Extractive Reading Comprehension (ERC) has made tremendous advances enabled by the availability of large-scale high-quality ERC training data. Despite of such rapid progress and widespread application, the datasets in languages other than high-resource languages such as English remain scarce. To address this issue, we propose a Cross-Lingual Transposition ReThinking (XLTT) model by modelling existing high-quality extractive reading comprehension datasets in a multilingual environment. To be specific, we present multilingual adaptive attention (MAA) to combine intra-attention and inter-attention to learn more general generalizable semantic and lexical knowledge from each pair of language families. Furthermore, to make full use of existing datasets, we adopt a new training framework to train our model by calculating task-level similarities between each existing dataset and target dataset. The experimental results show that our XLTT model surpasses six baselines on two multilingual ERC benchmarks, especially more effective for low-resource languages with 3.9 and 4.1 average improvement in F1 and EM, respectively.

Zoom has announced that it's acquiring a company known as Kites (short for Karlsruhe Information Technology Solutions), which has worked on creating real-time translation and transcription software. Zoom says the acquisition is a move to help it make communicating with people who speak different languages easier, and that it's looking to add translation capabilities to its video conferencing app. According to its site, Kites began at the Karlsruhe Institute of Technology, and its technology was originally developed to act as in-classroom translation for students who needed help understanding the English or German their professors were lecturing in. Zoom already has real-time transcriptions, but it's limited to people who are talking in English. On a support page, Zoom also makes it clear that its current live transcription feature may not meet certain accuracy requirements.

I hope the budding, online, free World University Medical School - http://worlduniversity.wikia.com/wiki/World_University_Medical_School Dr. Judy Palfrey is moving to Washington DC from the Boston area to help further Universal Health Care in the Obama administration, I think. WUaS is planning for a "Admitted Students' Day" for the first, matriculating Bachelor's degree class, on or around Saturday, April 14th, 2014, and the second Saturday of April for other degrees in the future. Prevent and Reverse Heart Disease: The Revolutionary, Scientifically Proven, Nutrition-Based Cure. Dr. Dean Ornish's Program for Reversing Heart Disease: The Only System Scientifically Proven to Reverse Heart Disease Without Drugs or Surgery.

The goal of Question Answering over Knowledge Graphs (KGQA) is to find answers for natural language questions over a knowledge graph. Recent KGQA approaches adopt a neural machine translation (NMT) approach, where the natural language question is translated into a structured query language. However, NMT suffers from the out-of-vocabulary problem, where terms in a question may not have been seen during training, impeding their translation. This issue is particularly problematic for the millions of entities that large knowledge graphs describe. We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. NMT is then used to create a query template with placeholders that are filled by entities identified in an EL phase. Slot filling is used to decide which entity fills which placeholder. Experiments for QA over Wikidata show that our approach outperforms pure NMT: while there remains a strong dependence on having seen similar query templates during training, errors relating to entities are greatly reduced.

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Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.

Video calling platforms and apps have taken on an unprecedented role since the arrival of Covid-19. One of the most important and popular is Zoom, which will now add a new real-time machine translation feature, after announcing the purchase of communications company Kites . Through its official blog, Zoom announced that they are in negotiations to acquire the company Karlsruhe Information Technology Solutions, abbreviated Kites . It is a German startup "dedicated to the development of real-time machine translation solutions" or MT, for its acronym in English. Zoom said that the acquisition of Kites represents the possibility of eliminating the language gaps between its users.

Despite their practical success, modern seq2seq architectures are unable to generalize systematically on several SCAN tasks. Hence, it is not clear if SCAN-style compositional generalization is useful in realistic NLP tasks. In this work, we study the benefit that such compositionality brings about to several machine translation tasks. We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task. Next, we study its performance in low-resource settings and on a newly introduced distribution-shifted English-French translation task. Overall, we find that improvements of a SCAN-capable model do not directly transfer to the resource-rich MT setup. In contrast, in the low-resource setup, general modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based metric is achieved in the introduced compositional English-French translation task. This provides experimental evidence that the compositional generalization assessed in SCAN is particularly useful in resource-starved and domain-shifted scenarios.

One of the wonders of the modern I.T. era is the extent to which technology has shrunken this world. Artificial Intelligence (AI) has given industry a global reach that can be traversed in mere nanoseconds. It is a brave new world. A reality that would have been the stuff of science fiction only a few short decades ago. I am off the vintage where myself and my co-workers had neither the worldwide web nor email to communicate with, and the cloud was just a fluffy white thing in the sky.

Zoom announced today it plans to acquire Karlsruhe Information Technology, a German startup that specializes in machine learning-based real-time translation. Also known as Kites, the company is made up of about a dozen researchers with ties to the Karlsruhe Institute of Technology. Zoom didn't share the financial terms of the deal, but did disclose that the startup will help it bring machine translation features to its platform. Moving forward, Zoom says it may also establish a research and development center in Germany. "We are continuously looking for new ways to deliver happiness to our users and improve meeting productivity, and [machine translation] solutions will be key in enhancing our platform for Zoom customers across the globe," said Velchamy Sankarlingam, president of product and engineering at Zoom.