| 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. |
Similar Papers
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| 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. |
A Generative Framework for Simultaneous Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches use a fixed number of source words to translate or learn dynamic policies for the number of sources by reinforcement learning. |
| Approach: | They propose a generative framework that uses a latent variable to model read or translate actions at every time step and integrates out to consider all possible translation policies. |
| Outcome: | The proposed framework achieves the best BLEU scores on benchmark datasets. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
Simultaneous Translation (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Anticipating Future with Large Language Model for Simultaneous Machine Translation (2025.naacl-long)
Copied to clipboard
Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
| Challenge: | Existing methods only use the partial utterance that has already arrived at the input and the generated hypothesis. |
| Approach: | They propose to use a large language model to predict future source words and opportunistically translate without introducing too much risk. |
| Outcome: | The proposed method outperforms baselines on four language directions and achieves the best translation quality-latency trade-off by up to 5 BLEU points at the same latency. |
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to simultaneous translation have been limited and use fixed-latency policies or a complicated two-staged model. |
| Approach: | They propose a single model that adds a “delay” token to the target vocabulary and a restricted dynamic oracle to greatly simplify training. |
| Outcome: | The proposed model achieves better BLEU scores and lower latencies compared to fixed and RL-learned policies on Chinese -> English simultaneous translation. |
Tied Multitask Learning for Neural Speech Translation (N18-1)
Copied to clipboard
| Challenge: | Recent efforts in endangered language documentation focus on collecting spoken language resources . BULB project uses mobile app to collect spoken resources accompanied by spoken translations . |
| Approach: | They propose a model where the second task decoder receives information from the first task . they apply regularization that encourages transitivity and invertibility to the model . |
| Outcome: | The proposed model improves performance on low-resource speech transcription and translation tasks. |
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work has shown that translating from multiple source languages improves translation quality. |
| Approach: | They propose to exploit multiple source sentences from auxiliary languages to improve multilingual translation in a more common scenario by using synthetic multi-source corpora. |
| Outcome: | Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that the proposed model outperforms the baseline model significantly by +4.0 BLEU. |
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation (N18-2)
Copied to clipboard
| Challenge: | a tunable agent decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. |
| Approach: | They propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and average proportion (AP) constraint. |
| Outcome: | The proposed agent outperforms existing Wait-if-diff and Wait-If-worse agents on BLEU with a lower latency. |
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
Copied to clipboard
| Challenge: | Despite the success of neural machine translation, simultaneous neural machine translators are challenging due to syntactic structure difference and simultaneity requirements. |
| Approach: | They propose a framework for adapting neural machine translation to translate simultaneously . they propose 'prefix translation' that utilizes a consecutive NMT model to translate source prefixes . |
| Outcome: | The proposed framework balancing quality and latency on three translation corpora and two language pairs shows that it performs well. |