Many challenges in natural language processing require generating text, including language translation, dialogue generation, and speech recognition. For all of these problems, text generation becomes more difficult as the text becomes longer. Current language models often struggle to keep track of coherence for long pieces of text. Here, we attempt to have the model construct and use an outline of the text it generates to keep it focused. We find that the usage of an outline improves perplexity. We do not find that using the outline improves human evaluation over a simpler baseline, revealing a discrepancy in perplexity and human perception. Similarly, hierarchical generation is not found to improve human evaluation scores.

Humans may have forfeited our lead in recognizing tumors or judging credit risk, but we still have, and may always have, the final authority over what is or isn't "natural" in a natural language. This authority is reflected in the metric of choice for evaluating machine translation algorithms – the BLEU (bilingual evaluation understudy) – which scores candidate translations based on their similarities to a human professional's work. "The closer a machine translation is to a professional human translation, the better it is", concede the framework's inventors.

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.

This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input. Our model was trained only on the audio-to-text parallel data released for the task, and fine-tuned on cleaned subsets of the original training corpus. The addition of weight normalization and label smoothing improved the baseline system by 1.0 BLEU point on our validation set. The final submission also featured checkpoint averaging within a training run and ensemble decoding of models trained during multiple runs. On test data, our best single model obtained a BLEU score of 9.7, while the ensemble obtained a BLEU score of 10.24.

Abstract--Applying Machine Learning (ML) to business applications for automation usually faces difficulties when integrating diverse ML dependencies and services, mainly because of the lack of a common ML framework. In most cases, the ML models are developed for applications which are targeted for specific business domain use cases, leading to duplicated effort, and making reuse impossible. This paper presents Acumos, an open platform capable of packaging ML models into portable containerized microservices which can be easily shared via the platform's catalog, and can be integrated into various business applications. We present a case study of packaging sentiment analysis and classification ML models via the Acumos platform, permitting easy sharing with others. We demonstrate that the Acumos platform reduces the technical burden on application developers when applying machine learning models to their business applications. Furthermore, the platform allows the reuse of readily available ML microservices in various business domains. In recent years, there has been tremendous excitement around and interest in the potential of ML technologies.

TLDR: No! Your Machine Translation Model is not "prophesying", but let's look at the six major issues with neural machine translation (NMT). So I saw a Twitter thread today with the editor-in-chief of Motherboard tweeting, "Google Translate is popping out bizarre religious texts and no one is sure why". I am going to spend a little time on the "why" part (folks who work in MT know why), but mostly focus on actual problems with neural machine translation. The choice of headlines, the promotion tweet, and the tone of the article reminds me of all the irresponsible writing that went around the famous "Facebook Frankenstein" experiment. I would not be surprised if other media outlets picked up this Motherboard piece and ran ridiculous stories about machine translation conspiracy theories.

These sources contain images that viewers would have to interpret themselves. Most images do not have a description, but the human can largely understand them without their detailed captions. However, machine needs to interpret some form of image captions if humans need automatic image captions from it. Image captioning is important for many reasons. For example, they can be used for automatic image indexing. Image indexing is important for Content-Based Image Retrieval (CBIR) and therefore, it can be applied to many areas, including biomedicine, commerce, the military, education, digital libraries, and web searching. Social media platforms such as Facebook and Twitter can directly generate descriptions from images. The descriptions can include where we are (e.g., beach, cafe), what we wear and importantly what we are doing there.

Humans may have forfeited our lead in recognizing tumours or judging credit risk, but we still have, and may always have, the final authority over what is or isn't "natural" in a natural language. This authority is reflected in the metric of choice for evaluating machine translation algorithms - the BLEU (bilingual evaluation understudy) - which scores candidate translations based on their similarities to a human professional's work. "The closer a machine translation is to a professional human translation, the better it is", concede the framework's inventors.

Google announced this week that its Translate app for iOS and Android recognize 13 new languages through your smartphone's camera. The update, which includes support for Arabic and Hindi, is in the process of being rolled out to Translate users worldwide, per VentureBeat. In addition to Arabic and Hindi, the app now supports Bengali and Punjabi--four of the top 10 most spoken languages in the world, according to Ethnologue. Translate also added support for Gujarati, Kannada, Malayalam, Marathi, Nepali, Tamil, Telugu, Thai, and Vietnamese. Google Translate's "See" and "Snap" features allow you to point your camera at a sign or menu and watch the app translate the text in real time, or take a quick picture and let the app process any translatable text for you.

What did Claude Monet see as he placed his easel by the bank of the Seine near Argenteuil on a lovely spring day in 1873? A color photograph, had it been invented, may have documented a crisp blue sky and a glassy river reflecting it. Monet conveyed his impression of this same scene through wispy brush strokes and a bright palette. What if Monet had happened upon the little harbor in Cassis on a cool summer evening? A brief stroll through a gallery of Monet paintings makes it possible to imagine how he would have rendered the scene: perhaps in pastel shades, with abrupt dabs of paint, and a somewhat flattened dynamic range.