Papers with English-Turkish
Quantifying Synthesis and Fusion and their Impact on Machine Translation (2022.naacl-main)
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| Challenge: | Literature in Natural Language Processing (NLP) typically labels whole language with strict type of morphology, e.g. fusional or agglutinative. |
| Approach: | They propose to quantify morphological typology at the word and segment level by using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). |
| Outcome: | The proposed method reduces the rigidity of NLP classification claims by measuring morphological diversity at the word and segment level. |
Enhancing Machine Translation with Dependency-Aware Self-Attention (2020.acl-main)
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| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
| Approach: | They propose a parameter-free, dependency-aware self-attention mechanism that integrates syntactic knowledge into a Transformer model and propose 'a parameter free approach' they also propose - a novel mechanism that improves translation quality for long sentences and in low-resource scenarios. |
| Outcome: | The proposed approach improves translation quality on English-German and English-Turkish translation tasks and in low-resource scenarios. |