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.

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 A Survey on Hybrid Machine Translation

2020 ◽  
Vol 184 ◽  
pp. 01061
Author(s):  
Anusha Anugu ◽  
Gajula Ramesh
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Literature Survey ◽  
Neural Machine Translation ◽  
Translation Tools ◽  
Translation Techniques ◽  
Hybrid Machine ◽  
Hybrid Machine Translation ◽  
Translation Systems ◽  
Evaluation Techniques

Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).

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.

scholarly journals On the Linguistic Representational Power of Neural Machine Translation Models

2020 ◽  
Vol 46 (1) ◽  
pp. 1-52
Author(s):  
Yonatan Belinkov ◽  
Nadir Durrani ◽  
Fahim Dalvi ◽  
Hassan Sajjad ◽  
James Glass
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Lexical Semantics ◽  
Linguistic Information ◽  
Neural Machine Translation ◽  
Recent Success ◽  
Part Of Speech ◽  
Morphologically Rich Languages ◽  
Representational Power ◽  
Semantic Dependencies

Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure captured within the learned representations, which is an important aspect in translating morphologically rich languages? (ii) Do the representations capture long-range dependencies, and effectively handle syntactically divergent languages? (iii) Do the representations capture lexical semantics? We conduct a thorough investigation along several parameters: (i) Which layers in the architecture capture each of these linguistic phenomena; (ii) How does the choice of translation unit (word, character, or subword unit) impact the linguistic properties captured by the underlying representations? (iii) Do the encoder and decoder learn differently and independently? (iv) Do the representations learned by multilingual NMT models capture the same amount of linguistic information as their bilingual counterparts? Our data-driven, quantitative evaluation illuminates important aspects in NMT models and their ability to capture various linguistic phenomena. We show that deep NMT models trained in an end-to-end fashion, without being provided any direct supervision during the training process, learn a non-trivial amount of linguistic information. Notable findings include the following observations: (i) Word morphology and part-of-speech information are captured at the lower layers of the model; (ii) In contrast, lexical semantics or non-local syntactic and semantic dependencies are better represented at the higher layers of the model; (iii) Representations learned using characters are more informed about word-morphology compared to those learned using subword units; and (iv) Representations learned by multilingual models are richer compared to bilingual models.

Japanese translation teaching corpus based on bilingual non parallel data model

2020 ◽  
pp. 1-11
Author(s):  
Zheng Guo ◽  
Zhu Jifeng
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Data Model ◽  
Graph Representation ◽  
Small Scale ◽  
Translation Model ◽  
Parallel Corpus ◽  
Parallel Data ◽  
Intelligent Technology ◽  
Japanese Translation

In recent years, with the development of Internet and intelligent technology, Japanese translation teaching has gradually explored a new teaching mode. Under the guidance of natural language processing and intelligent machine translation, machine translation based on statistical model has gradually become one of the primary auxiliary tools in Japanese translation teaching. In order to solve the problems of small scale, slow speed and incomplete field in the traditional parallel corpus machine translation, this paper constructs a Japanese translation teaching corpus based on the bilingual non parallel data model, and uses this corpus to train Japanese translation teaching machine translation model Moses to get better auxiliary effect. In the process of construction, for non parallel corpus, we use the translation retrieval framework based on word graph representation to extract parallel sentence pairs from the corpus, and then build a translation retrieval model based on Bilingual non parallel data. The experimental results of training Moses translation model with Japanese translation corpus show that the bilingual nonparallel data model constructed in this paper has good translation retrieval performance. Compared with the existing algorithm, the Bleu value extracted in the parallel sentence pair is increased by 2.58. In addition, the retrieval method based on the structure of translation option words graph proposed in this paper is time efficient and has better performance and efficiency in assisting Japanese translation teaching.

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