المؤلفون: Mahata, Sainik Kumar, Das, Dipankar, Bandyopadhyay, Sivaji

المصدر: Machine Translation; Dec2021, Vol. 35 Issue 4, p687-709, 23p

مصطلحات موضوعية: MACHINE translating, CONTRASTIVE linguistics, SYSTEMS development, TRANSLATING & interpreting, CORPORA

مستخلص: Parallel corpora are central to translation studies and contrastive linguistics. However, training machine translation (MT) systems by barely using the semantic aspects of a parallel corpus leads to unsatisfactory results, as then the trained MT systems are likely to generate target sentences that are semantically and pragmatically different from the source sentence. In the present work, we explore the improvement in the performance of an MT system when pragmatic features such as sentiment are introduced during its development. The language pair used for the experiments is English (source language) and Bengali (target language). The improvement in the MT output, before and after the introduction of sentiment features, is quantified by comparing various translation models, such as SMT, NMT and a newly developed translation model SeNA, with the help of automated (BLEU and TER) and manual evaluation metrics. In addition, the propagation of sentiment during the translation process is also studied extensively. We observe that the introduction of sentiment features during the system development process helps in elevating the translation quality. [ABSTRACT FROM AUTHOR]

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المؤلفون: Wolfe, Rosalee

المصدر: Machine Translation; Sep2021, Vol. 35 Issue 3, p301-304, 4p

مصطلحات موضوعية: SIGN language, TRANSLATING & interpreting, ORAL communication, AVATARS (Virtual reality), AUTOMATIC speech recognition

مستخلص: In I Computer-assisted Sign Language translation: a study of translators' practice to specify CAT software, i they describe an initial study to identify translator needs for producing fast, accurate translation. Absence of a standard written form Automatic systems for speech translation rely on written forms of the source and target languages. Consequently, creating a translator between a signed language and a spoken language is at least as challenging as creating a translator between spoken languages. [Extracted from the article]

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المؤلفون: Kaczmarek, Marion, Filhol, Michael

المصدر: Machine Translation; Sep2021, Vol. 35 Issue 3, p305-322, 18p

مصطلحات موضوعية: SIGN language, TRANSLATING & interpreting, TRANSLATORS, CATS, COMPUTER software

مستخلص: Professional Sign Language translators, unlike their text-to-text counterparts, are not equipped with computer-assisted translation (CAT) software. Those softwares are meant to ease the translators' tasks. No prior study as been conducted on this topic, and we aim at specifying such a software. To do so, we based our study on the professional Sign Language translators' practices and needs. The aim of this paper is to identify the necessary steps in the text-to-sign translation process. By filming and interviewing professionals for both objective and subjective data, we build a list of tasks and see if they are systematic and performed in a definite order. Finally, we reflect on how CAT tools could assist those tasks, how to adapt the existing tools to Sign Language and what is necessary to add in order to fit the needs of Sign Language translation. In the long term, we plan to develop a first prototype of CAT software for sign languages. [ABSTRACT FROM AUTHOR]

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المؤلفون: Guerberof Arenas, Ana, Moorkens, Joss, O'Brien, Sharon

المصدر: Machine Translation; Jun2021, Vol. 35 Issue 2, p205-237, 33p

مصطلحات موضوعية: TRANSLATING & interpreting, USER experience, MACHINE translating, PROTOCOL analysis (Cognition), COGNITIVE load, USER interfaces, EYE tracking

مستخلص: This paper presents results of the effect of different translation modalities on users when working with the Microsoft Word user interface. An experimental study was set up with 84 Japanese, German, Spanish, and English native speakers working with Microsoft Word in three modalities: the published translated version, a machine translated (MT) version (with unedited MT strings incorporated into the MS Word interface) and the published English version. An eye-tracker measured the cognitive load and usability according to the ISO/TR 16982 guidelines: i.e., effectiveness, efficiency, and satisfaction followed by retrospective think-aloud protocol. The results show that the users' effectiveness (number of tasks completed) does not significantly differ due to the translation modality. However, their efficiency (time for task completion) and self-reported satisfaction are significantly higher when working with the released product as opposed to the unedited MT version, especially when participants are less experienced. The eye-tracking results show that users experience a higher cognitive load when working with MT and with the human-translated versions as opposed to the English original. The results suggest that language and translation modality play a significant role in the usability of software products whether users complete the given tasks or not and even if they are unaware that MT was used to translate the interface. [ABSTRACT FROM AUTHOR]

: Copyright of Machine Translation is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Biçici, Ergun

المصدر: Machine Translation; Jun2021, Vol. 35 Issue 2, p239-263, 25p

مصطلحات موضوعية: MACHINE translating, ALGORITHMS, NATURAL language processing, TRANSLATING & interpreting, PARALLEL algorithms, ERROR rates

مستخلص: Parallel feature weight decay algorithms, parfwd, are engineered for language- and task-adaptive instance selection to build distinct machine translation (MT) models and enable the fast development of accurate MT using less data and computation. parfwd decay the weights of both source and target features to increase their average coverage. In a conference on MT (WMT), parfwd achieved the lowest translation error rate from French to English in 2015, and a rate 11.7 % less than the top phrase-based statistical MT (PBSMT) in 2017. parfwd also achieved a rate 5.8 % less than the top in TweetMT and the top from Catalan to English. BLEU upper bounds identify the translation directions that offer the largest room for relative improvement and MT models that use additional data. Performance trend angles show the power of MT models to convert unit data into unit translation results or more BLEU for an increase in coverage. The source coverage angle of parfwd in the 2013–2019 WMT reached + 6 ∘ better than the top with 35 ∘ for translation into English, and it was + 1.4 ∘ better than the top with 22 ∘ overall. [ABSTRACT FROM AUTHOR]

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المؤلفون: Specia, Lucia, Wang, Josiah, Lee, Sun Jae, Ostapenko, Alissa, Madhyastha, Pranava

المصدر: Machine Translation; Jun2021, Vol. 35 Issue 2, p145-165, 21p

مصطلحات موضوعية: MACHINE translating, TRANSLATING & interpreting

مستخلص: We propose multimodal machine translation (MMT) approaches that exploit the correspondences between words and image regions. In contrast to existing work, our referential grounding method considers objects as the visual unit for grounding, rather than whole images or abstract image regions, and performs visual grounding in the source language, rather than at the decoding stage via attention. We explore two referential grounding approaches: (i) implicit grounding, where the model jointly learns how to ground the source language in the visual representation and to translate; and (ii) explicit grounding, where grounding is performed independent of the translation model, and is subsequently used to guide machine translation. We performed experiments on the Multi30K dataset for three language pairs: English–German, English–French and English–Czech. Our referential grounding models outperform existing MMT models according to automatic and human evaluation metrics. [ABSTRACT FROM AUTHOR]

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المؤلفون: Armengol-Estapé, Jordi, Costa-jussà, Marta R.

المصدر: Machine Translation; Apr2021, Vol. 35 Issue 1, p3-17, 15p

مصطلحات موضوعية: TRANSLATING & interpreting, MACHINE translating

مستخلص: Introducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information. [ABSTRACT FROM AUTHOR]

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المؤلفون: Dewangan, Shubham, Alva, Shreya, Joshi, Nitish, Bhattacharyya, Pushpak

المصدر: Machine Translation; Apr2021, Vol. 35 Issue 1, p71-99, 29p

مصطلحات موضوعية: MACHINE translating, PROGRAMMING languages, INDIANS (Asians), TRANSLATING & interpreting

مصطلحات جغرافية: INDIA

مستخلص: In this paper we explore neural machine translation (NMT) for Indian languages. Reported work on Indian language Statistical Machine Translation (SMT) demonstrated good performance within the Indo-Aryan family, but relatively poor performance within the Dravidian family as well as between the two families. Interestingly, by common observation NMT generates more fluent output than SMT. This led us to investigate NMT's potential for translation involving Indian languages. The current practice in NMT is to train the models with subword units. Among subwording methods, byte pair encoding (BPE) is a popular choice. We conduct extensive experiments with BPE-based NMT models for Indian languages. An interesting outcome of our study is the finding that the optimal value for BPE merge for Indian language pairs seems to be falling in the range of 0–5000 which is fairly low compared to that observed for European Languages. Additionally, we apply other techniques such as phrase table injection and linguistic feature based enhancements on corpora, plus BERT augmented NMT to boost performance. To the best of our knowledge, this is the first comprehensive study on Indian language NMT (ILNMT) covering major languages in India. As an empirical paper, we expect this work could serve as a benchmark for ILNMT research. [ABSTRACT FROM AUTHOR]

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المؤلفون: Ashengo, Yeabsira Asefa, Aga, Rosa Tsegaye, Abebe, Surafel Lemma

المصدر: Machine Translation; Apr2021, Vol. 35 Issue 1, p19-36, 18p

مصطلحات موضوعية: MACHINE translating, RECURRENT neural networks, TRANSLATING & interpreting, SIMPLE machines

مستخلص: Context-aware machine translation approaches improve the quality of translation by incorporating the context of the surrounding phrases in the translation of a phrase. So far, for the low-resource language pair English-Amharic, context-aware machine translation approaches have not been investigated in depth. Moreover, the current approaches for machine translation of the low-resource language pair English-Amharic usually require a large set of parallel corpus to achieve fluency. This research investigates a new approach that translates English text to Amharic text using a combination of context based machine translation (CBMT) and a recurrent neural network machine translation (RNNMT). We built a bilingual dictionary for the CBMT to use along with a target corpus. The RNNMT model is then provided with the output of the CBMT and a parallel corpus for training. The approach is evaluated using the New Testament Bible as a corpus. Our combinational approach on English–Amharic language pair yields a performance improvement over the simple neural machine translation (NMT), while no improvement is seen over CBMT for a small dataset. We have also assessed the impact of the dictionary used by CBMT on the overall performance of the approach. The result shows that the dictionary accuracy, and hence, the CBMT output is found to affect the combinational approach. [ABSTRACT FROM AUTHOR]

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المؤلفون: Le, Ngoc-Tien, Lecouteux, Benjamin, Besacier, Laurent

المصدر: Machine Translation; Dec2018, Vol. 32 Issue 4, p325-351, 27p

مصطلحات موضوعية: ORAL communication, TRANSLATING & interpreting

مستخلص: This paper addresses the automatic quality estimation of spoken language translation (SLT). This relatively new task is defined and formalized as a sequence-labeling problem where each word in the SLT hypothesis is tagged as good or bad according to a large feature set. We propose several word confidence estimators (WCE) based on our automatic evaluation of transcription (ASR) quality, translation (MT) quality, or both (combined ASR + MT). This research work is possible because we built a specific corpus, which contains 6.7k utterances comprising the quintuplet: ASR output, verbatim transcript, text translation, speech translation, and post-edition of the translation. The conclusion of our multiple experiments using joint ASR and MT features for WCE is that MT features remain the most influential while ASR features can bring interesting complementary information. In addition, the last part of the paper proposes to disentangle ASR errors and MT errors where each word in the SLT hypothesis is tagged as good, asr_error or mt_error. Robust quality estimators for SLT can be used for re-scoring speech translation graphs or for providing feedback to the user in interactive speech translation or computer-assisted speech-to-text scenarios. [ABSTRACT FROM AUTHOR]

: Copyright of Machine Translation is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)