Papers with Neural Generation
Proceedings of the 3rd Workshop on Neural Generation and Translation (D19-56)
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| Challenge: | The third workshop on neural generation and translation is held in london . the workshop received 68 submissions from leading minds in the field . |
| Approach: | the third workshop on neural generation and translation is held in london . the workshop will feature four invited talks from leading minds in the field . |
| Outcome: | the third workshop on neural generation and translation is held in london . the conference received 68 submissions from which 36 accepted . |
Findings of the Third Workshop on Neural Generation and Translation (D19-56)
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Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh
| Challenge: | The 3rd Workshop on Neural Machine Translation and Generation (WNGT) was held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). |
| Approach: | They describe the results of the third workshop on Neural Generation and Translation held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). |
| Outcome: | The results of the 3rd Workshop on Neural Machine Translation and Generation (WNGT) were summarized in Sections 3 and 4. |
Monash University’s Submissions to the WNGT 2019 Document Translation Task (D19-56)
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| Challenge: | Despite the boom of work on document-level machine translation in the past two years, there has been a lack of the application of the proposed approaches to MT shared tasks. |
| Approach: | They propose to employ an established document-level neural machine translation model for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019). |
| Outcome: | The proposed model achieves a BLEU score of 39.83 for En-De and 45.06 for De-En translation directions on the Rotowire test set. |
SYSTRAN @ WNGT 2019: DGT Task (D19-56)
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| Challenge: | SYSTRAN participates in Document-level generation and trans-lation (DGT) task . data-to-text generation tasks are difficult because of the content selection and text generation data. |
| Approach: | They propose a Transformer-based datato-text generation model which jointly learns content selection and text generation. |
| Outcome: | The proposed model outperforms current state-of-the-art system on BLEU, content selection precision and content ordering metics. |