Hilário Alencar, G. Pacelli Bessa, Gregório Silva Neto
In this paper, we establish nonexistence results for complete translating solitons of the r-mean curvature flow under suitable growth conditions on the (r-1)-mean curvature and on the norm of the second fundamental form. We first show that such solitons cannot be entirely contained in the complement of a right rotational cone whose axis of symmetry is aligned with the translation direction. We then relax the growth condition on the (r-1)-mean curvature and prove that properly immersed translating solitons cannot be confined to certain half-spaces opposite to the translation direction. We conclude the paper by showing that complete, properly immersed translating solitons satisfying appropriate growth conditions on the (r-1)-mean curvature cannot lie completely within the intersection of two transversal vertical half-spaces.
Marileide Dias Esqueda, Igor Antônio Lourenço da Silva
The escalating global prominence of the game industry underscores the critical need for specialized translation and localization expertise. This article presents a case study of the Undergraduate Program in Translation at Universidade Federal de Uberlândia, a Brazilian public university that has been providing dedicated training in game localization for the last 15 years. The core of the Federal University of Uberlândia’s offering is a 60-hour course, that spans foundational localization concepts like transcreation and culturalization, as well as advanced technical skills, including computer-assisted translation, machine translation, and generative artificial intelligence. Its pedagogical approach integrates theoretical knowledge with practical application through lectures, workshops, and game localization projects, frequently leveraging open-source resources. Students gain experience by collaborating in teams, and emulating professional workflows. Further enhancing the training, students complete a senior thesis, often focusing on game localization, where they detail their experiences in technically adapting games. This research draws upon existing literature and employs an autoethnographic approach, critically reflecting on our experiences as trainers and thesis supervisors through personal archives, didactic materials, and learning tasks. Complementarily, a bibliometric and content analysis of past senior theses provides empirical data. A notable challenge addressed is the scarcity of game localization-specific teaching materials, prompting the program’s proactive development of resources tailored to the Brazilian market. This study thus aims to contribute to a specialized localization training.
Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. Does this finding also hold for speech translation (ST) models? If so, what are the implications for the speaker's gender assignment in translation? We address these questions from an interpretability perspective, using probing methods to assess gender encoding across diverse ST models. Results on three language directions (English-French/Italian/Spanish) indicate that while traditional encoder-decoder models capture gender information, newer architectures -- integrating a speech encoder with a machine translation system via adapters -- do not. We also demonstrate that low gender encoding capabilities result in systems' tendency toward a masculine default, a translation bias that is more pronounced in newer architectures.
Ancient people translated classical Chinese into Japanese using a system of annotations placed around characters. We abstract this process as sequence tagging tasks and fit them into modern language technologies. The research on this annotation and translation system faces a low resource problem. We alleviate this problem by introducing an LLM-based annotation pipeline and constructing a new dataset from digitized open-source translation data. We show that in the low-resource setting, introducing auxiliary Chinese NLP tasks enhances the training of sequence tagging tasks. We also evaluate the performance of Large Language Models (LLMs) on this task. While they achieve high scores on direct machine translation, our method could serve as a supplement to LLMs to improve the quality of character's annotation.
El artículo examina la cuestión de la anámnesis en la filosofía platónica, considerada como la caracterización de todo aprender como “recordar” en el Menón, el Fedro y el Fedón. Para tratar esta problemática, se presentan las presuposiciones necesarias de cualquier lectura de la anámnesis, notablemente la naturaleza de las formas contempladas y cómo se relacionan con nuestra comprensión. Con este fin, se recurre a las interpretaciones de Eric D. Perl y Hans-Georg Gadamer para “situar” la anámnesis dentro de una tradición interpretativa hermenéutico-filosófica de Platón. Posteriormente, se interpreta la anámnesis como una descripción del aprendizaje, y el “medio” que vincula nuestra comprensión y el ser revelado inteligiblemente —punto no desarrollado
por Perl o Gadamer—. La anámnesis se revela como “recordar lo eterno y siempre presente”, pasando de lo conceptual y lingüísticamente indeterminado a lo determinado. Finalmente, se sugieren vínculos entre la interpretación propuesta, la filosofía de Platón, Aristóteles y Gadamer.
This article aims to discuss untranslatability as a myth, using the Portuguese concept of saudade as an example. The first part is dedicated to reconstructing the most significant moments in the development of the concept, with particular emphasis on reflections regarding its translatability into other languages. The second part proposes employing Barthes’ concept of myth in the reflection on untranslatability, which will be briefly illustrated with a contemporary example: the lyrics of the song representing Portugal at the 66th Eurovision Song Contest in Turin in 2022. The third part presents concluding remarks that also serve as a basis for further considerations on the issue of untranslatability, including its role in reinforcing unequal power relations between languages.
Sign language translation (SLT) addresses the problem of translating information from a sign language in video to a spoken language in text. Existing studies, while showing progress, are often limited to narrow domains and/or few sign languages and struggle with open-domain tasks. In this paper, we push forward the frontier of SLT by scaling pretraining data, model size, and number of translation directions. We perform large-scale SLT pretraining on different data including 1) noisy multilingual YouTube SLT data, 2) parallel text corpora, and 3) SLT data augmented by translating video captions to other languages with off-the-shelf machine translation models. We unify different pretraining tasks with task-specific prompts under the encoder-decoder architecture, and initialize the SLT model with pretrained (m/By)T5 models across model sizes. SLT pretraining results on How2Sign and FLEURS-ASL#0 (ASL to 42 spoken languages) demonstrate the significance of data/model scaling and cross-lingual cross-modal transfer, as well as the feasibility of zero-shot SLT. We finetune the pretrained SLT models on 5 downstream open-domain SLT benchmarks covering 5 sign languages. Experiments show substantial quality improvements over the vanilla baselines, surpassing the previous state-of-the-art (SOTA) by wide margins.
Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the omitted particle X ('DE'). In news article titles from the Penn Chinese Discourse Treebank, we developed a targeted dataset to fine-tune Hugging Face Chinese to English translation models, specifically improving how this critical function word is handled. This focused approach not only complements the broader strategies suggested by previous studies but also offers a practical enhancement by specifically addressing a common error type in Chinese-English translation.
This article aims to analyse S.T. Coleridge’s theory of suspension of disbelief and poetic faith, which seems to overshadow a conception of the literary work as displaying a “separate universe” capable of reconfiguring the experience of everyday reality. This theory, particularly through the mediation of Owen Barfield, exerts a considerable influence on J.R.R. Tolkien’s essay On Fairy-stories, which enters subtle controversy with Coleridge and opposes and opposes the suspension of disbelief with his “Secondary Belief”. The difference between the two authors can shed light on dissimilar conceptions of the ontological status of the fictional worlds.
Geography. Anthropology. Recreation, Language. Linguistic theory. Comparative grammar
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings. However, we find that such relation-level modeling cannot capture the diverse relational structures of KGs well. In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings. TransEdge achieves promising performance on different prediction tasks. Our experiments on benchmark datasets indicate that it obtains the state-of-the-art results on embedding-based entity alignment. We also show that TransEdge is complementary with conventional entity alignment methods. Moreover, it shows very competitive performance on link prediction.
Connah Kendrick, B. Cassidy, Joseph M Pappachan
et al.
Diabetic foot ulcer is a severe condition that requires close monitoring and management. For training machine learning methods to auto-delineate the ulcer, clinical staff must provide ground truth annotations. In this paper, we propose a new diabetic foot ulcers dataset, namely DFUC2022, the largest segmentation dataset where ulcer regions were manually delineated by clinicians. We assess whether the clinical delineations are machine interpretable by deep learning networks or if image processing refined contour should be used. By providing benchmark results using a selection of popular deep learning algorithms, we draw new insights into the limitations of DFU wound delineation and report on the associated issues. This paper provides some observations on baseline models to facilitate DFUC2022 Challenge in conjunction with MICCAI 2022. The leaderboard will be ranked by Dice score, where the best FCN-based method is 0.5708 and DeepLabv3+ achieved the best score of 0.6277. This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results. DFUC2022 will be released on the 27th April 2022.
Randomized controlled trials (RCTs) have shown high efficacy of multiple vaccines against SARS-CoV-2 disease (COVID-19), but evidence remains scarce about vaccines' efficacy against infection with, and ability to transmit, the virus. We describe an approach to estimate these vaccines' effects on viral positivity, a prevalence measure which under reasonable assumptions forms a lower bound on efficacy against transmission. Specifically, we recommend separate analysis of positive tests triggered by symptoms (usually the primary outcome) and cross-sectional prevalence of positive tests obtained regardless of symptoms. The odds ratio of carriage for vaccine vs. placebo provides an unbiased estimate of vaccine effectiveness against viral positivity, under certain assumptions, and we show through simulations that likely departures from these assumptions will only modestly bias this estimate. Applying this approach to published data from the RCT of the Moderna vaccine, we estimate that one dose of vaccine reduces the potential for transmission by at least 61%, possibly considerably more. We describe how these approaches can be translated into observational studies of vaccine effectiveness.
T. Völker, M. Mazzonetto, Rasmus Slaattelid
et al.
Introduction By a series of calls within the Horizon 2020 framework programme, the EU funded projects intended to deploy Responsible Research and Innovation (RRI) at a territorial level, in regional research and innovation ecosystems. This paper presents efforts to document and evaluate the achievements in TRANSFORM, one of these projects. Methods Evaluative inquiry and theoretical reasoning. Results Noting the need for a general principle to be interpreted, adapted and translated in order to be rendered meaningful at a local level, we studied precisely these multiple territorial translations of RRI, the organizational and institutional orderings with which they co-emerge and the challenges that come with these translations. An important shared feature is that RRI work does not start from zero, but rather builds on pre-existing relationships and repertoires of collaboration. The RRI project is hence a way to continue ongoing work and follow pre-set purposes, aims and objectives, as a form of “maintenance work”. In this very human sense, RRI is deployed with a logic of care in the regional context, while the Horizon 2020 calls and proposals above all are formulated in a logic of choice, to be assessed by indicators. Discussion We warn against undue standardization of RRI by toolification and use of quantitative indicators, and recommend that RRI performance is monitored by methods of evaluative inquiry.
Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code translation datasets only focus on a single pair of popular programming languages. To advance research on code translation and meet diverse requirements of real-world applications, we construct CodeTransOcean, a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. CodeTransOcean consists of three novel multilingual datasets, namely, MultilingualTrans supporting translations between multiple popular programming languages, NicheTrans for translating between niche programming languages and popular ones, and LLMTrans for evaluating executability of translated code by large language models (LLMs). CodeTransOcean also includes a novel cross-framework dataset, DLTrans, for translating deep learning code across different frameworks. We develop multilingual modeling approaches for code translation and demonstrate their great potential in improving the translation quality of both low-resource and high-resource language pairs and boosting the training efficiency. We also propose a novel evaluation metric Debugging Success Rate@K for program-level code translation. Last but not least, we evaluate LLM ChatGPT on our datasets and investigate its potential for fuzzy execution predictions. We build baselines for CodeTransOcean and analyze challenges of code translation for guiding future research. The CodeTransOcean datasets and code are publicly available at https://github.com/WeixiangYAN/CodeTransOcean.
We studied the capability of automated machine translation in the online video education space by automatically translating Khan Academy videos with state-of-the-art translation models and applying text-to-speech synthesis and audio/video synchronization to build engaging videos in target languages. We also analyzed and established two reliable translation confidence estimators based on round-trip translations in order to efficiently manage translation quality and reduce human translation effort. Finally, we developed a deployable system to deliver translated videos to end users and collect user corrections for iterative improvement.