scholarly journals Semantic Neural Machine Translation Using AMR

2019 ◽  
Vol 7 ◽  
pp. 19-31 ◽  
Author(s):  
Linfeng Song ◽  
Daniel Gildea ◽  
Yue Zhang ◽  
Zhiguo Wang ◽  
Jinsong Su
Keyword(s):  
Machine Translation ◽  
The Other ◽  
Standard English ◽  
Semantic Representations ◽  
Data Sparsity ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Other Hand ◽  
Meaning Representation

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.

Non-autoregressive neural machine translation with auxiliary representation fusion

2021 ◽  
pp. 1-11
Author(s):  
Quan Du ◽  
Kai Feng ◽  
Chen Xu ◽  
Tong Xiao ◽  
Jingbo Zhu
Keyword(s):  
Machine Translation ◽  
Experimental Results ◽  
The Other ◽  
Generation Process ◽  
Neural Machine Translation ◽  
Trade Off ◽  
Translation Quality ◽  
Other Hand ◽  
Entire Sequence ◽  
Translation Accuracy

Recently, many efforts have been devoted to speeding up neural machine translation models. Among them, the non-autoregressive translation (NAT) model is promising because it removes the sequential dependence on the previously generated tokens and parallelizes the generation process of the entire sequence. On the other hand, the autoregressive translation (AT) model in general achieves a higher translation accuracy than the NAT counterpart. Therefore, a natural idea is to fuse the AT and NAT models to seek a trade-off between inference speed and translation quality. This paper proposes an ARF-NAT model (NAT with auxiliary representation fusion) to introduce the merit of a shallow AT model to an NAT model. Three functions are designed to fuse the auxiliary representation into the decoder of the NAT model. Experimental results show that ARF-NAT outperforms the NAT baseline by 5.26 BLEU scores on the WMT’14 German-English task with a significant speedup (7.58 times) over several strong AT baselines.

scholarly journals Improving Neural Machine Translation with AMR Semantic Graphs

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Long H. B. Nguyen ◽  
Viet H. Pham ◽  
Dien Dinh
Keyword(s):  
Machine Translation ◽  
Semantic Information ◽  
Linguistic Knowledge ◽  
Knowledge Sources ◽  
Semantic Representations ◽  
Neural Machine Translation ◽  
Proposed Model ◽  
Language Knowledge ◽  
Sparsity Problem ◽  
Meaning Representation

The Seq2Seq model and its variants (ConvSeq2Seq and Transformer) emerge as a promising novel solution to the machine translation problem. However, these models only focus on exploiting knowledge from bilingual sentences without paying much attention to utilizing external linguistic knowledge sources such as semantic representations. Not only do semantic representations can help preserve meaning but they also minimize the data sparsity problem. However, to date, semantic information remains rarely integrated into machine translation models. In this study, we examine the effect of abstract meaning representation (AMR) semantic graphs in different machine translation models. Experimental results on the IWSLT15 English-Vietnamese dataset have proven the efficiency of the proposed model, expanding the use of external language knowledge sources to significantly improve the performance of machine translation models, especially in the application of low-resource language pairs.

scholarly journals Ethical Issues of Neural Machine Translation

2020 ◽  
Vol 15 ◽  
pp. 81-83
Author(s):  
Serhii Zasiekin ◽  
Solomiia Vakuliuk
Keyword(s):  
Machine Translation ◽  
Ethical Issues ◽  
Point Of View ◽  
The Other ◽  
Neural Machine Translation ◽  
Translation Tools ◽  
Other Hand ◽  
Ethical Point ◽  
Translation Ethics

The paper is focused on the issues of machine translation ethics. The goal of the present study is to discuss the role of neural machine translation tools from an ethical point of view and their impact on humans. Although traditionally ethics of translation is viewed in terms of sameness and difference, it is human translator who is a party to ethics of translation. It is discussed that translators should rely on technology as a helpful leverage in their job, since it allows them to be faster and more productive. On the other hand, we take an interest in examining the extent to which translation technology tools are given power. Neural machine translators can be unsupervised by humans, therefore viewed as a party to ethics of translation.

scholarly journals Improving Stylized Neural Machine Translation with Iterative Dual Knowledge Transfer

2021 ◽  
Author(s):  
Xuanxuan Wu ◽  
Jian Liu ◽  
Xinjie Li ◽  
Jinan Xu ◽  
Yufeng Chen ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Machine Translation ◽  
Large Scale ◽  
Training Data ◽  
Transfer Model ◽  
Paired Data ◽  
Style Transfer ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Parallel Data

Stylized neural machine translation (NMT) aims to translate sentences of one style into sentences of another style, which is essential for the application of machine translation in a real-world scenario. However, a major challenge in this task is the scarcity of high-quality parallel data which is stylized paired. To address this problem, we propose an iterative dual knowledge transfer framework that utilizes informal training data of machine translation and formality style transfer data to create large-scale stylized paired data, for the training of stylized machine translation model. Specifically, we perform bidirectional knowledge transfer between translation model and text style transfer model iteratively through knowledge distillation. Then, we further propose a data-refinement module to process the noisy synthetic parallel data generated during knowledge transfer. Experiment results demonstrate the effectiveness of our method, achieving an improvement over the existing best model by 5 BLEU points on MTFC dataset. Meanwhile, extensive analyses illustrate our method can also improve the accuracy of formality style transfer.

PhraseAttn: Dynamic Slot Capsule Networks for phrase representation in Neural Machine Translation

2021 ◽  
pp. 1-8
Author(s):  
Binh Nguyen ◽  
Binh Le ◽  
Long H.B. Nguyen ◽  
Dien Dinh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Vital Role ◽  
Attention Mechanism ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Word Representation

 Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.

scholarly journals “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

2018 ◽  
Vol 9 (28) ◽  
pp. 6091-6098 ◽  
Author(s):  
Philippe Schwaller ◽  
Théophile Gaudin ◽  
Dávid Lányi ◽  
Costas Bekas ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Machine Translation ◽  
Chemical Reactions ◽  
Language Processing ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Complex Organic

Using a text-based representation of molecules, chemical reactions are predicted with a neural machine translation model borrowed from language processing.

scholarly journals Research on Neural Machine Translation Model

2019 ◽  
Vol 1237 ◽  
pp. 052020
Author(s):  
Mengyao Chen ◽  
Yong Li ◽  
Runqi Li
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Translation Model

scholarly journals Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement

2019 ◽  
Vol 33 ◽  
pp. 86-93 ◽  
Author(s):  
Zi-Yi Dou ◽  
Zhaopeng Tu ◽  
Xing Wang ◽  
Longyue Wang ◽  
Shuming Shi ◽  
...  
Keyword(s):  
Machine Translation ◽  
Recent Progress ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Baseline Model ◽  
Individual Layer ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Hidden States ◽  
Dynamic Layer

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 sh⇒German and WMT17 Chinese⇒English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.

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