Papers by Baigong Zheng
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)
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| 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. |
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)
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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
| Challenge: | Simultaneous translation is notoriously dif- ficult due to word-order differences. |
| Approach: | They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model. |
| Outcome: | The proposed framework achieves low latency and reasonable qual- ity on 4 directions. |
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. |
Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)
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| Challenge: | Existing approaches to balancing translation quality and latency are either too aggressive or too conservative. |
| Approach: | They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information. |
| Outcome: | The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context . |
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training (2020.findings-emnlp)
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| Challenge: | Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster. |
| Approach: | They propose a method which generates more fluent target speech latency than the baseline . they propose to use self-adaptive translation to adjust the length of translations to accommodate different source speech rates. |
| Outcome: | Xiong et al., 2019) show that the proposed method generates more fluent target speech latency than baseline . authors say it provides more natural communication process than speech-to-text translation . xiong and colleagues say the proposed technique is more efficient than current approaches . |
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. |
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. |