Papers by Mingbo Ma
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. |
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)
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| Challenge: | Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency. |
| Approach: | They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder . |
| Outcome: | The proposed method achieves substantially better translation quality at similar levels of latency. |
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. |
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 . |
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 . |
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)
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| Challenge: | Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available. |
| Approach: | They propose an algorithm to generate exponentially many pseudo-references by compressing existing references into lattices and traversing them to generate new pseudo-References. |
| Outcome: | The proposed model significantly improves on baselines in machine translation and image captioning, and is comparable to existing models. |
Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings (2021.emnlp-main)
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| Challenge: | Existing systems for simultaneous translation are still trained on full-sentence bitexts due to the abundance of unnecessary long-distance reorderings. |
| Approach: | They propose to rewrite target side of existing full-sentence corpora into simultaneous-style translation by adding generated pseudo-references to the target side. |
| Outcome: | Experiments on ZhEn and JaEn simultaneous translation show that the proposed method improves on existing full-sentence corpora. |
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
| Approach: | They propose to embed phonetic and textual information into neural machine translation datasets to improve robustness to homophone noises. |
| Outcome: | The proposed method improves the robustness of neural machine translation to homophone noises on clean test sets. |
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. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
Learning to Stop in Structured Prediction for Neural Machine Translation (N19-1)
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| Challenge: | Beam search optimization solves many problems in neural machine translation, but lacks principled stopping criteria and does not learn how to stop during training. |
| Approach: | They propose a ranking method which enables an optimal beam search stop-ping criteria and a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. |
| Outcome: | Experiments on synthetic and real languages show that the proposed methods improve translation quality and length. |
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. |