Papers by Raymond Hendy Susanto
Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to incorporate lexical constraints in neural machine translation have been unsuccessful . |
| Approach: | They propose an algorithm that incorporates lexical constraints into neural machine translation. |
| Outcome: | The proposed method improves on English-German datasets without modification . it does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. |
Can Automatic Post-Editing Improve NMT? (2020.emnlp-main)
Copied to clipboard
| Challenge: | APE has been successful with statistical machine translation systems but has not been as successful over neural machine translation (NMT) systems. |
| Approach: | They propose to train neural APE models on a corpus of human post-edits of NMT and compile a larger corpus to test their hypothesis. |
| Outcome: | The proposed model can improve a strong in-domain NMT system, challenging the current understanding in the field. |
Sarah’s Participation in WAT 2019 (D19-52)
Copied to clipboard
| Challenge: | Using the Transformer architecture, we trained similar systems across different tasks. |
| Approach: | They presented their results in the 6th Workshop on Asian Translation (WAT) translation task and their submissions to the task. |
| Outcome: | The proposed models perform better on tasks with smaller datasets and with smaller heads on multilingual datasets. |