs DBnary or how to bring back together vector representations and lexical resources ? A case study for machine translation evaluation)
Cet article pr{\'e}sente une approche associant r{\'e}seaux lexico-s{\'e}mantiques et repr{\'e}sentations distribu{\'e}es de mots appliqu{\'e}e {\a} l{'}{\'e}valuation de la traduction automatique. Cette {\'e}tude est faite {\a} travers l{'}enrichissement d{'}une m{\'e}trique bien connue pour {\'e}valuer la traduction automatique (TA) : METEOR. METEOR permet un appariement approch{\'e} (similarit{\'e} morphologique ou synonymie) entre une sortie de syst{\e}me automatique et une traduction de r{\'e}f{\'e}rence. Nos exp{\'e}rimentations s{'}appuient sur la t{\^a}che Metrics de la campagne d{'}{\'e}valuation WMT 2014 et montrent que les repr{\'e}sentations distribu{\'e}es restent moins performantes que les ressources lexico-s{\'e}mantiques pour l{'}{\'e}valuation en TA mais peuvent n{\'e}ammoins apporter un compl{\'e}ment d{'}information int{\'e}ressant {\a} ces derni{\`e}res.
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