Papers by Ashkan Alinejad
Effectively pretraining a speech translation decoder with Machine Translation data (2020.emnlp-main)
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| Challenge: | Existing approaches to improve the performance of AST systems are based on pretraining the encoder parameters using an ASR model, but using a pretrained MT decoder is not beneficial or improves the results. |
| Approach: | They propose to use an adversarial regularizer to bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities. |
| Outcome: | The proposed model can be pre-trained using the Automatic Speech Recognition (ASR) task even in different languages and improves in low resource settings. |
Prediction Improves Simultaneous Neural Machine Translation (D18-1)
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| Challenge: | Current systems for simultaneous machine translation use an AGENT to control an incremental encoder-decoder model. |
| Approach: | They propose a general-purpose prediction action which predicts future words in the input stream. |
| Outcome: | The proposed agent with prediction has better translation quality and less delay compared to an agent-based system without prediction. |
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to train simultaneous machine translation agents have been used to find the optimal action sequences for translation quality and lag. |
| Approach: | They propose a supervised learning approach that detects minimum reads required for generating target tokens by comparing simultaneous translations against full-sentence translations. |
| Outcome: | The proposed method produces much higher quality translations while minimizing the average lag in simultaneous translation. |