ymoslem/acl-abstracts huggingface dataset:
Spelling Normalization of Historical Documents by Using a Machine Translation Approach (2018)

The lack of a spelling convention in historical documents makes their orthography to change depending on the author and the time period in which each document was written. This represents a problem for the preservation of the cultural heritage, which strives to create a digital text version of a historical document. With the aim of solving this problem, we propose three approaches{---}based on statistical, neural and character-based machine translation{---}to adapt the document`s spelling to modern standards. We tested these approaches in different scenarios, obtaining very encouraging results.
Neural Machine Translation of Basque (2018)

We describe the first experimental results in neural machine translation for Basque. As a synthetic language featuring agglutinative morphology, an extended case system, complex verbal morphology and relatively free word order, Basque presents a large number of challenging characteristics for machine translation in general, and for data-driven approaches such as attentionbased encoder-decoder models in particular. We present our results on a large range of experiments in Basque-Spanish translation, comparing several neural machine translation system variants with both rule-based and statistical machine translation systems. We demonstrate that significant gains can be obtained with a neural network approach for this challenging language pair, and describe optimal configurations in terms of word segmentation and decoding parameters, measured against test sets that feature multiple references to account for word order variability.
Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation (2018)

We address the issues arising when a neural machine translation engine trained on generic data receives requests from a new domain that contains many specific technical terms. Given training data of the new domain, we consider two alternative methods to adapt the generic system: corpus-based and instance-based adaptation. While the first approach is computationally more intensive in generating a domain-customized network, the latter operates more efficiently at translation time and can handle on-the-fly adaptation to multiple domains. Besides evaluating the generic and the adapted networks with conventional translation quality metrics, in this paper we focus on their ability to properly handle domain-specific terms. We show that instance-based adaptation, by fine-tuning the model on-the-fly, is capable to significantly boost the accuracy of translated terms, producing translations of quality comparable to the expensive corpusbased method.
Translation Quality Estimation for Indian Languages (2018)

Translation Quality Estimation (QE) aims to estimate the quality of an automated machine translation (MT) output without any human intervention or reference translation. With the increasing use of MT systems in various cross-lingual applications, the need and applicability of QE systems is increasing. We study existing approaches and propose multiple neural network approaches for sentence-level QE, with a focus on MT outputs in Indian languages. For this, we also introduce five new datasets for four language pairs: two for English{--}Gujarati, and one each for English{--}Hindi, English{--}Telugu and English{--}Bengali, which includes one manually post-edited dataset for English{--} Gujarati. These Indian languages are spoken by around 689M speakers world-wide. We compare results obtained using our proposed models with multiple state-of-the-art systems including the winning system in the WMT17 shared task on QE and show that our proposed neural model which combines the discriminative power of carefully chosen features with Siamese Convolutional Neural Networks (CNNs) works best for all Indian language datasets.
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation (2018)

We present an approach to interactivepredictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of 5 feedback requests for every input.
Machine Translation Evaluation beyond the Sentence Level (2018)

Automatic machine translation evaluation was crucial for the rapid development of machine translation systems over the last two decades. So far, most attention has been paid to the evaluation metrics that work with text on the sentence level and so did the translation systems. Across-sentence translation quality depends on discourse phenomena that may not manifest at all when staying within sentence boundaries (e.g. coreference, discourse connectives, verb tense sequence etc.). To tackle this, we propose several document-level MT evaluation metrics: generalizations of sentence-level metrics, language-(pair)-independent versions of lexical cohesion scores and coreference and morphology preservation in the target texts. We measure their agreement with human judgment on a newly created dataset of pairwise paragraph comparisons for four language pairs.
An Analysis of Source Context Dependency in Neural Machine Translation (2018)

The encoder-decoder with attention model has become the state of the art for machine translation. However, more investigations are still needed to understand the internal mechanism of this end-to-end model. In this paper, we focus on how neural machine translation (NMT) models consider source information while decoding. We propose a numerical measurement of source context dependency in the NMT models and analyze the behaviors of the NMT decoder with this measurement under several circumstances. Experimental results show that this measurement is an appropriate estimate for source context dependency and consistent over different domains.
Gist MT Users: A Snapshot of the Use and Users of One Online MT Tool (2018)

This study analyzes usage statistics and the results of an end-user survey to compile a snapshot of the current use and users of one online machine translation (MT) tool, Multilizer`s PDF Translator1. The results reveal that the tool is used predominantly for assimilation purposes and that respondents use MT often. People use the tool to translate texts from different areas of life, including work, study and leisure. Of these, the study area is currently the most prevalent. The results also reveal a tendency for users to machine translate documents that are in languages they have some understanding of, rather than texts they do not understand at all. The findings imply that gist MT is becoming a part of people`s everyday lives and that perhaps people use gist MT in a different way than they use publishing-level translations.
Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair (2018)

While systems using the Neural Network-based Machine Translation (NMT) paradigm achieve the highest scores on recent shared tasks, phrase-based (PBMT) systems, rule-based (RBMT) systems and other systems may get better results for individual examples. Therefore, combined systems should achieve the best results for MT, particularly if the system combination method can take advantage of the strengths of each paradigm. In this paper, we describe a system that predicts whether a NMT, PBMT or RBMT will get the best Spanish translation result for a particular English sentence in DGT-TM 20161. Then we use fuzzy-match repair (FMR) as a mechanism to show that the combined system outperforms individual systems in a black-box machine translation setting.
Data selection for NMT using Infrequent n-gram Recovery (2018)

Neural Machine Translation (NMT) has achieved promising results comparable with Phrase-Based Statistical Machine Translation (PBSMT). However, to train a neural translation engine, much more powerful machines are required than those required to develop translation engines based on PBSMT. One solution to reduce the training cost of NMT systems is the reduction of the training corpus through data selection (DS) techniques. There are many DS techniques applied in PBSMT which bring good results. In this work, we show that the data selection technique based on infrequent n-gram occurrence described in (Gasco ́ et al., 2012) commonly used for PBSMT systems also works well for NMT systems. We focus our work on selecting data according to specific corpora using the previously mentioned technique. The specific-domain corpora used for our experiments are IT domain and medical domain. The DS technique significantly reduces the execution time required to train the model between 87{\%} and 93{\%}. Also, it improves translation quality by up to 2.8 BLEU points. The improvements are obtained with just a small fraction of the data that accounts for between 6{\%} and 20{\%} of the total data.
Translating Short Segments with NMT: A Case Study in English-to-Hindi (2018)

This paper presents a case study in translating short image captions of the Visual Genome dataset from English into Hindi using out-of-domain data sets of varying size. We experiment with three NMT models: the shallow and deep sequence-tosequence and the Transformer model as implemented in Marian toolkit. Phrase-based Moses serves as the baseline. The results indicate that the Transformer model outperforms others in the large data setting in a number of automatic metrics and manual evaluation, and it also produces the fewest truncated sentences. Transformer training is however very sensitive to the hyperparameters, so it requires more experimenting. The deep sequence-to-sequence model produced more flawless outputs in the small data setting and it was generally more stable, at the cost of more training iterations.
Feature Decay Algorithms for Neural Machine Translation (2018)

Neural Machine Translation (NMT) systems require a lot of data to be competitive. For this reason, data selection techniques are used only for finetuning systems that have been trained with larger amounts of data. In this work we aim to use Feature Decay Algorithms (FDA) data selection techniques not only to fine-tune a system but also to build a complete system with less data. Our findings reveal that it is possible to find a subset of sentence pairs, that outperforms by 1.11 BLEU points the full training corpus, when used for training a German-English NMT system .
Investigating Backtranslation in Neural Machine Translation (2018)

A prerequisite for training corpus-based machine translation (MT) systems {--} either Statistical MT (SMT) or Neural MT (NMT) {--} is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a highquality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus {--} both as a separate standalone dataset as well as combined with human-generated parallel data {--} affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.
Multi-Domain Neural Machine Translation (2018)

We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domainsasdistinctlanguagesandusemultilingual NMT methods to create multi-domain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases.
A Comparison of Different Punctuation Prediction Approaches in a Translation Context (2018)

We test a series of techniques to predict punctuation and its effect on machine translation (MT) quality. Several techniques for punctuation prediction are compared: language modeling techniques, such as n-grams and long shortterm memories (LSTM), sequence labeling LSTMs (unidirectional and bidirectional), and monolingual phrase-based, hierarchical and neural MT. For actual translation, phrase-based, hierarchical and neural MT are investigated. We observe that for punctuation prediction, phrase-based statistical MT and neural MT reach similar results, and are best used as a preprocessing step which is followed by neural MT to perform the actual translation. Implicit punctuation insertion by a dedicated neural MT system, trained on unpunctuated source and punctuated target, yields similar results.
Integrating MT at Swiss Post`s Language Service: preliminary results (2018)

This paper presents the preliminary results of an ongoing academia-industry collaboration that aims to integrate MT into the workflow of Swiss Post`s Language Service. We describe the evaluations carried out to select an MT tool (commercial or open-source) and assess the suitability of machine translation for post-editing in Swiss Post`s various subject areas and language pairs. The goal of this first phase is to provide recommendations with regard to the tool, language pair and most suitable domain for implementing MT.
Iterative Data Augmentation for Neural Machine Translation: a Low Resource Case Study for English-Telugu (2018)

Telugu is the fifteenth most commonly spoken language in the world with an estimated reach of 75 million people in the Indian subcontinent. At the same time, it is a severely low resourced language. In this paper, we present work on English{--}Telugu general domain machine translation (MT) systems using small amounts of parallel data. The baseline statistical (SMT) and neural MT (NMT) systems do not yield acceptable translation quality, mostly due to limited resources. However, the use of synthetic parallel data (generated using back translation, based on an NMT engine) significantly improves translation quality and allows NMT to outperform SMT. We extend back translation and propose a new, iterative data augmentation (IDA) method. Filtering of synthetic data and IDA both further boost translation quality of our final NMT systems, as measured by BLEU scores on all test sets and based on state-of-the-art human evaluation.
Toward leveraging Gherkin Controlled Natural Language and Machine Translation for Global Product Information Development (2018)

Machine Translation (MT) already plays an important part in software development process at McAfee where the technology can be leveraged to provide early builds for localization and internationalization testing teams. Behavior Driven Development (BDD) has been growing in usage as a development methodology in McAfee. Within BDD, the Gherkin Controlled Natural Language (CNL) is a syntax and common terminology set that is used to describe the software or business process in a User Story. Given there exists this control on the language to describe User Stories for software features using Gherkin, we seek to use Machine Translation to Globalize it at high accuracy and without PostEditing and reuse it as Product Information. This enables global product information development to happen as part of the Software Development Life Cycle (SDLC) and at low cost.
Implementing a neural machine translation engine for mobile devices: the Lingvanex use case (2018)

In this paper, we present the challenge entailed by implementing a mobile version of a neural machine translation system, where the goal is to maximise translation quality while minimising model size. We explain the whole process of implementing the translation engine on an English{--}Spanish example and we describe all the difficulties found and the solutions implemented. The main techniques used in this work are data selection by means of Infrequent n-gram Recovery, appending a special word at the end of each sentence, and generating additional samples without the final punctuation marks. The last two techniques were devised with the purpose of achieving a translation model that generates sentences without the final full stop, or other punctuation marks. Also, in this work, the Infrequent n-gram Recovery was used for the first time to create a new corpus, and not enlarge the in-domain dataset. Finally, we get a small size model with quality good enough to serve for daily use.
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation (2018)

With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent. Since these models require large amounts of data and in-domain knowledge, expanding an equivalent service into new markets is disrupted by language barriers that inhibit dialog automation. This paper presents a user study to evaluate the utility of out-of-the-box machine translation technology to (1) rapidly bootstrap multilingual spoken dialog systems and (2) enable existing human analysts to understand foreign language utterances. We additionally evaluate the utility of machine translation in human assisted environments, where a portion of the traffic is processed by analysts. In English{\textrightarrow}Spanish experiments, we observe a high potential for dialog automation, as well as the potential for human analysts to process foreign language utterances with high accuracy.
How to Move to Neural Machine Translation for Enterprise-Scale Programs - An Early Adoption Case Study (2018)

While Neural Machine Translation (NMT) technology has been around for a few years now in research and development, it is still in its infancy when it comes to customization readiness and experience with implementation on an enterprise scale with Language Service Providers (LSPs). For large, multi-language LSPs, it is therefore not only important to stay up-to-date on latest research on the technology as such, the best use cases, as well as main advantages and disadvantages. Moreover, due to this infancy, the challenges encountered during an early adoption of the technology in an enterprise-scale translation program are of a very practical and concrete nature and range from the quality of the NMT output over availability of language pairs in (customizable) NMT systems to additional translation workflow investments and considerations with regard to involving the supply chain. In an attempt to outline the above challenges and possible approaches to overcome them, this paper describes the migration of an established enterprise-scale machine translation program of 28 language pairs with post-editing from a Statistical Machine Translation (SMT) setup to NMT.
A Comparison of Statistical and Neural MT in a Multi-Product and Multilingual Software Company - User Study (2018)

Over the last 4 years, Infor has been implementing machine translation (MT) in its translation process. In this paper, the results of both statistical and neural MT projects are provide to give an insight in the advantages and disadvantages of MT use in a large company. We also offer a look into the future of MT within our company and to strengthen the implementation of MT in our translation process.
Does Machine Translation Really Produce Translations? (2018)

I will try to answer the question of whether Machine Translation (MT) can be considered a full translation process. I argue that, instead, it should be seen as part of a process performed by translators, in which MT plays a fundamental support role. The roles of translators and MT in the translation process is presented in an analysis that get its elements from Translation Studies and Translation Process Research.
Pre-professional pre-conceptions (2018)

While MT+PE has become an industry standard, our translation schools are not able to accompany these changes by updating their academic programs. We polled 100 pre-professionals to confirm that in our local context they are reluctant to accept post-editing jobs mainly because they have inherited pre-conceptions or negative opinions about MT during their studies.
Determining translators' perception, productivity and post-editing effort when using SMT and NMT systems (2018)

Thanks to the great progress seen in the machine translation (MT) field in recent years, the use and perception of MT by translators need to be revisited. The main objective of this paper is to determine the perception, productivity and the postediting effort (in terms of time and number of editings) of six translators when using Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) systems. This presentation is focused on how translators perceive these two systems in order to know which one they prefer and what type of errors and problems present each system, as well as how translators solve these issues. These tests will be performed with the Dynamic Quality Framework (DQF) tools (quick comparison and productivity tasks) using Google Neural Machine Translation and Microsoft Translator (SMT) APIs in two different English into Spanish texts, an instruction manual and a marketing webpage. Results showed that translators considerably prefer NMT over SMT. Moreover, NMT is more adequate and fluent than SMT.
Machine translation post-editing in the professional translation market in Spain: a case study on the experience and opinion of professional translators (2018)

The objective of this paper is to analyse some aspects related to the practice of post-editing services in the current translation market in Spain. To this aim, some quantitative data collected through an online survey and concerning the experience and opinion of professional translators regarding post-editing will be shown.
Perception vs. Acceptability of TM and SMT Output: What do translators prefer? (2018)

This paper reports the results of two studies carried out with two different group of professional translators to find out how professionals perceive and accept SMT in comparison with TM. The first group translated and post-edited segments from English into German, and the second group from English into Spanish. Both studies had equivalent settings in order to guarantee the comparability of the results. It will also help to shed light upon the real benefit of SMT from which translators may take advantage.
Learning to use machine translation on the Translation Commons Learn portal (2018)

We describe the Learn portal of Translation Commons (TC), a self-managed community of volunteer translators community aimed at sharing tools, resources and initiatives for the translation community as a whole. Members are encouraged to upload and share their free resources on the platform and to create free courses and tutorials. Specifically there are no educational material on machine translation yet and we invite experts to contribute.