One-Shot Neural Cross-Lingual Transfer for Paradigm Completion

We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58% higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.Comment: Accepted at ACL 201

Atar: Attention-based LSTM for Arabizi transliteration

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49)

The SP2 SCOPES Project on Speech Prosody

This is an overview of a Joint Research Project within the Scientific co-operation between Eastern Europe and Switzerland (SCOPES) Program of the Swiss National Science Foundation (SNFS) and Swiss Agency for Development and Cooperation (SDC). Within the SP2 SCOPES Project on Speech Prosody, in the course of the following two years, the four partners aim to collaborate on the subject of speech prosody and advance the extraction, processing, modeling and transfer of prosody for a large portfolio of European languages: French, German, Italian, English, Hungarian, Serbian, Croatian, Bosnian, Montenegrin, and Macedonian. Through the intertwined four research plans, synergies are foreseen to emerge that will build a foundation for submitting strong joint proposals for EU funding

Image and video manipulation: The generation of deepfakes

Podeu consultar el llibre complet a recurs relacionat[eng] The growth of fake news is nowadays a growing reality, leading to users feeling insecure when consuming information. Text-based news is perhaps the most manipulated, but the video is rapidly gaining ground. Technology allows content to be easily adulterated, leading users to become misinformed. In this study, we explain the different image, video, and audio manipulation techniques that are carried out with software and advanced audiovisual techniques. The most prominent methods are morphing and warping, together with machine learning techniques. Also, artificial intelligence is a critical element in the growth of fake video generation. This brings with it the need to identify the fake videos to verify the facts being told. It is also essential to understand social tolerance, especially in humour programmes, as some media outlets use these mechanisms. Moreover, it is analysed the importance of privacy policies that affect how the users’ personal information is collected and their implications in their fundamental rights. Finally, it is concluded with the need for media and digital literacy campaigns in the education system to minimise this problem.[spa] El crecimiento de las noticias falsas es hoy en día una realidad creciente, que lleva a los usuarios a sentirse inseguros cuando consumen información. Las noticias basadas en texto son quizás las más manipuladas, pero el vídeo está ganando terreno rápidamente. La tecnología permite adulterar fácilmente los contenidos, lo que lleva a los usuarios a estar mal informados. En este estudio se explican las diferentes técnicas de manipulación de imagen, vídeo y audio que se llevan a cabo con software y técnicas audiovisuales avanzadas. Los métodos más destacados son el morphing y el warping, junto con las técnicas de aprendizaje automático. Además, la inteligencia artificial es un elemento crítico en el crecimiento de la generación de vídeos falsos. Esto conlleva la necesidad de identificar los vídeos falsos para verificar los hechos que se cuentan. También es esencial entender la tolerancia social, especialmente en los programas de humor, ya que algunos medios de comunicación utilizan estos mecanismos. Además, se analiza la importancia de las políticas de privacidad que afectan a la recogida de información personal de los usuarios y sus implicaciones en sus derechos fundamentales. Finalmente, se concluye con la necesidad de realizar campañas de alfabetización mediática y digital en el sistema educativo para minimizar este problema