Papers by Dario Stojanovski
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation (2020.coling-main)
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| Challenge: | Recent high scores on pronoun translation suggest current approaches work well . et al., 2018: are context-aware nmt models learning this task? |
| Approach: | They propose a test set to assess the ability to handle specific steps for pronoun translation . they propose heuristics that break down when translations require real reasoning . |
| Outcome: | The proposed model can model complex inferences required for translation of english into german . it shows that current approaches are not able to model all of this information well . |
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation (2021.naacl-main)
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| Challenge: | Existing methods for unsupervised neural machine translation (UNMT) use cross-lingual pretraining to align the lexical- and high-level representations of two languages. |
| Approach: | They propose to use type-level cross-lingual subword embeddings to enhance the bilingual masked language model pretraining with lexical-level information to align the two languages. |
| Outcome: | Empirical results show that the method improves on UNMT (up to 4.5 BLEU) and bilingual lexicon induction compared to baseline models. |
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT (2020.emnlp-main)
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| Challenge: | Neural machine translation (NMT) models with limited data are ineffective when the two languages are not available for one language. |
| Approach: | They propose an approach that reuses a language model that is pretrained on two languages with large monolingual data to initialize an unsupervised neural machine translation system. |
| Outcome: | The proposed method outperforms a competitive cross-lingual pretraining model in English-Macedonian (En-Mk) and English-Albanian (En Sq) it yields more than +8.3 BLEU points for all four translation directions. |