pwc-archive/papers-with-abstracts huggingface dataset:
Like Chalk and Cheese? On the Effects of Translationese in MT Training
Authors: Samuel Larkin, Michel Simard, Rebecca Knowles
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

We revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in translation direction: the Canadian Hansard. According to automatic metrics and we observe that using parallel data that was produced in the “matching” translation direction (Authentic source and translationese target) improves translation quality. In cases of data imbalance in terms of translation direction and we find that tagging of translation direction can close the performance gap. We perform a human evaluation that differs slightly from the automatic metrics and but nevertheless confirms that for this French-English dataset that is known to contain high-quality translations and authentic or tagged mixed source improves over translationese source for training.
Investigating Softmax Tempering for Training Neural Machine Translation Models
Authors: Raj Dabre, Atsushi Fujita
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) models are typically trained using a softmax cross-entropy loss where the softmax distribution is compared against the gold labels. In low-resource scenarios and NMT models tend to perform poorly because the model training quickly converges to a point where the softmax distribution computed using logits approaches the gold label distribution. Although label smoothing is a well-known solution to address this issue and we further propose to divide the logits by a temperature coefficient greater than one and forcing the softmax distribution to be smoother during training. This makes it harder for the model to quickly over-fit. In our experiments on 11 language pairs in the low-resource Asian Language Treebank dataset and we observed significant improvements in translation quality. Our analysis focuses on finding the right balance of label smoothing and softmax tempering which indicates that they are orthogonal methods. Finally and a study of softmax entropies and gradients reveal the impact of our method on the internal behavior of our NMT models.
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation
Authors: Minghan Wang, Jiaxin Guo, Yimeng Chen, Chang Su, Min Zhang, Shimin Tao, Hao Yang
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Multimodal Machine Translation, NMT, Transfer Learning, Translation

Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT. To this end and we propose a transfer learning solution. Specifically and 1) A vanilla Transformer is pre-trained on massive bilingual text-only corpus to obtain prior knowledge; 2) A multimodal Transformer named VLTransformer is proposed with several components incorporated visual contexts; and 3) The parameters of VLTransformer are initialized with the pre-trained vanilla Transformer and then being fine-tuned on MMT tasks with a newly proposed method named cross-modal masking which forces the model to learn from both modalities. We evaluated on the Multi30k en-de and en-fr dataset and improving up to 8% BLEU score compared with the SOTA performance. The experimental result demonstrates that performing transfer learning with monomodal pre-trained NMT model on multimodal NMT tasks can obtain considerable boosts.
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework
Authors: Divya Kumari, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Sentiment Analysis, Sentiment Classification, Translation

Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment and emotion and gender traits and etc.) to the target and especially in a low-resource scenario. Such loss can affect the performance of any downstream Natural Language Processing (NLP) task and such as sentiment analysis and that heavily relies on the output of the MT systems. The susceptibility to sentiment polarity loss becomes even more severe when an MT system is employed for translating a source content that lacks a legitimate language structure (e.g. review text). Therefore and we must find ways to minimize the undesirable effects of sentiment loss in translation without compromising with the adequacy. In our current work and we present a deep re-inforcement learning (RL) framework in conjunction with the curriculum learning (as per difficulties of the reward) to fine-tune the parameters of a pre-trained neural MT system so that the generated translation successfully encodes the underlying sentiment of the source without compromising the adequacy unlike previous methods. We evaluate our proposed method on the English–Hindi (product domain) and French–English (restaurant domain) review datasets and and found that our method brings a significant improvement over several baselines in the machine translation and and sentiment classification tasks.
On nature and causes of observed MT errors
Authors: Maja Popovic
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Translation

This work describes analysis of nature and causes of MT errors observed by different evaluators under guidance of different quality criteria: adequacy and comprehension and and a not specified generic mixture of adequacy and fluency. We report results for three language pairs and two domains and eleven MT systems. Our findings indicate that and despite the fact that some of the identified phenomena depend on domain and/or language and the following set of phenomena can be considered as generally challenging for modern MT systems: rephrasing groups of words and translation of ambiguous source words and translating noun phrases and and mistranslations. Furthermore and we show that the quality criterion also has impact on error perception. Our findings indicate that comprehension and adequacy can be assessed simultaneously by different evaluators and so that comprehension and as an important quality criterion and can be included more often in human evaluations.
A Comparison of Sentence-Weighting Techniques for NMT
Authors: Simon Rieß, Matthias Huck, Alex Fraser
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Classification, Domain Adaptation, domain classification, Language Modeling, Language Modelling, NMT, Sentence, Translation

Sentence weighting is a simple and powerful domain adaptation technique. We carry out domain classification for computing sentence weights with 1) language model cross entropy difference 2) a convolutional neural network 3) a Recursive Neural Tensor Network. We compare these approaches with regard to domain classification accuracy and and study the posterior probability distributions. Then we carry out NMT experiments in the scenario where we have no in-domain parallel corpora and and only very limited in-domain monolingual corpora. Here and we use the domain classifier to reweight the sentences of our out-of-domain training corpus. This leads to improvements of up to 2.1 BLEU for German to English translation.
Sentiment-based Candidate Selection for NMT
Authors: Alexander G. Jones, Derry Wijaya
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Binary Classification, Decoder, Machine Translation, NMT, Sentence, Sentiment Analysis, Translation

The explosion of user-generated content (UGC)—e.g. social media posts and comments and and reviews—has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic and sentiment-charged language and we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train monolingual sentiment classifiers in English and Spanish and in addition to a multilingual sentiment model and by fine-tuning BERT and XLM-RoBERTa. Using n-best candidates generated by a baseline MT model with beam search and we select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation and and perform two human evaluations to assess the produced translations. Unlike previous work and we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval and rather than using e.g. binary classification and allowing for more fine-grained selection of translation candidates. The results of human evaluations show that and in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built and our pipeline produces more accurate translations of colloquial and sentiment-heavy source texts.
Studying The Impact Of Document-level Context On Simultaneous Neural Machine Translation
Authors: Raj Dabre, Aizhan Imankulova, Masahiro Kaneko
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

In a real-time simultaneous translation setting and neural machine translation (NMT) models start generating target language tokens from incomplete source language sentences and making them harder to translate and leading to poor translation quality. Previous research has shown that document-level NMT and comprising of sentence and context encoders and a decoder and leverages context from neighboring sentences and helps improve translation quality. In simultaneous translation settings and the context from previous sentences should be even more critical. To this end and in this paper and we propose wait-k simultaneous document-level NMT where we keep the context encoder as it is and replace the source sentence encoder and target language decoder with their wait-k equivalents. We experiment with low and high resource settings using the ALT and OpenSubtitles2018 corpora and where we observe minor improvements in translation quality. We then perform an analysis of the translations obtained using our models by focusing on sentences that should benefit from the context where we found out that the model does and in fact and benefit from context but is unable to effectively leverage it and especially in a low-resource setting. This shows that there is a need for further innovation in the way useful context is identified and leveraged.
Attainable Text-to-Text Machine Translation vs. Translation: Issues Beyond Linguistic Processing
Authors: Atsushi Fujita
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

Existing approaches for machine translation (MT) mostly translate given text in the source language into the target language and without explicitly referring to information indispensable for producing proper translation. This includes not only information in other textual elements and modalities than texts in the same document and but also extra-document and non-linguistic information and such as norms and skopos. To design better translation production work-flows and we need to distinguish translation issues that could be resolved by the existing text-to-text approaches and those beyond them. To this end and we conducted an analytic assessment of MT outputs and taking an English-to-Japanese news translation task as a case study. First and examples of translation issues and their revisions were collected by a two-stage post-editing (PE) method: performing minimal PE to obtain translation attainable based on the given textual information and further performing full PE to obtain truly acceptable translation referring to any information if necessary. Then and the collected revision examples were manually analyzed. We revealed dominant issues and information indispensable for resolving them and such as fine-grained style specifications and terminology and domain-specific knowledge and and reference documents and delineating a clear distinction between translation and what text-to-text MT can ultimately attain.
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation
Authors: Kamal Gupta, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Data Augmentation, Machine Translation, NMT, Translation

Product reviews provide valuable feedback of the customers and however and they are available today only in English on most of the e-commerce platforms. The nature of reviews provided by customers in any multilingual country poses unique challenges for machine translation such as code-mixing and ungrammatical sentences and presence of colloquial terms and lack of e-commerce parallel corpus etc. Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz. (i). a novel phrase augmentation technique (PhrRep) where the syntactic noun phrases in sentences are replaced by the other noun phrases carrying different meanings but in similar context; and (ii). a novel attention guided noise augmentation (AttnNoise) technique to make our NMT model robust towards various noise. Evaluation shows that using the proposed augmentation techniques we achieve a 6.67 BLEU score improvement over the baseline model. In order to show that our proposed approach is not language-specific and we also perform experiments for two other language pairs and viz. En-Fr (MTNT18 corpus) and En-De (IWSLT17) that yield the improvements of 2.55 and 0.91 BLEU points and respectively and over the baselines.
Optimizing Word Alignments with Better Subword Tokenization
Authors: Anh Khoa Ngo Ho, François Yvon
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Sentence, Translation, Word Alignment

Word alignment identify translational correspondences between words in a parallel sentence pair and are used and for example and to train statistical machine translation and learn bilingual dictionaries or to perform quality estimation. Subword tokenization has become a standard preprocessing step for a large number of applications and notably for state-of-the-art open vocabulary machine translation systems. In this paper and we thoroughly study how this preprocessing step interacts with the word alignment task and propose several tokenization strategies to obtain well-segmented parallel corpora. Using these new techniques and we were able to improve baseline word-based alignment models for six language pairs.
Introducing Mouse Actions into Interactive-Predictive Neural Machine Translation
Authors: Ángel Navarro, Francisco Casacuberta
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

The quality of the translations generated by Machine Translation (MT) systems has highly improved through the years and but we are still far away to obtain fully automatic high-quality translations. To generate them and translators make use of Computer-Assisted Translation (CAT) tools and among which we find the Interactive-Predictive Machine Translation (IPMT) systems. In this paper and we use bandit feedback as the main and only information needed to generate new predictions that correct the previous translations. The application of bandit feedback reduces significantly the number of words that the translator need to type in an IPMT session. In conclusion and the use of this technique saves useful time and effort to translators and its performance improves with the future advances in MT and so we recommend its application in the actuals IPMT systems.
Neural Machine Translation with Inflected Lexicon
Authors: Artur Nowakowski, Krzysztof Jassem
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Sentence, Translation

The paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language. In particular and we introduce a method and based on constrained decoding and which handles the inflected forms of lexical entries and does not require any modification to the training data or model architecture. To evaluate its effectiveness and we carry out experiments in two different scenarios: general and domain-specific. We compare our method with baseline translation and i.e. translation without lexical constraints and in terms of translation speed and translation quality. To evaluate how well the method handles the constraints and we propose new evaluation metrics which take into account the presence and placement and duplication and inflectional correctness of lexical terms in the output sentence.
An Alignment-Based Approach to Semi-Supervised Bilingual Lexicon Induction with Small Parallel Corpora
Authors: Kelly Marchisio, Philipp Koehn, Conghao Xiong
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Bilingual Lexicon Induction, Translation

Aimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While interesting and fully unsupervised settings are unrealistic; small amounts of bilingual data are usually available due to the existence of massively multilingual parallel corpora and or linguists can create small amounts of parallel data. In this work and we demonstrate an effective bootstrapping approach for semi-supervised bilingual lexicon induction that capitalizes upon the complementary strengths of two disparate methods for inducing bilingual lexicons. Whereas statistical methods are highly effective at inducing correct translation pairs for words frequently occurring in a parallel corpus and monolingual embedding spaces have the advantage of having been trained on large amounts of data and and therefore may induce accurate translations for words absent from the small corpus. By combining these relative strengths and our method achieves state-of-the-art results on 3 of 4 language pairs in the challenging VecMap test set using minimal amounts of parallel data and without the need for a translation dictionary. We release our implementation at www.blind-review.code.
Post-Editing Job Profiles for Subtitlers
Authors: Anke Tardel, Silvia Hansen-Schirra, Jean Nitzke
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

Language technologies, such as machine translation (MT), but also the application of artificial intelligence in general and an abundance of CAT tools and platforms have an increasing influence on the translation market. Human interaction with these technologies becomes ever more important as they impact translators’ workflows, work environments, and job profiles. Moreover, it has implications for translator training. One of the tasks that emerged with language technologies is post-editing (PE) where a human translator corrects raw machine translated output according to given guidelines and quality criteria (O’Brien, 2011: 197-198). Already widely used in several traditional translation settings, its use has come into focus in more creative processes such as literary translation and audiovisual translation (AVT) as well. With the integration of MT systems, the translation process should become more efficient. Both economic and cognitive processes are impacted and with it the necessary competences of all stakeholders involved change. In this paper, we want to describe the different potential job profiles and respective competences needed when post-editing subtitles.
Operating a Complex SLT System with Speakers and Human Interpreters
Authors: Ondřej Bojar, Vojtěch Srdečný, Rishu Kumar, Otakar Smrž, Felix Schneider, Barry Haddow, Phil Williams, Chiara Canton
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Translation

We describe our experience with providing automatic simultaneous spoken language translation for an event with human interpreters. We provide a detailed overview of the systems we use, focusing on their interconnection and the issues it brings. We present our tools to monitor the pipeline and a web application to present the results of our SLT pipeline to the end users. Finally, we discuss various challenges we encountered, their possible solutions and we suggest improvements for future deployments.
Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs
Authors: Francesco Saina
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Translation

This paper explores how technology, particularly digital tools and artificial intelligence, are impacting multilingual communication and language transfer processes. Information and communication technologies are enabling novel interaction patterns, with computers transitioning from pure media to actual language generators, and profoundly reshaping the industry of language services, as the relevance of language data and assisting engines continues to rise. Since these changes deeply affect communication and languages models overall, they need to be addressed not only from the perspective of information technology or by business-driven companies, but also in the field of translation and interpreting studies, in a broader debate among scholars and practitioners, and when preparing educational programs for the training of specialised language professionals. Special focus is devoted to some of the latest advancements in automatic speech recognition and spoken translation, and how their applications in interpreting may push the boundaries of new ‘augmented’ real-world use cases. Hence, this work—at the intersection of theoretical investigation, professional practice, and instructional design—aims at offering an introductory overview of the current landscape and envisaging potential paths for forthcoming scenarios.
Is “good enough” good enough? Ethical and responsible development of sign language technologies
Authors: Maartje De Meulder
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8

This paper identifies some common and specific pitfalls in the development of sign language technologies targeted at deaf communities, with a specific focus on signing avatars. It makes the call to urgently interrogate some of the ideologies behind those technologies, including issues of ethical and responsible development. The paper addresses four separate and interlinked issues: ideologies about deaf people and mediated communication, bias in data sets and learning, user feedback, and applications of the technologies. The paper ends with several take away points for both technology developers and deaf NGOs. Technology developers should give more consideration to diversifying their team and working interdisciplinary, and be mindful of the biases that inevitably creep into data sets. There should also be a consideration of the technologies’ end users. Sign language interpreters are not the end users nor should they be seen as the benchmark for language use. Technology developers and deaf NGOs can engage in a dialogue about how to prioritize application domains and prioritize within application domains. Finally, deaf NGOs policy statements will need to take a longer view, and use avatars to think of a significantly better system compared to what sign language interpreting services can provide.