Segmentation-Free Streaming Machine Translation

Streaming Machine Translation (MT) is the task of translating an unbounded input text stream in real-time. The traditional cascade approach, which combines an Automatic Speech Recognition (ASR) and an MT system, relies on an intermediate segmentation step which splits the transcription stream into sentence-like units. However, the incorporation of a hard segmentation constrains the MT system and is a source of errors. This paper proposes a Segmentation-Free framework that enables the model to translate an unsegmented source stream by delaying the segmentation decision until the translation has been generated. Extensive experiments show how the proposed Segmentation-Free framework has better quality-latency trade-off than competing approaches that use an independent segmentation model. Software, data and models will be released upon paper acceptance.Comment: 11 pages, 5 figure

The spatial ultimatum game revisited

We revisit the issue of the emergence of fair behavior in the framework of the spatial Ultimatum game, adding many important results and insights to the pioneering work by Page et al. [2000. The spatial Ultimatum game. Proc. R. Soc. London B 267, 2177], who showed in a specific example that on a two dimensional setup evolution may lead to strategies with some degree of fairness. With in this spatial framework, we carry out a thorough simulation study and show that the emergence of altruism is a very generic phenomenon whose details depend on the dynamics considered. A very frequent feature is the spontaneous emergence and fixation of quasiempathetic individuals,whose offers are very close to their acceptance thresholds. We present analytical arguments that allow an understanding of our results and give insights on the manner in which local effects in evolution may lead to such non rational or apparently maladaptive behaviors.This work was supported in part by MICINN (Spain) through grants MOSAICO and RESINEE, and by Comunidad de Madrid (Spain) through grant MODELICO. J. I. is supported by a contract from the Consejería de Educación of the Comunidad de Madrid (Spain) and from the European Social Fund.Publicad

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research

Vrain at IroSvA 2019:Exploring classical and transfer learning approaches to short message irony detection

This paper describes VRAIN’s participation at IroSvA 2019: Irony Detection in Spanish Variants task of the Iberian Languagues Evaluation Forum (IberLEF 2019). We describe the entire pre-processing, feature extraction, model selection and hyperparameter optimization carried out for our submissions to the shared task. A central part of our work is to provide an in-depth comparison of the performance of different classical Machine learning techniques, as well as some recent transfer learning proposals for Natural Language Processing (NLP) classification problems.</p

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.Comment: 5 pages; EMNLP 2023 Accepted Versio

Vrain at IroSvA 2019:Exploring classical and transfer learning approaches to short message irony detection

This paper describes VRAIN’s participation at IroSvA 2019: Irony Detection in Spanish Variants task of the Iberian Languagues Evaluation Forum (IberLEF 2019). We describe the entire pre-processing, feature extraction, model selection and hyperparameter optimization carried out for our submissions to the shared task. A central part of our work is to provide an in-depth comparison of the performance of different classical Machine learning techniques, as well as some recent transfer learning proposals for Natural Language Processing (NLP) classification problems.</p

Teaching competence strategies to students through teamwork

El Espacio Europeo de Educación Superior plantea desarrollar y evaluar métodos de aprendizaje centrados en la adquisición de competencias, que permitirán al alumno enfrentarse a distintos tipos de situaciones, tanto en contextos académicos como profesionales. Este nuevo planteamiento, implica el uso de diferentes metodologías didácticas, entre las que se encuentra el trabajo en grupo. Sin embargo, para el profesor universitario es un desafío evaluar los resultados del aprendizaje en grupo y no siempre se muestra como una tarea sencilla. En el presente trabajo se realiza una aproximación a los diferentes métodos de evaluación de la competencia realizar trabajos en grupo, mediante una revisión bibliográfica y un cuestionario a profesores de la Universitat de València. La revisión bibliográfica indica que las tendencias actuales para la evaluación del trabajo en grupo requieren la combinación de diferentes estrategias, tales como la observación directa del profesor formalizada a través de rúbricas u hojas de evaluación, autoevaluación, coevaluación y tutorías docentes. Los resultados del cuestionario muestran que la mayoría del profesorado utiliza el trabajo en grupo como estrategia docente; sin embargo, no se establecen unos criterios formales para su evaluación. Las estrategias de evaluación del trabajo en grupo más utilizadas por los docentes son la observación directa en clase y durante la exposición de los resultados, bien a través de informes o bien a través de la presentación oral. Además, el papel que juega el alumno en el proceso de evaluación de esta competencia (autoevaluación y coevaluación) está escasamente potenciado por el docente.O espaço europeu de ensino superior levanta desenvolver e avaliar métodos de aprendizagem focando a aquisição de competências, que permitirá que o aluno lidar com diferentes tipos de situações, tanto em contextos académicos e profissionais. Esta nova abordagem, envolve o uso de diferentes metodologias didáticas, entre o grupo de trabalho. No entanto, um desafio para o Professor da faculdade de avaliação dos resultados do grupo de aprendizagem e não é sempre mostrado como uma tarefa simples. O presente trabalho é uma abordagem para os diferentes métodos de avaliação do grupo de trabalho de concorrência, através de uma revisão de literatura e um questionário com os professores da Universidade de Valência. A revisão de literatura indica que as tendências atuais para a avaliação do grupo de trabalho requerem a combinação de diferentes estratégias, tais como a observação direta do professor formalizada através de títulos ou folhas de avaliação, auto-avaliação, oportunidades e tutoriais educacionais. Resultados do questionário mostram que a maioria dos professores usar o grupo de trabalho como uma estratégia de ensino; No entanto, não são critérios formais estabelecidos para a avaliação. Avaliação das estratégias de trabalho de grupo usado por professores são observação direta em classe e durante a apresentação dos resultados, através de relatórios ou através da apresentação oral. O papel que desempenha o aluno no processo de avaliação deste concurso (auto-avaliação e oportunidades) é também mal alimentado pelo professor.The European Higher Education Area arise to develop and evaluate learning methods focused on the acquisition of competences that will enable students to face different kind of situations, both in academic and professional contexts. This new approach involves the use of different didactic methodologies, including teamwork. Nevertheless, teamwork learning evaluation is a challenge for the professor and it is not always an easy task. In the present paper we carried out an approach to the different methods for evaluating the competence "to do teamwork task" through a bibliographic review, and a questionnaire administrated to professors at the University of Valencia. The bibliographic review shows that the current tendency for evaluating teamwork demands require the combination of different strategies such as the professor direct observation formalized in rubrics or evaluation sheets, self-evaluation, co-evaluation and tutorship. The results of the questionnaire show that most of the professors use teamwork as teaching strategy. However, the evaluation criteria have not been formally established. The most used teamwork evaluation strategy is the direct observation in class, students presentation, and assignments. In addition, regarding the student role in teamwork evaluation (self-evaluation and co-evaluation) is hardly strengthened by professors

From Simultaneous to Streaming Machine Translation by Leveraging Streaming History

Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available. Currently, simultaneous translation is carried out by translating each sentence independently of the previously translated text. More generally, Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream. In this work, a state-of-the-art simultaneous sentence-level MT system is extended to the streaming setup by leveraging the streaming history. Extensive empirical results are reported on IWSLT Translation Tasks, showing that leveraging the streaming history leads to significant quality gains. In particular, the proposed system proves to compare favorably to the best performing systems.Comment: ACL 2022 - Camera ready; v3: expanded data pre-processin

Stream-level Latency Evaluation for Simultaneous Machine Translation

[EN] Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream-level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 761758 (X5Gon) and 952215 (TAILOR) and Erasmus+ Education program under grant agreement no. 20-226-093604-SCH; the Government of Spain's research project Multisub, ref. RTI2018-094879-B-I00 (MCIU/AEI/FEDER,EU) and FPU scholarships FPU18/04135; and the Generalitat Valenciana's research project Classroom Activity Recognition, ref. PROMETEO/2019/111.Iranzo-Sánchez, J.; Civera Saiz, J.; Juan, A. (2021). Stream-level Latency Evaluation for Simultaneous Machine Translation. The Association for Computational Linguistics. 664-670. http://hdl.handle.net/10251/182203S66467