Ashkan Alinejad


2021

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Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation
Ashkan Alinejad | Hassan S. Shavarani | Anoop Sarkar
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.

2020

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Effectively pretraining a speech translation decoder with Machine Translation data
Ashkan Alinejad | Anoop Sarkar
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Directly translating from speech to text using an end-to-end approach is still challenging for many language pairs due to insufficient data. Although pretraining the encoder parameters using the Automatic Speech Recognition (ASR) task improves the results in low resource settings, attempting to use pretrained parameters from the Neural Machine Translation (NMT) task has been largely unsuccessful in previous works. In this paper, we will show that by using an adversarial regularizer, we can bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities, and how this helps us effectively use a pretrained NMT decoder for speech translation.

2018

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Simultaneous Translation using Optimized Segmentation
Maryam Siahbani | Hassan Shavarani | Ashkan Alinejad | Anoop Sarkar
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Prediction Improves Simultaneous Neural Machine Translation
Ashkan Alinejad | Maryam Siahbani | Anoop Sarkar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation. We train this agent using reinforcement learning with a novel reward function. Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.