With the development of information technology, there is an explosive growth in the number of online comment concerning news, blogs and so on. The massive comments are overloaded, and often contain some misleading and unwelcome information. Therefore, it is necessary to identify high-quality comments and filter out low-quality comments. In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments. First, we construct a news comment corpus, which consists of news, comments, and the corresponding quality label. Second, we analyze the dataset, and find the quality of comments can be measured in three aspects: informativeness, consistency, and novelty. Finally, we propose a novel multi-target text matching model, which can measure three aspects by referring to the news and surrounding comments. Experimental results show that our method can outperform various baselines by a large margin on the news dataset.

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.

In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, product title summarization has some extra and essential constraints. For example, factual detail errors or loss of the key information are intolerable for E-commerce applications. Therefore, we abstract two more constraints for product title summarization: (i) do not introduce irrelevant information; (ii) retain the key information (e.g., brand name and commodity name). To address these issues, we propose a novel multi-source pointer network by adding a new knowledge encoder for pointer network. The first constraint is handled by pointer mechanism, generating the short title by copying words from the source title. For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism. For evaluation, we build a large collection of real-world product titles along with human-written short titles. Experimental results demonstrate that our model significantly outperforms the other baselines.Finally, online deployment of our proposed model has yielded a significant business impact, as measured by the click-through rate.

A common question when studying a class of context-free grammars (CFGs) is whether equivalence is decidable within this class. We answer this question positively for the class of Clark-congruential grammars, which are of interest to grammatical inference. We also consider the problem of checking whether a given CFG is Clark-congruential, and show that it is decidable given that the CFG is a deterministic CFG.

Aug 20 (Reuters) - Nvidia Corp on Monday released a new generation of gaming chips that combine its latest "ray tracing" technology and artificial intelligence to give gamers access to more realistic graphics. At a Gamescom 2018 press conference in Cologne, Germany, Nvidia rolled out its newest generation of gaming chips - the RTX 2070, RTX 2080 and RTX 2080 Ti. They are based on its recently launched chip blueprint called "Turing," which Nvidia is also using for higher-priced chips announced last week for game designers, movie makers and other computer graphics professionals. The chips will range from $499 to $999 and will be available in retail stores starting Sept. 20, Chief Executive Jensen Huang said at the event.

At a Gamescom 2018 press conference in Cologne, Germany, Nvidia rolled out its newest generation of gaming chips - the RTX 2070, RTX 2080 and RTX 2080 Ti. They are based on its recently launched chip blueprint called "Turing," which Nvidia is also using for higher-priced chips announced last week for game designers, movie makers and other computer graphics professionals. The chips will range from $499 to $999 and will be available in retail stores starting Sept. 20, Chief Executive Jensen Huang said at the event. The biggest selling point of the gaming chips released on Monday is an improvement in so-called "real-time ray tracing," or the ability for the chip to simulate how light rays will bounce around in a visual scene, which helps video games and other computer graphics more closely resemble shadows and reflections in the real world. Part of how the new chip achieves high resolution graphics quickly is a special section of the chip that finishes most of the image but then uses artificial intelligence to guess what the unfinished pixels should look like.

Britain faces the threat of social unrest as robots take'swathes' of jobs, the Bank of England's chief economist warned today. Andy Haldane said the so-called Fourth Industrial Revolution will see'the machine replacing humans doing thinking things'. He cautioned that the'dark side' of the change could be disruption on a much bigger scale than in Victorian times, with professions such as accountancy among those at risk. The stark message came amid calls for a massive skills drive to find employment for those set to be affected by the next wave of automation. In an interview with BBC Radio 4's Today programme, Mr Haldane said: 'The first three industrial revolutions have been about largely machines replacing humans doing principally manual tasks, whereas the fourth will be different.

Check out the full schedule at the Strata Data Conference in New York, September 11-13, 2018. Interest in machine learning (ML) has been growing steadily, and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. The reality is that we are still in the early phases of adoption, and a majority of companies have yet to deploy ML across their operations. In this post, I'll describe how one can go from "exploration and evaluation" to actual "implementation" of ML technologies. Along the way, I'll highlight key sections of the upcoming Strata Data conference in New York this September.

The rise of artificial intelligence will have a "dark" fallout that is even more disruptive than previous industrial revolutions, the Bank of England's chief economist has warned. Andy Haldane predicted that the "Fourth Industrial Revolution" would be on a much greater scale than those of the 18th to 20th centuries and would lead to huge job losses and societal changes. Automation has already displaced millions of low-skilled jobs. Experts predict that artificial intelligence (AI) will displace millions more roles in the next two decades as technologies such as driverless cars and lorries and software that can perform increasingly sophisticated roles come into use. The Serious Fraud Office already uses AI instead of barristers to sift through case documents to identify relevant evidence.