Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

Machine translation aims to minimize the language barrier between people of different linguistic backgrounds. Machine translation is an automatic translation technique between pairs of different languages. Machine translation using neural network or neural machine translation came into the picture due to several limitation associated with its predecessors which are rule based and statistical based models. For large data sets with a rich range of vocabulary, the neural network machine translation system provides fair translation accuracy. We have observed that there remain very few machine translators dedicated to Indian languages, especially those spoken in the North-East area. So our paper mainly focuses on implementing a neural machine translation system for the English-Assamese language. In this paper we used five different neural machine translation models for English-Assamese language pair using LSTMs and GRUs, along with an attention layer. We have made a comparison analysis based on the performance results of these models. We used BLEU Score to calculate the accuracy of these five models, thus achieving a BLEU score of 34.168%, which was the highest among the five models.

Neural Machine Translation (NMT) has recently achieved the state-of-the-art in many machine translation tasks, but one of the challenges that NMT faces is the lack of parallel corpora, especially for low-resource language pairs. And the result is that the performance of NMT is much less effective for low-resource languages. To address this specific problem, in this paper, we describe a novel NMT model that is based on encoder-decoder architecture and relies on character-level inputs. Our proposed model employs Convolutional Neural Networks (CNN) and highway networks over character inputs, whose outputs are given to an encoder-decoder neural machine translation network. Besides, we also present two other approaches to improve the performance of the low-resource NMT system much further. First, we use language modeling implemented by denoising autoencoding to pre-train and initialize the full model. Second, we share the weights of the front few layers of two encoders between two languages to strengthen the encoding ability of the model. We demonstrate our model on two low-resource language pairs. On the IWSLT2015 English-Vietnamese translation task, our proposed model obtains improvements up to 2.5 BLEU points compared to the baseline. We also outperform the baseline approach more than 3 BLEU points on the CWMT2018 Chinese-Mongolian translation task.

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.

Corpus-based machine translation (MT) has been the main approach to developing and implementing MT systems in both academia and the industry over the last three decades. In this field, the type and size of the corpus used for training MT engines have presented problems for both statistical MT (SMT) systems as well as neural MT (NMT) systems, being the two dominant corpusbased approaches. Moreover, language pairs such as Turkish-English have been understudied within this framework. This article aims to evaluate the translation quality in Turkish-to-English custom MT systems that have been trained on different corpus sizes and types. Two NMT engines and two SMT engines were trained on the KantanMT platform using two different training corpus types with either only domain-specific cardiology corpus or this corpus plus a mixed-domain corpus. The study conducted both automatic evaluations with metrics including BLEU, F-Measure and TER, as well as a comprehensive human evaluation with metrics including fluency, A/B test, and adequacy. Lastly, the study realized a separate, subjective terminology evaluation in order to investigate how differently MT systems handle terminology, as this is a crucial aspect for specific-domain text types such as cardiology. While the automatic evaluation results suggest the SMT engines to perform better than NMT engines, all human evaluators rated the mixed-domain NMT engine as the highest performing one. However, the terminology evaluation task demonstrated SMT to still be able to perform better and to commit less terminology errors, despite the industry and academia shifting toward NMT engines.

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context. In this paper, we propose a Confidence Based Bidirectional Global Context Aware (CBBGCA) training framework for NMT, where the NMT model is jointly trained with an auxiliary conditional masked language model (CMLM). The training consists of two stages: (1) multi-task joint training; (2) confidence based knowledge distillation. At the first stage, by sharing encoder parameters, the NMT model is additionally supervised by the signal from the CMLM decoder that contains bidirectional global contexts. Moreover, at the second stage, using the CMLM as teacher, we further pertinently incorporate bidirectional global context to the NMT model on its unconfidently-predicted target words via knowledge distillation. Experimental results show that our proposed CBBGCA training framework significantly improves the NMT model by +1.02, +1.30 and +0.57 BLEU scores on three large-scale translation datasets, namely WMT'14 English-to-German, WMT'19 Chinese-to-English and WMT'14 English-to-French, respectively.

Neural machine translation is a machine translation system that uses artificial neural networks to identify nonlinear relationships between bilingual sentence pairs. The language English-Kannada pair has met with less attention in the field of Neural Machine Translation (NMT), mostly due to lack of parallel corpora and the complexity of Kannada's linguistic structure. This research proposes a novel NMT model based on Gated Recurrent Units (GRU) and a Hybrid attention mechanism designed specifically for Kannada's morphological complexity and compared with existing models. By adding language-specific preprocessing techniques and employing data augmentation tactics, our model outperforms previous LSTM, GRU and Bi-LSTM-based algorithms in terms of BLEU and METEOR scores. The proposed model with hybrid attention might be a useful substitute in low-resource settings because transformers and other large models require more resources. The comparative study indicates that the proposed methodology improves the translation by 10% increase in Bilingual Evaluation Understudy (BLEU) and accuracy of 80% and achieved a Rouge Score of 0.9.

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system.

Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjabi to English NMT system. A parallel corpus for Punjabi to English containing MWEs was developed and used to train the different models of NMT. Punjabi is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively.

While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on the assumption, our method is able to train a source-to-target NMT model ("student") without parallel corpora available guided by an existing pivot-to-target NMT model ("teacher") on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.

With the active participation of users in product review platforms, online consumer-generated content, and, more specifically, user-generated reviews, have become a clear reference in purchasing decision-making processes, which sometimes exceed the impact of advertising campaigns. A common feature of most tourism review platforms is the use of machine translation (MT) systems to immediately make reviews available to users in various languages. However, the quality of the MT output of these reviews varies greatly, primarily due to the subjective and unstructured nature of this digital genre. Different studies confirm that there are no universal quality rating scales. The assessment of MT output quality usually depends on factors such as the purpose of the text or the value given to the immediacy of the translation. New neural MT systems have been a revolution in the quality increase of the translated output; however, new lines of research are opening up to verify whether the quality of this new paradigm of MT can be assessed with the existing scales, mainly from previous rule-based systems and statistical translation, or whether it is necessary to develop new quality metrics specifically for these new intelligent systems. On the other hand, one of the questions that remain to be resolved in this new context of neural MT is whether the use of large amounts of textual data in the training of these systems is as effective as the use of less data but of higher quality and better-adjusted to the specialty and type of text for which it is used. Based on the hypothesis that each genre requires specific quality rating scales, this work identifies the error patterns and textual characteristics of online user reviews from a corpus-based approach analysis that will contribute to adapting quality rating scales to this specific digital genre.

Machine Translation (MT) has recently been strengthened by the emergence of deep neural network techniques, which have brought the dream of language translation in community-based practices and clinical practices closer to reality. This chapter introduces the clinical practice of MT, especially in low resource languages. The following are some of the highlights of this chapter. How MT played a crucial role in good social reasons is focused on in this chapter. The chapter starts with the history of translation technologies that have played a vital role in various crisis and relief scenarios as the Haitian earthquake in 2010 and Translators without Borders (TWB), respectively. The Standby Task Force (SBTF) deployed the technology to tackle misinformation during Coronavirus Pandemic 2019. Next, the chapter explores the importance of the MT system, where it describes MT literacy as required across society to impact the healthcare of human beings. It describes the challenges of translation for clinical texts. Some of those are highlighted as (a) Domain: characteristics of clinical text differentiated from texts with other domains, (b) code mixed low-resource languages: noisy clinical data and lower quantity of data in the digital form available for computation, (c) lack of clinical domain corpora, and (d) very rich morphological structure: e.g., procedures (e.g., hepatico-cholangio-jejuno-stomy) and chemicals (e.g., Hydro-xy-nitro-di-hydro-thy-mine). Strategies or approaches to building MT on the clinical text can be categorized as: (1) Rule-based MT (early days), (2) Statistical MT (SMT, one of the successful methods in MT history), and (3) Neural MT (NMT, recent advancements of MT). Both SMT and NMT that work on same grounds by learning from text and are the primary focus of this chapter. L earning transparency for the patient's use of clinical data is an essential component. Much of today's healthcare data is unstructured text, such as clinical text, doctor's notes, nursing notes, lab reports, test reports, and more. A clinical practitioner is faced with a language barrier for communicating with a patient to interpret patient histories, examine a clinical diagnosis, or restate the recommended treatment method and follow-up to aid comprehension. The only way a medical practitioner encourages patients to ask questions or respond to queries is by directing them to input text with the help of MT. Finally, the chapter discusses contemporary techniques with Neural Machine Translation (NMT), namely multilingual, transfer learning on clinical data.

Generative adversarial networks (GANs) has achieved great success in the field of image processing, Adversarial Neural Machine Translation(NMT) is the application of GANs to machine translation. Unlike previous work training NMT model through maximizing the likelihood of the human translation, Adversarial NMT minimizes the distinction between human translation and the translation generated by a NMT model. Even though Adversarial NMT has achieved impressive results, while using little in the way of prior knowledge. In this paper, we integrated bilingual dictionaries to Adversarial NMT by leveraging a character model. Extensive experiment shows that our proposed methods can achieve remarkable improvement on the translation quality of Adversarial NMT, and obtain better result than several strong baselines.

Neural Machine Translation (NMT) has attracted growing interest in recent years for its promising performance compared to traditional approaches such as Statistical Machine Translation. However, its application to languages having different structures, like the (English, Arabic) pair that interests us in this work, degrades its performance. Indeed, the limited vocabulary size required by the NMT models decreases the vocabulary coverage rate of the Arabic language, well known by its morphological richness. Likewise, long sentences present an additional challenge to NMT systems because they perform less well for longer sentences than for the shorter ones. In this paper, we provide a series of experiments to mitigate the effects of these constraints. To address the problem of out-of-vocabulary words, we integrated into factored NMT models morphosyntactic features as an output factor, namely stem, lemma, POS, root, and pattern. We have also developed two techniques for segmenting long sentences into smaller sub-sentences. The first uses a list of lexical markers that we have collected as segmentation points, and the second integrates into the NMT model the parallel phrases extracted by an SMT system. The experiments carried out on the English–Arabic pair show that the proposed approaches considerably improve the translation quality compared to the basic NMT system.

Due to the limited availability of corpora in the field of Electrical Engineering and the presence of numerous specialized terms, neural machine translation (NMT) performs poorly in translating the sentence backbone information when it is applied to corpora in the field of Electrical Engineering. In response to this issue, a method to improve NMT by using the sentence backbone information is proposed in this paper. In the proposed method, the source language sentences are used as the input of the Sentence Backbone Information Extraction Model to obtain the sentence backbone information, and then the sentence backbone information are incorporated as an auxiliary during the training process of the NMT model. Furthermore, a module called the Sentence Backbone Information Enhancement Module is introduced. It utilizes the dependency parse trees of the source language sentences to generate the sentence backbone mask matrices. These matrices are then applied to the encoder to force the NMT model to pay more attention to the backbones of sentences. On the English-Chinese parallel corpus in the field of Electrical Engineering, the proposed method in this paper outperforms the Transformer baseline translation model by 1.25 BLEU points. And it outperforms the baseline model in both METEOR and ROUGE-L evaluation metrics. It indicates that the proposed method in this paper can effectively improve translation performance.