Papers with PBSMT
UCSMNLP: Statistical Machine Translation for WAT 2019 (D19-52)
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| Challenge: | UCSMNLP submitted to WAT 2019 Translation Tasks focusing on Myanmar-English translation. |
| Approach: | They propose to use Name Entity Recognition corpus and bilingual dictionary to build phrase based statistical machine translation system using listwise reranking process and initial distortion weight is changed to improve translation quality. |
| Outcome: | The proposed system outperforms the baseline system in the Myanmar-English translation task. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
| Approach: | They propose a neural machine translation approach that generates equivalent sentences in target language and source language. |
| Outcome: | The proposed approach outperforms phrase-based statistical machine translation systems for some pairs of languages. |
The Transference Architecture for Automatic Post-Editing (2020.coling-main)
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| Challenge: | A research challenge is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions. |
| Approach: | They propose a multi-encoder based neural APE model that conditions post-editing decisions on both the source and machine translated text as inputs. |
| Outcome: | The proposed model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English–German APE shared tasks. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |