Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combinatorial problem, and turns a broad class of DP algorithms into differentiable operators. Theoretically, we provide a new probabilistic perspective on backpropagating through these DP operators, and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm for time-series alignment. We showcase these instantiations on two structured prediction tasks and on structured and sparse attention for neural machine translation.

Late last year, a St. Louis tech executive named Emre Şarbak noticed something strange about Google Translate. He was translating phrases from Turkish -- a language that uses a single gender-neutral pronoun "o" instead of "he" or "she." But when he asked Google's tool to turn the sentences into English, they seemed to read like a children's book out of the 1950's. The ungendered Turkish sentence "o is a nurse" would become "she is a nurse," while "o is a doctor" would become "he is a doctor." The website Quartz went on to compose a sort-of poem highlighting some of these phrases; Google's translation program decided that soldiers, doctors and entrepreneurs were men, while teachers and nurses were women.

StitchFix CEO Katrina Lake posted this on Twitter on the day of her company's IPO in 2017. Automated translation is using the same kind of models for suggesting words that are sometimes laced with bias. Parents know one particular challenge of raising kids all too well: teaching them to do what we say, not what we do. A similar challenge has hit artificial intelligence. As more apps and software use AI to automate tasks, a popular data-backed model, called "word embedding," has also picked up entrenched social biases.

Cornell University's automated online distribution system for research papers, Arxiv.org, is a prolific source for anyone interested in staying up to date on progress in neural machine translation (NMT). It has been almost a year from when we first wrote about the dramatic acceleration of academic NMT research as reflected on the number of papers submitted to Arxiv, and the upward trend continues. To understand where current research is heading, we reviewed NMT-related papers within the research repository for the first six weeks of 2018 as well as the last couple of months of the previous year. From November 1, 2017 to February 14, 2018, there were 58 relevant papers. Twelve of those papers are not directly about NMT specifically, but were focused on either machine learning via neural networks in general or adjacent technology such as natural language processing.

Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our $A^4NT$ to preserve the semantics of the input text. $A^4NT$ learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.

We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En-De and En-Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.

Translating is difficult work, the more so the further two languages are from one another. But sign language is a unique case, and translating it uniquely difficult, because it is fundamentally different from spoken and written languages. All the same, SignAll has been working hard for years to make accurate, real-time machine translation of ASL a reality. One would think that with all the advances in AI and computer vision happening right now, a problem as interesting and beneficial to solve as this would be under siege by the best of the best. Even thinking about it from a cynical market-expansion point of view, an Echo or TV that understands sign language could attract millions of new (and very thankful) customers.

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in https://github.com/mingyuliutw/unit .

Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning. Researchers have proposed different avenues such as neural machine translation for proof synthesis, vectorization of symbols and expressions for representing symbolic patterns, and coupling of neural back-ends for dimensionality reduction with symbolic front-ends for decision making. However, these initial explorations are still only point solutions, and bear other shortcomings such as lack of correctness guarantees. In this paper, we present our approach of casting symbolic reasoning as games, and directly harnessing the power of deep reinforcement learning in the style of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT) problem as showcase, we demonstrate the feasibility of our method, and the advantages of modularity, efficiency, and correctness guarantees.

During their first years of life, infants learn the language(s) of their environment at an amazing speed despite large cross cultural variations in amount and complexity of the available language input. Understanding this simple fact still escapes current cognitive and linguistic theories. Recently, spectacular progress in the engineering science, notably, machine learning and wearable technology, offer the promise of revolutionizing the study of cognitive development. Machine learning offers powerful learning algorithms that can achieve human-like performance on many linguistic tasks. Wearable sensors can capture vast amounts of data, which enable the reconstruction of the sensory experience of infants in their natural environment. The project of 'reverse engineering' language development, i.e., of building an effective system that mimics infant's achievements appears therefore to be within reach. Here, we analyze the conditions under which such a project can contribute to our scientific understanding of early language development. We argue that instead of defining a sub-problem or simplifying the data, computational models should address the full complexity of the learning situation, and take as input the raw sensory signals available to infants. This implies that (1) accessible but privacy-preserving repositories of home data be setup and widely shared, and (2) models be evaluated at different linguistic levels through a benchmark of psycholinguist tests that can be passed by machines and humans alike, (3) linguistically and psychologically plausible learning architectures be scaled up to real data using probabilistic/optimization principles from machine learning. We discuss the feasibility of this approach and present preliminary results.