pwc-archive/papers-with-abstracts huggingface dataset:
SG Translate Together - Uplifting Singapore’s translation standards with the community through technology
Authors: Lee Siew Li, Adeline Sim, Gowri Kanagarajah, Siti Amirah, Foo Yong Xiang, Gayathri Ayathorai, Sarina Mohamed Rasol, Aw Ai Ti, Wu Kui, Zheng Weihua, Ding Yang, Tarun Kumar Vangani, Nabilah Binte Md Johan
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Cultural Vocal Bursts Intensity Prediction, Machine Translation, Translation

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
Authors: Deyan Zou
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

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.
Robust Translation of French Live Speech Transcripts
Authors: Elise Bertin-Lemée, Guillaume Klein, Josep Crego, Jean Senellart
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Sentence, speech-recognition, Speech Recognition, Translation

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
Authors: Evelyne Tzoukermann, Steven Van Guilder, Jennifer Doyon, Ekaterina Harke
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Speech-to-Text, Translation

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
Authors: Nikita Teslenko Grygoryev, Mercedes Garcia Martinez, Francisco Casacuberta Nolla, Amando Estela Pastor, Manuel Herranz
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Data Augmentation, Machine Translation, NMT, Translation

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.
Building and Analysis of Tamil Lyric Corpus with Semantic Representation
Authors: Karthika Ranganathan, Geetha T V
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9

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.
The graphical brain and deep inference
Authors: Karl Friston
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9

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
Authors: Junyi Mao
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

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
Authors: Longhui Zou, Ali Saeedi, Michael Carl
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

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
Authors: Haruka Ogawa
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Sentence, Translation

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
Authors: Moritz J Schaeffer
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: NMT, Translation

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
Authors: Takanori Mizowaki, Haruka Ogawa, Masaru Yamada
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Reading Comprehension, Translation

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
Proficiency and External Aides: Impact of Translation Brief and Search Conditions on Post-editing Quality
Authors: Longhui Zou, Michael Carl, Masaru Yamada, Takanori Mizowaki
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

This study investigates the impact of translation briefs and search conditions on post-editing (PE) quality produced by participants with different levels of translation proficiency. We hired five Chinese student translators and seven Japanese professional translators to conduct full post-editing (FPE) and light post-editing (LPE), as described in the translation brief, while controlling two search conditions i.e., usage of a termbase (TB) and internet search (IS). Our results show that FPE versions of the final translations tend to have less errors than LPE ver- sions. The FPE translation brief improves participants’ performance on fluency as compared to LPE, whereas the search condition of TB helps to improve participants’ performance on accuracy as compared to IS. Our findings also indicate that the occurrences of fluency errors produced by experienced translators (i.e., the Japanese participants) are more in line with the specifications addressed in translation briefs, whereas the occurrences of accuracy errors pro- duced by inexperienced translators (i.e., our Chinese participants) depend more on the search conditions.
Entropy as a measurement of cognitive load in translation
Authors: Yuxiang Wei
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Translation

In view of the “predictive turn” in translation studies, empirical investigations of the translation process have shown increasing interest in studying features of the text which can predict translation efficiency and effort, especially using large-scale experimental data and rigorous statistical means. In this regard, a novel metric based on entropy (i.e., HTra) has been proposed and experimentally studied as a predictor variable. On the one hand, empirical studies show that HTra as a product-based metric can predict effort, and on the other, some conceptual analyses have provided theoretical justifications of entropy or entropy reduction as a description of translation from a process perspective. This paper continues the investigation of entropy, conceptually examining two ways of quantifying cognitive load, namely, shift of resource allocation and reduction of entropy, and argues that the former is represented by surprisal and ITra while the latter is represented by HTra. Both can be approximated via corpus-based means and used as potential predictors of effort. Empirical analyses were also conducted comparing the two metrics (i.e., HTra and ITra) in terms of their prediction of effort, which showed that ITra is a stronger predictor for TT production time while HTra is a stronger predictor for ST reading time. It is hoped that this would contribute to the exploration of dependable, theoretically justifiable means of predicting the effort involved in translation
A TensorFlow implementation of Local Binary Patterns Transform
Authors: Devrim Akgun
Date: 2021-06-30 00:00:00
Proceeding: MANAS Journal of Engineering 2021 6
Tasks: CPU, GPU

Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.
Normalizing Flows for Interventional Density Estimation
Authors: Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Date: 2022-09-13 00:00:00
Tasks: Causal Inference, Density Estimation

Existing machine learning methods for causal inference usually estimate quantities expressed via the mean of potential outcomes (e.g., average treatment effect). However, such quantities do not capture the full information about the distribution of potential outcomes. In this work, we estimate the density of potential outcomes after interventions from observational data. For this, we propose a novel, fully-parametric deep learning method called Interventional Normalizing Flows. Specifically, we combine two normalizing flows, namely (i) a nuisance flow for estimating nuisance parameters and (ii) a target flow for parametric estimation of the density of potential outcomes. We further develop a tractable optimization objective based on a one-step bias correction for efficient and doubly robust estimation of the target flow parameters. As a result, our Interventional Normalizing Flows offer a properly normalized density estimator. Across various experiments, we demonstrate that our Interventional Normalizing Flows are expressive and highly effective, and scale well with both sample size and high-dimensional confounding. To the best of our knowledge, our Interventional Normalizing Flows are the first proper fully-parametric, deep learning method for density estimation of potential outcomes.
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Authors: Adyasha Maharana, Darryl Hannan, Mohit Bansal
Date: 2022-09-13 00:00:00
Tasks: Image Generation, Story Continuation, Story Visualization, Video Captioning

Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a corresponding sequence of captions, forming a narrative. Moreover, we find that the story visualization task fails to accommodate generalization to unseen plots and characters in new narratives. Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters. Then, we enhance or 'retro-fit' the pretrained text-to-image synthesis models with task-specific modules for (a) sequential image generation and (b) copying relevant elements from an initial frame. Then, we explore full-model finetuning, as well as prompt-based tuning for parameter-efficient adaptation, of the pre-trained model. We evaluate our approach StoryDALL-E on two existing datasets, PororoSV and FlintstonesSV, and introduce a new dataset DiDeMoSV collected from a video-captioning dataset. We also develop a model StoryGANc based on Generative Adversarial Networks (GAN) for story continuation, and compare it with the StoryDALL-E model to demonstrate the advantages of our approach. We show that our retro-fitting approach outperforms GAN-based models for story continuation and facilitates copying of visual elements from the source image, thereby improving continuity in the generated visual story. Finally, our analysis suggests that pretrained transformers struggle to comprehend narratives containing several characters. Overall, our work demonstrates that pretrained text-to-image synthesis models can be adapted for complex and low-resource tasks like story continuation.