Non-adversarial unsupervised word translation

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

תקציר

Unsupervised word translation from nonparallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Extensive experiments on word translation of European and Non-European languages show that our method achieves better performance than recent state-of-the-art deep adversarial approaches and is competitive with the supervised baseline. It is also efficient, easy to parallelize on CPU and interpretable.

שפה מקוריתאנגלית אמריקאית
כותר פרסום המארחProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
עורכיםEllen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
עמודים469-478
מספר עמודים10
מסת"ב (אלקטרוני)9781948087841
סטטוס פרסוםפורסם - 2018
אירוע2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, בלגיה
משך הזמן: 31 אוק׳ 20184 נוב׳ 2018

סדרות פרסומים

שםProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

כנס

כנס2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
מדינה/אזורבלגיה
עירBrussels
תקופה31/10/184/11/18

ASJC Scopus subject areas

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