The day that machines will surpass the ability of humans in language translation could still be many years away, following the breakdown of Tencent Holdings' artificial intelligence-powered translation system during the Boao Forum for Asia in Hainan province last week. Tencent's simultaneous translation system, which was designed to provide both interpretation and transcripts, made an error-filled debut at the high-profile forum, sometimes known as Asia's Davos. It spouted gibberish that were displayed live on screen at the event and in a WeChat mini program. These included garbled characters, repeated words and even broken Chinese, screenshots of which were widely circulated on social media last week. Tencent took the high ground by conceding the errors and aiming for improvements in future.

Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step forward in machine learning and programming languages. Unfortunately, as of now, this new crop of languages is hard to use and understand.

Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy. In this work, we address these three important aspects of a good summary via a reinforcement learning approach with two novel reward functions: ROUGE-Sal and Entail, on top of a coverage-based baseline. The ROUGESal reward modifies the ROUGE metric by up-weighting the salient phrases/words detected via a keyphrase classifier. The Entail reward gives high (lengthnormalized) scores to logically-entailed summaries using an entailment classifier. Further, we show superior performance improvement when these rewards are combined with traditional metric (ROUGE) based rewards, via our novel and effective multi-reward approach of optimizing multiple rewards simultaneously in alternate mini-batches. Our method achieves the new state-of-the-art results on CNN/Daily Mail dataset as well as strong improvements in a test-only transfer setup on DUC-2002.

We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logged feedback for offline bandit learning of NMT parameters. We conduct a thorough analysis of the available explicit user judgments---five-star ratings of translation quality---and show that they are not reliable enough to yield significant improvements in bandit learning. In contrast, we successfully utilize implicit task-based feedback collected in a cross-lingual search task to improve task-specific and machine translation quality metrics.

Evaluation in Monolingual Translation, and particularly in Grammatical Error Correction (GEC) is a challenging research field, much due to the difficulty in integrating different types of rewriting operations into a single measure, and the vast number of valid outputs (Tetreault and Chodorow, 2008; Madnani et al., 2011; Chodorow et al., 2012; Bryant and Ng, 2015). These difficulties have recently motivated a number of proposals for new, improved reference-based measures (RBMs) (Dahlmeier and Ng, 2012; Felice and Briscoe, 2015; Napoles et al., 2015). Nevertheless, the size and heterogeneity of the space of valid outputs per sentence often prohibits obtaining a reference set that covers this space well, thereby limiting the applicability of RBMs (Bryant and Ng, 2015).

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.

In the first blog, we will cover metrics in regression only. Most of the blogs have focussed on classification metrics like precision, recall, AUC etc. For a change, I wanted to explore all kinds of metrics including those used in regression as well. MAE and RMSE are the two most popular metrics for continuous variables. Let's start with the more popular one.

Stanford has long been considered one of the best universities in terms of teaching, quality of faculty and the content they teach. With the recent boom in the machine learning field, Stanford's ML courses have generated a lot of interest (you can find videos on YouTube if you haven't done so already). Each year, Stanford releases a list of projects that it's students have worked on and recently, in that same regard, has released a list of course projects for it's Natural Language Processing (NLP) course. And wow, is it impressive. Students were given two options for the project – either choose your own topic (called'Custom Project') or take part in the'Default Project', which was building Question Answering models based on the SQuAD challenge.

This article was originally posted on LinkedIn. In a few short years, Artificial Intelligence (AI) has been thrust into the limelight – elevating itself from a far-fetched, science-fiction topic to one that is currently dominating my conversations with customers, partners and industry leaders across Asia. The journey to where we are today with AI is a long one – almost seven decades in the making. However, in the last few years, the convergence of big data, ubiquitous and powerful cloud computing, along with breakthroughs in software algorithms and machine learning have made exciting new scenarios in AI deployment a possibility. AI today is at the center of the digital transformation of organizations and even nations.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.