HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation (D19-1)
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| Challenge: | Existing methods to incorporate bilingual lexicons into statistical machine translation are unclear how to do so in the neural framework. |
| Approach: | They present a dataset to test methods for bilingual lexicon integration into neural machine translation using human generated alignments of words and phrases in three language pairs. |
| Outcome: | The proposed method improves on baselines and improves training to address overfitting. |
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| Challenge: | Existing methods for bilingual lexicon induction use nearest neighbor or related retrieval methods to induce word translation pairs. |
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| Challenge: | Existing methods for bilingual lexicon induction are linear and require simplifying assumptions. |
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| Challenge: | Using bilingual lexicons for low-resource languages can improve machine translation for low resource languages. |
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| Challenge: | Recent advances in neural machine translation have led to astonishing translation quality of research systems. |
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