scholarly journals ME-MD: An Effective Framework for Neural Machine Translation with Multiple Encoders and Decoders

2017 ◽  
Author(s):  
Jinchao Zhang ◽  
Qun Liu ◽  
Jie Zhou
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
State Of The Art ◽  
The State ◽  
Neural Machine Translation ◽  
Source Sentence ◽  
Target Words

The encoder-decoder neural framework is widely employed for Neural Machine Translation (NMT) with a single encoder to represent the source sentence and a single decoder to generate target words. The translation performance heavily relies on the representation ability of the encoder and the generation ability of the decoder. To further enhance NMT, we propose to extend the original encoder-decoder framework to a novel one, which has multiple encoders and decoders (ME-MD). Through this way, multiple encoders extract more diverse features to represent the source sequence and multiple decoders capture more complicated translation knowledge. Our proposed ME-MD framework is convenient to integrate heterogeneous encoders and decoders with multiple depths and multiple types. Experiment on Chinese-English translation task shows that our ME-MD system surpasses the state-of-the-art NMT system by 2.1 BLEU points and surpasses the phrase-based Moses by 7.38 BLEU points. Our framework is general and can be applied to other sequence to sequence tasks.

scholarly journals Neural Machine Translation with Joint Representation

2020 ◽  
Vol 34 (05) ◽  
pp. 8285-8292
Author(s):  
Yanyang Li ◽  
Qiang Wang ◽  
Tong Xiao ◽  
Tongran Liu ◽  
Jingbo Zhu
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
Large Scale ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
The State ◽  
Small Scale ◽  
Neural Machine Translation ◽  
Joint Representation

Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e.g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency. In this paper, we employ Joint Representation that fully accounts for each possible interaction. We sidestep the inefficiency issue by refining representations with the proposed efficient attention operation. The resulting Reformer models offer a new Sequence-to-Sequence modelling paradigm besides the Encoder-Decoder framework and outperform the Transformer baseline in either the small scale IWSLT14 German-English, English-German and IWSLT15 Vietnamese-English or the large scale NIST12 Chinese-English translation tasks by about 1 BLEU point. We also propose a systematic model scaling approach, allowing the Reformer model to beat the state-of-the-art Transformer in IWSLT14 German-English and NIST12 Chinese-English with about 50% fewer parameters. The code is publicly available at https://github.com/lyy1994/reformer.

scholarly journals Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder

2019 ◽  
Vol 33 ◽  
pp. 5466-5473 ◽  
Author(s):  
Yingce Xia ◽  
Tianyu He ◽  
Xu Tan ◽  
Fei Tian ◽  
Di He ◽  
...  
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
State Of The Art ◽  
Compact Model ◽  
Word Embeddings ◽  
Simple Method ◽  
Neural Machine Translation ◽  
German Translation ◽  
One Step ◽  
Target Side

Sharing source and target side vocabularies and word embeddings has been a popular practice in neural machine translation (briefly, NMT) for similar languages (e.g., English to French or German translation). The success of such wordlevel sharing motivates us to move one step further: we consider model-level sharing and tie the whole parts of the encoder and decoder of an NMT model. We share the encoder and decoder of Transformer (Vaswani et al. 2017), the state-of-the-art NMT model, and obtain a compact model named Tied Transformer. Experimental results demonstrate that such a simple method works well for both similar and dissimilar language pairs. We empirically verify our framework for both supervised NMT and unsupervised NMT: we achieve a 35.52 BLEU score on IWSLT 2014 German to English translation, 28.98/29.89 BLEU scores on WMT 2014 English to German translation without/with monolingual data, and a 22.05 BLEU score on WMT 2016 unsupervised German to English translation.

scholarly journals Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction

2020 ◽  
Vol 34 (05) ◽  
pp. 7594-7601
Author(s):  
Pierre Colombo ◽  
Emile Chapuis ◽  
Matteo Manica ◽  
Emmanuel Vignon ◽  
Giovanna Varni ◽  
...  
Keyword(s):  
Machine Translation ◽  
Random Fields ◽  
Conditional Random Fields ◽  
State Of The Art ◽  
The State ◽  
Attention Mechanism ◽  
Accuracy Score ◽  
Beam Search ◽  
Conversational Agents ◽  
Neural Machine Translation

The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.

scholarly journals Modeling Coherence for Discourse Neural Machine Translation

2019 ◽  
Vol 33 ◽  
pp. 7338-7345 ◽  
Author(s):  
Hao Xiong ◽  
Zhongjun He ◽  
Hua Wu ◽  
Haifeng Wang
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Neural Machine Translation ◽  
Discourse Context ◽  
Translation Quality ◽  
Discourse Coherence ◽  
Baseline System

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.

scholarly journals Improved Neural Machine Translation with Source Syntax

2017 ◽  
Author(s):  
Shuangzhi Wu ◽  
Ming Zhou ◽  
Dongdong Zhang
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Syntactic Structure ◽  
Phrase Structure ◽  
Long Distance ◽  
Neural Machine Translation ◽  
Attention Model ◽  
Word Level ◽  
Source Sentence ◽  
Dependency Structures

Neural Machine Translation (NMT) based on the encoder-decoder architecture has recently achieved the state-of-the-art performance. Researchers have proven that extending word level attention to phrase level attention by incorporating source-side phrase structure can enhance the attention model and achieve promising improvement. However, word dependencies that can be crucial to correctly understand a source sentence are not always in a consecutive fashion (i.e. phrase structure), sometimes they can be in long distance. Phrase structures are not the best way to explicitly model long distance dependencies. In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT. Our method based on dependency trees enriches each source state with global dependency structures, which can better capture the inherent syntactic structure of source sentences. Experiments on Chinese-English and English-Japanese translation tasks show that our proposed method outperforms state-of-the-art SMT and NMT baselines.

scholarly journals Explicit Sentence Compression for Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8311-8318
Author(s):  
Zuchao Li ◽  
Rui Wang ◽  
Kehai Chen ◽  
Masao Utiyama ◽  
Eiichiro Sumita ◽  
...  
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
General Information ◽  
Compression Method ◽  
Neural Machine Translation ◽  
Sentence Compression ◽  
French And English ◽  
Source Sentence ◽  
Empirical Tests ◽  
Target Side

State-of-the-art Transformer-based neural machine translation (NMT) systems still follow a standard encoder-decoder framework, in which source sentence representation can be well done by an encoder with self-attention mechanism. Though Transformer-based encoder may effectively capture general information in its resulting source sentence representation, the backbone information, which stands for the gist of a sentence, is not specifically focused on. In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT. In practice, an explicit sentence compression goal used to learn the backbone information in a sentence. We propose three ways, including backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the compressed sentence into NMT. Our empirical tests on the WMT English-to-French and English-to-German translation tasks show that the proposed sentence compression method significantly improves the translation performances over strong baselines.

scholarly journals Translating with Bilingual Topic Knowledge for Neural Machine Translation

2019 ◽  
Vol 33 ◽  
pp. 7257-7264
Author(s):  
Xiangpeng Wei ◽  
Yue Hu ◽  
Luxi Xing ◽  
Yipeng Wang ◽  
Li Gao
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Target Domain ◽  
Neural Machine Translation ◽  
Topic Knowledge ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Source Sentence ◽  
Hidden States

The dominant neural machine translation (NMT) models that based on the encoder-decoder architecture have recently achieved the state-of-the-art performance. Traditionally, the NMT models only depend on the representations learned during training for mapping a source sentence into the target domain. However, the learned representations often suffer from implicit and inadequately informed properties. In this paper, we propose a novel bilingual topic enhanced NMT (BLTNMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT. Specifically, the bilingual topic knowledge is included into the hidden states of both encoder and decoder, as well as the attention mechanism. With this new setting, the proposed BLT-NMT has access to the background knowledge implied in bilingual topics which is beyond the sequential context, and enables the attention mechanism to attend to topic-level attentions for generating accurate target words during translation. Experimental results show that the proposed model consistently outperforms the traditional RNNsearch and the previous topic-informed NMT on Chinese-English and EnglishGerman translation tasks. We also introduce the bilingual topic knowledge into the newly emerged Transformer base model on English-German translation and achieve a notable improvement.

scholarly journals Convolutional over Recurrent Encoder for Neural Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Praveen Dakwale ◽  
Christof Monz
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Neural Machine Translation ◽  
German Translation ◽  
Source Sentence ◽  
End To End ◽  
Target Words

AbstractNeural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN). In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

scholarly journals Neural Machine Translation with Key-Value Memory-Augmented Attention

2018 ◽  
Author(s):  
Fandong Meng ◽  
Zhaopeng Tu ◽  
Yong Cheng ◽  
Haiyang Wu ◽  
Junjie Zhai ◽  
...  
Keyword(s):  
Target Word ◽  
Machine Translation ◽  
English Translation ◽  
Translation Process ◽  
Neural Machine Translation ◽  
Attention Model ◽  
Proposed Model ◽  
Source Sentence ◽  
Remarkable Progress ◽  
Source Word

Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations. To alleviate these issues, we propose a novel key-value memory-augmented attention model for NMT, called KVMEMATT. Specifically, we maintain a timely updated keymemory to keep track of attention history and a fixed value-memory to store the representation of source sentence throughout the whole translation process. Via nontrivial transformations and iterative interactions between the two memories, the decoder focuses on more appropriate source word(s) for predicting the next target word at each decoding step, therefore can improve the adequacy of translations. Experimental results on Chinese)English and WMT17 German,English translation tasks demonstrate the superiority of the proposed model.

Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

2021 ◽  
pp. 2050017
Author(s):  
Rashmini Naranpanawa ◽  
Ravinga Perera ◽  
Thilakshi Fonseka ◽  
Uthayasanker Thayasivam
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Rare Word ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Word Level ◽  
Morphologically Rich Languages

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

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