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Zhongjun He

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6 papers

1 author row

AAAI Conference 2020 Conference Paper

Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

Yuchen Liu
Jiajun Zhang
Hao Xiong
Long Zhou
Zhongjun He
Hua Wu
Haifeng Wang
Chengqing Zong

Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional pipeline system, the end-to-end ST model has potential beneﬁts of lower latency, smaller model size, and less error propagation. However, it is notoriously difﬁcult to implement such a model without transcriptions as intermediate. Existing works generally apply multi-task learning to improve translation quality by jointly training end-to-end ST along with automatic speech recognition (ASR). However, different tasks in this method cannot utilize information from each other, which limits the improvement. Other works propose a two-stage model where the second model can use the hidden state from the ﬁrst one, but its cascade manner greatly affects the ef- ﬁciency of training and inference process. In this paper, we propose a novel interactive attention mechanism which enables ASR and ST to perform synchronously and interactively in a single model. Speciﬁcally, the generation of transcriptions and translations not only relies on its previous outputs but also the outputs predicted in the other task. Experiments on TED speech translation corpora have shown that our proposed model can outperform strong baselines on the quality of speech translation and achieve better speech recognition performances as well.

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AAAI Conference 2019 Conference Paper

Addressing the Under-Translation Problem from the Entropy Perspective

Yang Zhao
Jiajun Zhang
Chengqing Zong
Zhongjun He
Hua Wu

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation problem still remains a big challenge. In this paper, we focus on the under-translation problem and attempt to find out what kinds of source words are more likely to be ignored. Through analysis, we observe that a source word with a large translation entropy is more inclined to be dropped. To address this problem, we propose a coarse-to-fine framework. In coarse-grained phase, we introduce a simple strategy to reduce the entropy of highentropy words through constructing the pseudo target sentences. In fine-grained phase, we propose three methods, including pre-training method, multitask method and two-pass method, to encourage the neural model to correctly translate these high-entropy words. Experimental results on various translation tasks show that our method can significantly improve the translation quality and substantially reduce the under-translation cases of high-entropy words.

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AAAI Conference 2019 Conference Paper

Modeling Coherence for Discourse Neural Machine Translation

Hao Xiong
Zhongjun He
Hua Wu
Haifeng Wang

Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1. 23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2. 2 BLEU improvements when evaluated by discourse metrics.

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AAAI Conference 2018 Conference Paper

Multi-Channel Encoder for Neural Machine Translation

Hao Xiong
Zhongjun He
Xiaoguang Hu
Hua Wu

Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More speciﬁcally, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine (NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6. 52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English- French task, our single shallow system achieves BLEU=38. 8, comparable with the state-of-the-art deep models.

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IJCAI Conference 2016 Conference Paper

Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation

Yong Cheng
Shiqi Shen
Zhongjun He
Wei He
Hua Wu
Maosong Sun
Yang Liu

The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.

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AAAI Conference 2016 Conference Paper

Improved Neural Machine Translation with SMT Features

Wei He
Zhongjun He
Hua Wu
Haifeng Wang

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in ﬂuent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method signiﬁcantly improves the translation quality of the state-of-the-art NMT system on Chinese-to- English translation tasks. Our method produces a gain of up to 2. 33 BLEU score on NIST open test sets.

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