ymoslem/acl-abstracts huggingface dataset:
Machine Translate: Open resources and community (2022)

Machine Translate is a non-profit organization on a mission to make machine translation more accessible to more people. As the field of machine translation continues to grow, the project builds open resources and a community for developers, buyers and translators. The project is ruled by three values: quality, openness and accessibility. Content is open-source and welcomes open-contribution. It is kept up-to-date, and its information is presented in a clear and well-organized format. Machine Translate aims to be accessible to people from many backgrounds and, ultimately, also non-English speakers. The project covers everything about machine translation, from products to research, from development to theory, and from history to news. The topics are very diverse, and the writing is focused on concepts rather than on mathematical details.
Unlocking the value of bilingual translated documents with Deep Learning Segmentation and Alignment for Arabic (2022)

To unlock the value of high-quality bilingual translated documents we need parallel data. With sentence-aligned translation pairs, we can fuel our neural machine translation, customize MT or create translation memories for our clients. To automate this process, automatic segmentation and alignment are required. Despite Arabic being the fifth biggest language in the world, language technology for Arabic is many times way behind other languages. We will show how we struggled to find a proper sentence segmentation for Arabic and instead explored different frameworks, from statistical to deep learning, to end up fine-tuning our own Arabic DL segmentation model. We will highlight our learnings and challenges with segmenting and aligning Arabic and English bilingual data. Finally, we will show the impact on our proprietary NMT engine as we started to unlock the value and could leverage data that had been translated offline, outside CAT tools, as well as comparable corpora, to feed our NMT.
Language I/O: Our Solution for Multilingual Customer Support (2022)

In this presentation, we will highlight the key technological innovations provided by Language I/O, see below. Dynamic MT engine selection based on the customer, content type, language pair, the content itself, as well as other metadata. Our proprietary MT quality estimation mechanism that allows customers to control their human review budget. The Self-Improving Glossary technology to continuously learn new keywords and key phrases based on the actual content processed in the platform.
A Proposed User Study on MT-Enabled Scanning (2022)

In this talk I will present a proposed user study to measure the impact of potentially misleading MT output on MT-enabled scanning of foreign language text by intelligence analysts (IAs) and the effectiveness of a practical intervention: providing output from more than one NMT system to the user. The focus of the talk will be on the approach to de-signing the user study to resemble scanning tasks in a measurable way with unclassified documents.
You`ve translated it, now what? (2022)

Humans use document formatting to discover document and section titles, and important phrases. But when machines process a paper{--}especially documents OCRed from images{--}these cues are often invisible to downstream processes: words in footnotes or body text are treated as just as important as words in titles. It would be better for indexing and summarization tools to be guided by implicit document structure. In an ODNI-sponsored project, ARLIS looked at discovering formatting in OCRed text as a way to infer document structure. Most OCR engines output results as hOCR (an XML format), giving bounding boxes around characters. In theory, this also provides style information such as bolding and italicization, but in practice, this capability is limited. For example, the Tesseract OCR tool provides bounding boxes, but does not attempt to detect bold text (relevant to author emphasis and specialized fields in e.g. print dictionaries), and its discrimination of italicization is poor. Our project inferred font size from hOCR bounding boxes, and using that and other cues (e.g. the fact that titles tend to be short) determined which text constituted section titles; from this, a document outline can be created. We also experimented with algorithms for detecting bold text. Our best algorithm has a much improved recall and precision, although the exact numbers are font-dependent. The next step is to incorporate inferred structure into the output of machine translation. One way is to embed XML tags for inferred structure into the text extracted from the imaged document, and to either pass the strings enclosed by XML tags to the MT engine individually, or pass the tags through the MT engine without modification. This structural information can guide downstream bulk processing tasks such as summarization and search, and also enables building tables of contents for human users examining individual documents.
SG Translate Together - Uplifting Singapore`s translation standards with the community through technology (2022)

The Singapore`s Ministry of Communications and Information (MCI) has officially launched the SG Translate Together (SGTT) web portal on 27 June 2022, with the aim of partnering its citizens to improve translation standards in Singapore. This web portal houses the Singapore Government`s first neural machine translation (MT) engine, known as SG Translate, which was jointly developed by MCI and the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR). Adapted using localised translation data, SG Translate is able to generate translations that are attuned to Singapore`s context and supports Singapore`s four (4) official languages {--} English (Singapore), Chinese (Singapore), Bahasa Melayu (Singapore) and Tamil (Singapore). Upon completion of development, MCI allowed all Government agencies to use SG Translate for their daily operations. This presentation will briefly cover the methodologies adopted and showcase SG Translate`s capability to translate content involving local culture, everyday life and government policies and schemes. This presentation will also showcase MCI`s sustainable approach for the continual training of the SG Translate MT engine through citizenry participation.
Multi-dimensional Consideration of Cognitive Effort in Translation and Interpreting Process Studies (2022)

Cognitive effort is the core element of translation and interpreting process studies, but theoretical and practical issues such as the concept, characteristics and measurement of cognitive effort still need to be clarified. This paper firstly analyzes the concept and research characteristics of cognitive effort in translation and interpreting process studies. Then, based on the cost concept (internal cost, opportunity cost) and the reward concept (need for cognition, learned industriousness) of cognitive effort, it carries out multi-dimensional analysis of the characteristics of cognitive effort. Finally, it points out the enlightenment of multi-dimensional consideration of cognitive effort to translation and interpreting process studies.
Thoughts on the History of Machine Translation in the United States (2022)

The history of machine translation (MT) covers intricate patterns of technical, policy, social, and artistic threads, many of which have been documented by researchers such as John Hutchins and Dr. Harold Somers. However, the history of MT{---}including the history of MT in the United States{---}has stories that not yet been told or that have only received the briefest of nods to the extraordinary work achieved. This presentation would address some of those stories, including: the U.S. government organizations that created research programs such as the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation (NSF) and how the values of those founding organizations impacted the development of MT. It would address the almost unknown or nearly forgotten work of the Xerox Palo Alto Research Center (PARC), the Xerox Rochester Translation Center, and Systran in the late 1980s and early 1990s to develop automated post-editing tools, confidence measures, and multi-engine solutions. It would discuss and illustrate the astounding impact of MT in movies and literature since the 1950s that still shapes public perception of the technology as more than ready to conduct the complex, nuanced, and multilanguage business of individuals, empires, and alliances. In addition, this presentation would raise questions and promote discussion of how we as a community can continue to capture our colorful and fast-developing history. The stories and observations are drawn from research by the speaker to develop an article on {\textquotedblleft}The History of Machine Translation in the United States,{\textquotedblright} which will be published later this year in The Routledge Encyclopedia of Machine Translation.
Hand in 01101000 01100001 01101110 01100100 with the Machine: A Roadmap to Quality (2022)

Seeking a clear roadmap for the translation services of the future, the Government of Canada`s Translation Bureau has spent the last few years modernizing its technology infrastructure and drawing up a strategy that would let it make the best of the opportunities opened up by artificial intelligence and computer-assisted translation tools. Yet in a sector that has gone from budding to thriving in such a short time, with a myriad options now available, it is no small feat to chart a course and move beyond the kid-in-the-candy-store stage. How can one distinguish between the flavour of the week and a sustainable way forward? Through a series of carefully planned proofs of concepts{---}and let`s say it, a fair share of trial and error{---}, a clear pathway to the future is shaping out for the Translation Bureau. Answers to some of the key questions of our times are beginning to take shape... and so are the challenges that stand in the way to success. The Translation Bureau`s Innovation Director Caroline-Soledad Mallette recounts lessons learned, surveys the lay of the land and outlines best practices in the search for an adaptative, best-fit solution for technology-augmented linguistic service provision. Join her as she suggests a new heading in our quest for progress: let the hype be focused not on technology, but on the people it empowers, with one ultimate goal in mind: quality.
Robust Translation of French Live Speech Transcripts (2022)

Despite a narrowed performance gap with direct approaches, cascade solutions, involving automatic speech recognition (ASR) and machine translation (MT) are still largely employed in speech translation (ST). Direct approaches employing a single model to translate the input speech signal suffer from the critical bottleneck of data scarcity. In addition, multiple industry applications display speech transcripts alongside translations, making cascade approaches more realistic and practical. In the context of cascaded simultaneous ST, we propose several solutions to adapt a neural MT network to take as input the transcripts output by an ASR system. Adaptation is achieved by enriching speech transcripts and MT data sets so that they more closely resemble each other, thereby improving the system robustness to error propagation and enhancing result legibility for humans. We address aspects such as sentence boundaries, capitalisation, punctuation, hesitations, repetitions, homophones, etc. while taking into account the low latency requirement of simultaneous ST systems.
Speech-to-Text and Evaluation of Multiple Machine Translation Systems (2022)

The National Virtual Translation Center (NVTC) and the larger Federal Bureau of Investiga-tion (FBI) seek to acquire tools that will facilitate its mission to provide English translations of non-English language audio and video files. In the text domain, NVTC has been using translation memory (TM) for some time and has reported on the incorporation of machine translation (MT) into that workflow. While we have explored the use of speech-to-text (STT) and speech translation (ST) in the past, we have now invested in the creation of a substantial human-created corpus to thoroughly evaluate alternatives in three languages: French, Rus-sian, and Persian. We report on the results of multiple STT systems combined with four MT systems for these languages. We evaluated and scored the different systems in combination and analyzed results. This points the way to the most successful tool combination to deploy in this workflow.
English-Russian Data Augmentation for Neural Machine Translation (2022)

Data Augmentation (DA) refers to strategies for increasing the diversity of training examples without explicitly collecting new data manually. We have used neural networks and linguistic resources for the automatic generation of text in Russian. The system generates new texts using information from embeddings trained with a huge amount of data in neural language models. Data from the public domain have been used for experiments. The generation of these texts increases the corpus used to train models for NLP tasks, such as machine translation. Finally, an analysis of the results obtained evaluating the quality of generated texts has been carried out and those texts have been added to the training process of Neural Machine Translation (NMT) models. In order to evaluate the quality of the NMT models, firstly, these models have been compared performing a quantitative analysis by means of several standard automatic metrics used in machine translation, and measuring the time spent and the amount of text generated for a good use in the language industry. Secondly, NMT models have been compared through a qualitative analysis, where generated examples of translation have been exposed and compared with each other. Using our DA method, we achieve better results than a baseline model by fine tuning NMT systems with the newly generated datasets.
Efficient Machine Translation Corpus Generation (2022)

This paper proposes an efficient and semi-automated method for human-in-the-loop post- editing for machine translation (MT) corpus generation. The method is based on online training of a custom MT quality estimation metric on-the-fly as linguists perform post-edits. The online estimator is used to prioritize worse hypotheses for post-editing, and auto-close best hypothe- ses without post-editing. This way, significant improvements can be achieved in the resulting quality of post-edits at a lower cost due to reduced human involvement. The trained estimator can also provide an online sanity check mechanism for post-edits and remove the need for ad- ditional linguists to review them or work on the same hypotheses. In this paper, the effect of prioritizing with the proposed method on the resulting MT corpus quality is presented versus scheduling hypotheses randomly. As demonstrated by experiments, the proposed method im- proves the lifecycle of MT models by focusing the linguist effort on production samples and hypotheses, which matter most for expanding MT corpora to be used for re-training them
Building and Analysis of Tamil Lyric Corpus with Semantic Representation (2022)

In the new era of modern technology, the cloud has become the library for many things including entertainment, i.e, the availability of lyrics. In order to create awareness about the language and to increase the interest in Tamil film lyrics, a computerized electronic format of Tamil lyrics corpus is necessary for mining the lyric documents. In this paper, the Tamil lyric corpus was collected from various books and lyric websites. Here, we also address the challenges faced while building this corpus. A corpus was created with 15286 documents and stored all the lyric information obtained in the XML format. In this paper, we also explained the Universal Networking Language (UNL) semantic representation that helps to represent the document in a language and domain independent ways. We evaluated this corpus by performing simple statistical analysis for characters, words and a few rhetorical effect analysis. We also evaluated our semantic representation with the existing work and the results are very encouraging.
Ukrainian-To-English Folktale Corpus: Parallel Corpus Creation and Augmentation for Machine Translation in Low-Resource Languages (2022)

Folktales are linguistically very rich and culturally significant in understanding the source language. Historically, only human translation has been used for translating folklore. Therefore, the number of translated texts is very sparse, which limits access to knowledge about cultural traditions and customs. We have created a new Ukrainian-To-English parallel corpus of familiar Ukrainian folktales based on available English translations and suggested several new ones. We offer a combined domain-specific approach to building and augmenting this corpus, considering the nature of the domain and differences in the purpose of human versus machine translation. Our corpus is word and sentence-aligned, allowing for the best curation of meaning, specifically tailored for use as training data for machine translation models.
The graphical brain and deep inference (2022)

This presentation considers deep temporal models in the brain. It builds on previous formulations of active inference to simulate behaviour and electrophysiological responses under deep (hierarchical) generative models of discrete state transitions. The deeply structured temporal aspect of these models means that evidence is accumulated over distinct temporal scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour in terms of Bayesian belief updating {--} and associated neuronal processes {--} to reproduce the epistemic foraging seen in reading. These simulations reproduce these sort of perisaccadic delay period activity and local field potentials seen empirically; including evidence accumulation and place cell activity. These simulations are presented as an example of how to use basic principles to constrain our understanding of system architectures in the brain {--} and the functional imperatives that may apply to neuronal networks.
Differentiated measurements for fatigue and demotivation/amotivation in translation - lessons learnt from fatigue and motivation studies (2022)

Fatigue is physical and mental weariness caused by prolonged continuity of work and would undermine work performance. In translation studies, although fatigue is a confounding fac- tor previous experiments all try to control, its detection and measurement are largely ignored. To bridge this lacuna, this article recommends some subjective and objective approaches to measuring translation fatigue based on prior fatigue research. Meanwhile, as demotivation is believed to be an emotion that confounds its accurate measurements, a discussion on how to distinguish those two states is further conducted from theoretical and methodological perspec- tives. In doing so, this paper not only illuminates on how to measure two essential influencers of translation performance, but also offers some insights into the distinction of affective and physical states during translation process.
Investigating the Impact of Different Pivot Languages on Translation Quality (2022)

Translating via an intermediate pivot language is a common practice, but the impact of the pivot language on the quality of the final translation has not often been investigated. In order to compare the effect of different pivots, we back-translate 41 English source segments via vari- ous intermediate channels (Arabic, Chinese and monolingual paraphrasing) into English. We compare the 912 English back-translations of the 41 original English segments using manual evaluation, as well as COMET and various incarnations of BLEU. We compare human from- scratch back-translations with MT back-translations and monolingual paraphrasing. A varia- tion of BLEU (Cum-2) seems to better correlate with our manual evaluation than COMET and the conventional BLEU Cum-4, but a fine-grained qualitative analysis reveals that differences between different pivot languages (Arabic and Chinese) are not captured by the automatized TQA measures.
Predicting the number of errors in human translation using source text and translator characteristics (2022)

Translation quality and efficiency are of great importance in the language services industry, which is why production duration and error counts are frequently investigated in Translation Process Research. However, a clear picture has not yet emerged as to how these two variables can be optimized or how they relate to one another. In the present study, data from multiple English-Japanese translation sessions is used to predict the number of errors per segment using source text and translator characteristics. An analysis utilizing zero-inflated generalized linear mixed effects models revealed that two source text characteristics (syntactic complexity and the proportion of long words) and three translator characteristics (years of experience, the time translators spent reading a source text before translating, and the time translators spent revising a translation) significantly influenced the number of errors. Furthermore, a lower proportion of long words per source text sentence and more training led to a significantly higher probability of error-free translation. Based on these results, combined with findings from a previous study on production duration, it is concluded that years of experience and the duration of the final revision phase are important factors that have a positive impact on translation efficiency and quality
The impact of translation competence on error recognition of neural MT (2022)

Schaeffer et al. (2019) studied whether translation student`s error recognition processes dif- fered from those in professional translators. The stimuli consisted of complete texts, which contained errors of five kinds, following Mertin`s (2006) error typology. Translation students and professionals saw translations which contained errors produced by human translators and which had to be revised. Vardaro et al (2019) followed the same logic, but first determined the frequency of error types produced by the EU commission`s NMT system and then pre- sented single sentences containing errors based on the MQM typology. Participants in Vardaro et al (2019) were professional translators employed by the EU. For the current pur- pose, we present the results from a comparison between those 30 professionals in Vardaro et al (2019) and a group of 30 translation students. We presented the same materials as in Vardaro et al (2019) and tracked participants' eye movements and keystrokes. Results show that translation competence interacts with how errors are recognized and corrected during post-editing. We discuss the results of this study in relation to current models of the transla- tion process by contrasting the predictions these make with the evidence from our study
Syntactic Cross and Reading Effort in English to Japanese Translation (2022)

In English to Japanese translation, a linear translation refers to a translation in which the word order of the source text is kept as unchanged as possible. Previous research suggests that linear translation reduces the cognitive effort for interpreters and translators compared to the non-linear case. In this study, we empirically tested whether this was also the case in a mon- olingual setting from the viewpoint of reception study. The difference between linear and non-linear translation was defined using Cross values, which quantify how much reordering was required in Japanese translation relative to an English source text. Reading effort was measured by the average total reading time on the target text. In a linear mixed-effects model analysis, variations in reading time per participant and text type were also considered random effects. The results revealed that the reading effort for the linear translation was smaller than that for the non-linear translation. In addition, the accuracy of text comprehension was also found to affect the reading time