The Automatic Determination of Translation Equivalents in Lexicography: What Works and What Doesn’t?
| Authors | |
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| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Proceedings of the XXI EURALEX International Congress |
| MU Faculty or unit | |
| Citation | |
| Web | Plný text |
| Keywords | Translation equivalent determination; Cross-lingual embedding models; Evaluation |
| Description | Cross-lingual embedding models act as facilitator of lexical knowledge transfer and offer many advantages, notably their applicability to low-resource and non-standard language pairs, making them a valuable tool for retrieving translation equivalents in lexicography. Despite their potential, these models have primarily been developed with a focus on Natural Language Processing (NLP), leading to significant issues, including flawed training and evaluation data, as well as inadequate evaluation metrics and procedures. In this paper, we introduce cross-lingual embedding models for lexicography, addressing the challenges and limitations inherent in the current NLP-focused research. We demonstrate the problematic aspects across three baseline cross-lingual embedding models and three language pairs and outline possible solutions. We show the importance of high-quality data, advocating that its role is vital compared to algorithmic optimisation in enhancing the effectiveness of these models. |
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