Machine translation quality in an audiovisual context

The volume of Audiovisual Translation (AVT) is increasing to meet the rising demand for data that needs to be accessible around the world. Machine Translation (MT) is one of the most innovative technologies to be deployed in the field of translation, but it is still too early to predict how it can support the creativity and productivity of professional translators in the future. Currently, MT is more widely used in (non-AV) text translation than in AVT. In this article, we discuss MT technology and demonstrate why its use in AVT scenarios is particularly challenging. We also present some potentially useful methods and tools for measuring MT quality that have been developed primarily for text translation. The ultimate objective is to bridge the gap between the tech-savvy AVT community, on the one hand, and researchers and developers in the field of high-quality MT, on the other

Script Independent Morphological Segmentation for Arabic Maghrebi Dialects: An Application to Machine Translation

International audienceThis research deals with resources creation for under-resourced languages. We try to adapt existing resources for other resourced-languages to process less-resourced ones. We focus on Arabic dialects of the Maghreb, namely Algerian, Moroccan and Tunisian. We first adapt a well-known statistical word segmenter to segment Algerian dialect texts written in both Arabic and Latin scripts. We demonstrate that unsupervised morphological segmentation could be applied to Arabic dialects regardless of used script. Next, we use this kind of segmentation to improve statistical machine translation scores between the tree Maghrebi dialects and French. We use a parallel multidialectal corpus that includes six Arabic dialects in addition to MSA and French. We achieved interesting results. Regards to word segmentation, the rate of correctly segmented words reached 70% for those written in Latin script and 79% for those written in Arabic script. For machine translation, the unsupervised morphological segmentation helped to decrease out-of-vocabulary words rates by a minimum of 35%

Survey on Automated Short Answer Grading with Deep Learning:from Word Embeddings to Transformers

Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the futur

Findings of the 2011 Workshop on Statistical Machine Translation

This paper presents the results of the WMT11 shared tasks, which included a translation task, a system combination task, and a task for machine translation evaluation metrics. We conducted a large-scale manual evaluation of 148 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 21 evaluation metrics. This year featured a Haitian Creole to English task translating SMS messages sent to an emergency response service in the aftermath of the Haitian earthquake. We also conducted a pilot 'tunable metrics' task to test whether optimizing a fixed system to different metrics would result in perceptibly different translation quality