We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data. One popular strategy is to reduce multilingual alignment to the much simplified bilingual setting, by picking one of the input languages as the pivot language that we transit through. However, it is well-known that transiting through a poorly chosen pivot language (such as English) may severely degrade the translation quality, since the assumed transitive relations among all pairs of languages may not be enforced in the training process. Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative ''mean'' language: it encapsulates information from all languages and minimizes all pairwise transportation costs. We evaluate our method on standard benchmarks and demonstrate state-of-the-art performances.

Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is a common way of assisting readers in their news consumption. However, it remains a challenging research problem to efficiently and effectively generate a representative headline for each story. Automatic summarization of a document set has been studied for decades, while few studies have focused on generating representative headlines for a set of articles. Unlike summaries, which aim to capture most information with least redundancy, headlines aim to capture information jointly shared by the story articles in short length, and exclude information that is too specific to each individual article. In this work, we study the problem of generating representative headlines for news stories. We develop a distant supervision approach to train large-scale generation models without any human annotation. This approach centers on two technical components. First, we propose a multi-level pre-training framework that incorporates massive unlabeled corpus with different quality-vs.-quantity balance at different levels. We show that models trained within this framework outperform those trained with pure human curated corpus. Second, we propose a novel self-voting-based article attention layer to extract salient information shared by multiple articles. We show that models that incorporate this layer are robust to potential noises in news stories and outperform existing baselines with or without noises. We can further enhance our model by incorporating human labels, and we show our distant supervision approach significantly reduces the demand on labeled data.

Otter.ai to Bring AI-Powered Meeting Note Collaboration Service to Japan in partnership with NTT DOCOMO Partnership includes Investment and Customer Trials of Otter's Real-Time Transcription Los Altos, CA, January 23, 2020 –Otter.ai DOCOMO made a strategic investment in Otter through its wholly-owned subsidiary NTT DOCOMO Ventures, Inc. and announced plans for its AI-based translation service subsidiary to integrate Otter's meeting note collaboration into its offering to provide highly accurate English transcripts translated into Japanese. As a part of Otter's customer engagement with DOCOMO the Otter Voice Meeting Notes application is being used on a trial basis in Berlitz Corporation's English language classes in Japan. Students use Otter to transcribe and review the content of lessons, click on sections of text, and initiate voice playback. DOCOMO, Otter.ai and Berlitz are expanding their collaboration in language education to verify Otter's effectiveness in the study of English DOCOMO is featuring Otter during demonstrations at the DOCOMO Open House 2020, taking place in the Tokyo Big Sight exhibition complex January 23 and 24, 2020.

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mask-predict, producing training examples that contain model predictions as part of their inputs. Models trained with SMART produce higher-quality translations when using mask-predict decoding, effectively closing the remaining performance gap with fully autoregressive models.

Deep neural machine translation (NMT) can learn representations containing linguistic information. And despite the differences between various models, they all tend to learn similar properties. This phenomena got researchers wondering whether the learned information is fully distributed and embedded to individual neurons. Recent research results confirmed that hypothesis, revealing that simple properties such as coordinating conjunctions and determiners can be attributed to individual neurons, while more complex linguistic properties such as syntax and semantics are distributed across multiple neurons. Following on this, researchers from The Chinese University of Hong Kong, Tencent AI Lab and University of Macau have proposed a new neuron interaction based representation composition for NMT.

Machine learning is a trendy topic in this age of Artificial Intelligence. The fields of computer vision and Natural Language Processing (NLP) are making breakthroughs that no one could've predicted. We see both of them in our lives more and more, facial recognition in your smartphones, language translation software, self-driving cars and so on. What might seem sci-fi is becoming a reality, and it is only a matter of time before we attain Artificial General Intelligence. In this article, I will be covering Jeff Dean's keynote on the advancements of computer vision and language models and how ML will progress towards the future from the perspective of model building.

Any high school student would guess there is a cosine involved when they see an integral of a sine. Regardless of whether the person understands the thought process behind these functions, it does the job for them. This intuition behind calculus is rarely explored. Though Newton and Leibnitz developed advanced mathematics to solve real-world problems, today most of the schools teach differential equations through semantics. The linguistic appeal of mathematics might get grades in high school, but in the world of research, this is hysterical.

Does anyone know of scientific literature that shows that, even in cases in which we have enough parallel data (English-French), use of monolingual data can be beneficial? To me it seems reasonable that if we, for instance, added monolingual data to the decoder, it would be better at scoring candidate predictions in terms of fluency. That being said, I cannot find peer-reviewed articles that show this.

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

Google's I/O 2018 asserts one thing – the next wave of smartphones will run on a generous amount of Artificial Intelligence. Even the recent Mobile World Congress (MWC) also had conversations that were largely revolving around Artificial Intelligence. Major smartphones makers, led by Apple, Google, Samsung, and many others are creating operating systems, mobile apps and even smartphone that have Artificial Intelligence at their core. McKinsey Global Institute estimates that the investments in Artificial Intelligence R&D made by tech giants by Google and Baidu to be in the range of $20 Billion to $30 Billion. In fact, Ai is ranked to be one among the 5 disruptive Technologies that are shaping up our future digital landscape.