Papers with simultaneous translation
MiSS: An Assistant for Multi-Style Simultaneous Translation (2021.emnlp-demo)
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| Challenge: | MiSS is a multi-style simultaneous translation assistant . it has five key features: high translation accuracy, simultaneous translation, flexibility, and measurable translation quality. |
| Approach: | They propose an assistant system for multi-style simultaneous translation that provides a complete translation experience for machine translation users. |
| Outcome: | The proposed system improves translation efficiency and performance by combining machine translation, grammatical error correction, and interactive edits. |
Cross Attention Augmented Transducer Networks for Simultaneous Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to simultaneous translation are limited by monotonic constraint . a novel architecture for simultaneous translation is proposed . |
| Approach: | They propose a cross attention-augmented transducer for simultaneous translation that optimizes both policies and translation models by expanding target sequences with blank symbols. |
| Outcome: | The proposed architecture achieves better latency-quality trade-offs than state-of-the-art approaches. |
Simultaneous Translation (2020.emnlp-tutorials)
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| 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 . |
SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation (2020.emnlp-demos)
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| Challenge: | SimulEval is an evaluation toolkit for simultaneous text and speech translation. |
| Approach: | They propose a server-client scheme for simultaneous translation that uses server input and client policies to evaluate models. |
| Outcome: | The proposed evaluation toolkit is available for both text and speech translation. |
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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| 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. |
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++ (2020.aacl-main)
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| Challenge: | Slot-filling, Translation, Intent classification, and Language identification (STIL) are tasks for multilingual Natural Language Understanding (NLU) . |
| Approach: | They propose to perform simultaneous slot filling and translation into a single output language (English in this case). |
| Outcome: | The proposed task performs better than the current state-of-the-art system for the languages tested, but with lower intent classification accuracy and lower slot F1 . |
Stream-level Latency Evaluation for Simultaneous Machine Translation (2021.findings-emnlp)
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| Challenge: | Simultaneous machine translation systems need to find a trade-off between translation quality and response time. |
| Approach: | They propose to adapt existing translation latency measures to streaming scenarios by re-segmenting the output translation to take into account sequential nature of streaming scenarios. |
| Outcome: | The proposed measures are evaluated on a streaming task on simulated speech translation systems. |
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation (N18-2)
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| 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. |
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. |
Speculative Beam Search for Simultaneous Translation (D19-1)
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| Challenge: | Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial. |
| Approach: | They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model. |
| Outcome: | The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search. |
Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation (2022.findings-emnlp)
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| Challenge: | Existing methods to balance source and target information at the token level are limited by the number of received source tokens. |
| Approach: | They propose a Wait-info Policy to balance source and target at the information level . they quantify the amount of info contained in each token and compare it with previous outputs . |
| Outcome: | The proposed method outperforms baselines under and achieves better balance . it is based on comparisons between the total info of previous target outputs and received source inputs . |
Learning Adaptive Segmentation Policy for Simultaneous Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that adaptive segmentation policies for simultaneous translation are more accurate than current methods . if translation starts before adequate source content is delivered, the quality of translation degrades . waiting for too much source text increases latency, which would hurt accuracy . |
| Approach: | They propose a new adaptive segmentation policy for simultaneous translation based on human interpreters . it learns to segment the source text by considering possible translations produced by the translation model . |
| Outcome: | Experimental results show that the proposed method achieves better accuracy-latency trade-off over state-of-the-art methods. |
Beyond Sentence-Level End-to-End Speech Translation: Context Helps (2021.acl-long)
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| Challenge: | Document-level contextual information has shown benefits to text-based machine translation, but whether and how it helps end-to-end speech translation is still under-studied. |
| Approach: | They propose a concatenation-based ST model with adaptive feature selection for computational efficiency. |
| Outcome: | The proposed model improves translation quality and robustness to (artificial) audio segmentation errors. |
Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)
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| Challenge: | Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process . |
| Approach: | They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones . |
| Outcome: | Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency. |
Self-training Reduces Flicker in Retranslation-based Simultaneous Translation (2023.eacl-main)
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| Challenge: | Existing approaches to reduce flicker in simultaneous translation have increased the latency through masking and specialised inference, thus losing the simplicity of the approach. |
| Approach: | They propose to train a machine translation system to reduce flicker by controlling monotonicity and biased beam search to achieve the same flicker-latency tradeoff. |
| Outcome: | The proposed approach reduces flicker by controlling monotonicity while maintaining similar translation quality to the original. |
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. |
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework (2020.findings-emnlp)
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Mingbo Ma, Baigong Zheng, Kaibo Liu, Renjie Zheng, Hairong Liu, Kainan Peng, Kenneth Church, Liang Huang
| Challenge: | Text-to-speech synthesis (TTS) has seen rapid progress in recent years, but still suffers from latencies. |
| Approach: | They propose a neural incremental TTS approach that synthesizes speech in an online fashion, playing a segment of audio while generating the next. |
| Outcome: | Experiments on English and Chinese TTS show that the proposed approach achieves similar speech naturalness compared to full sentence TTS, but with a constant (1-2 words) latency. |
SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)
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| Challenge: | SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages . |
| Approach: | They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. |
| Outcome: | The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model. |
From Simultaneous to Streaming Machine Translation by Leveraging Streaming History (2022.acl-long)
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| Challenge: | Streaming MT is an extension of simultaneous MT to the incremental translation of a continuous input text stream. |
| Approach: | They propose to extend simultaneous machine translation to streaming setups by leveraging streaming history. |
| Outcome: | The proposed system compares favorably to the best performing systems on IWSLT Translation Tasks. |
A Generative Framework for Simultaneous Machine Translation (2021.emnlp-main)
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| 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. |
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous Translation (2024.emnlp-main)
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| Challenge: | Current fine-tuning methods to adapt LLMs for simultaneous translation suffer from several issues, such as unnecessarily expanded training sets, increased prompt sizes, or restriction to a single decision policy. |
| Approach: | They propose a new paradigm for fine-tuning large language models for simultaneous translation using an attention mask approach. |
| Outcome: | The proposed model improves translation quality compared to state-of-the-art models on five language pairs while reducing the computational cost. |