Learning to combine foveal glimpses with a third-order Boltzmann machine. Multiple object recognition with visual attention. Show, attend and tell: neural image caption generation with visual attention. Neural machine translation by jointly learning to align and translate. Learning what and where to draw.

The quality of machine translation produced by state-of-the-art models is already quite high and often requires only minor corrections from professional human translators. This is especially true for high-resource language pairs like English-German and English-French. So, the main focus of recent research studies in machine translation was on improving system performance for low-resource language pairs, where we have access to large monolingual corpora in each language but do not have sufficiently large parallel corpora. Facebook AI researchers seem to lead in this research area and have introduced several interesting solutions for low-resource machine translation during the last year. This includes augmenting the training data with back-translation, learning joint multilingual sentence representations, as well as extending BERT to a cross-lingual setting.

News that Gong.io has acquired $40M to add their existing funding shows they are well on their way to transforming CRM systems with the use of artificial intelligence. Gong.io is relatively new on the scene but is already making big moves in the CRM industry. Their AI-assisted Conversation Intelligence and account analytics platform removes the conjectures and unknowns to assist sales and marketing teams to formulate winning sales strategies. Founded in 2015 by Amit Bendov, CEO, and Eilon Reshef, CTO, the San Francisco and Israel based company has two very successful people at the helm. Both founders have impressive resumes with proven track records in the growing, selling, and IPOing of startups.

The future of the language industry is bright. In a world where globalization brings us closer together, advances in technology make it easier than ever to communicate and conduct our work efficiently. The primary purpose of a machine is to facilitate a specific task; so, the question remains, why do so many of us fear the rise of artificial intelligence (AI)? Admittedly, the notion of a machine learning to navigate an area so intimately human as language is disquieting. Where do humans fit in an industry that is so eager to introduce machine learning technologies?

As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.

End-to-end optimization has achieved state-of-the-art performance on many specific problems, but there is no straight-forward way to combine pretrained models for new problems. Here, we explore improving modularity by learning a post-hoc interface between two existing models to solve a new task. Specifically, we take inspiration from neural machine translation, and cast the challenging problem of cross-modal domain transfer as unsupervised translation between the latent spaces of pretrained deep generative models. By abstracting away the data representation, we demonstrate that it is possible to transfer across different modalities (e.g., image-to-audio) and even different types of generative models (e.g., VAE-to-GAN). We compare to state-of-the-art techniques and find that a straight-forward variational autoencoder is able to best bridge the two generative models through learning a shared latent space. We can further impose supervised alignment of attributes in both domains with a classifier in the shared latent space. Through qualitative and quantitative evaluations, we demonstrate that locality and semantic alignment are preserved through the transfer process, as indicated by high transfer accuracies and smooth interpolations within a class. Finally, we show this modular structure speeds up training of new interface models by several orders of magnitude by decoupling it from expensive retraining of base generative models.

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.

In September of 2018, iFlytek, a Chinese technology company and world leader in A.I. -- particularly in voice recognition software -- was accused of disguising human translation as machine translation during a tech conference in Shanghai. The whistleblower was an interpreter, Bell Wang, who was doing live translation at the conference. He noticed that iFlytek was using his translations as live subtitles on a screen next to the company's brand logo. This gave the appearance that the translated output was produced by their A.I. system, rather than by Wang. The company was also broadcasting the translations live online using a computer-synthesized voice, instead of the original human interpreters' voices.

SDL a global leader in content creation, translation and delivery, today calls on brands to rethink current content strategies, and prepare for a digital future where content supply chains are autonomous, machine-first and human optimized, for greater impact with worldwide audiences, across any language and device. Companies are struggling to handle the growing volume and velocity of content required to engage with global audiences. And it's expected to get worse: 93% say the content they produce will increase in the next two years. SDL's Enabling the Future of Content report addresses these challenges, offering insights on how companies can move towards an autonomous content supply chain of the future, capable of delivering any type of content to global audiences. Peggy Chen, CMO, SDL said, "Engaging with customers globally requires content, and lots of it.

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.