When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context. Recent work has focused primarily on answering questions based on some relevant document or content, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a difficult new dataset for commonsense question answering. To capture common sense beyond associations, each question discriminates between three target concepts that all share the same relationship to a single source drawn from ConceptNet (Speer et al., 2017). This constraint encourages crowd workers to author multiple-choice questions with complex semantics, in which all candidates relate to the subject in a similar way. We create 9,500 questions through this procedure and demonstrate the dataset's difficulty with a large number of strong baselines. Our best baseline, the OpenAI GPT (Radford et al., 2018), obtains 54.8% accuracy, well below human performance, which is 95.3%.

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution conditioned on the corresponding answer, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves multi-pass decoder to integrate with structural knowledge for diversity. Series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.

Abstract-- MAC Net [3] is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. This makes it an ideal candidate for solving tasks that involve logical reasoning. Our experiments with 20 bAbI tasks, demonstrate the value of MAC net as a data-efficient and interpretable architecture for Natural Language Question Answering. The transparent nature of MAC net provides a highly granular view of the reasoning steps taken by the network in answering a query. There is a growing interest in the Machine Learning community to build explainable Artificial Intelligence.

In my experience as a C-level executive and long-time AI professional, I've learned that people who want to utilize artificial Intelligence find getting started to be the most difficult part. Even the more confident practitioners could easily become intimidated by the array and complexity of tools to navigate. But this problem is now a thing of the past. With IBM Watson Studio, you and your project can now hit the ground running. IBM Watson Studio's integrated environment makes AI significantly easier, by allowing users to quickly and easily build visually appealing projects and models. I don't have the luxury to get bogged down in inefficient and slow processes.

After logging into Watson Studio, select New Modeler Flow. Enter a name, keep the default settings, and then click Create. Next expand the Import menu, drag the Data Asset node onto the stream canvas and select Titanic training data file (train.csv) in the node settings to load data into the project. Right-click the node and select Preview to see your detailed dataset. To build a modeler stream look under Record Operations.

Within the search marketing space, there has been a lot of talk about voice search. Many are projecting voice search as the next big thing – in fact, as the next marketplace disruptor. But the truth is, voice search probably isn't going to be the next big thing. Yes, voice search is disrupting text-based searches, and this is causing a few raised eyebrows. However, voice is only a small part of the disruption that's happening today.

IBM's Watson supercomputer has beat Jeopardy champions, reconstituted recipes, and even helped create highlight reels for the World Cup. Now it's taking on a new tech challenge; changing how the construction industry operates. A new partnership between IBM and Fluor, a global engineering and construction company, will put the supercomputer's computational skills to work on making building more efficient. The new Watson-based system, in development since 2015 and now in use on select projects, will be able to analyze a job site "like a doctor diagnoses a patient," according to Leslie Lindgren, Fluor's vice president of Information Management. That degree of risk analysis, predictive logistics, and comprehension is no small challenge given the complexity of today's construction megaprojects.

Within the search marketing space, there has been a lot of talk about voice search. Many are projecting voice search as the next big thing – in fact, as the next marketplace disruptor. But the truth is, voice search probably isn't going to be the next big thing. Yes, voice search is disrupting text-based searches, and this is causing a few raised eyebrows. However, voice is only a small part of the disruption that's happening today.

Abstract--With the rapid development of knowledge base, question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type, outdegree. Experimental results show that context information can improve the result of simple question answering. Question answering (QA) is a classic natural language processing task, which aims at building systems that automatically answer questions formulated in natural language [1]. In recent years, several large-scale general purpose knowledge bases (KBs) have been constructed, including Freebase [2], YAGO [3], DBpedia [4] and Wikidata [5] .

Many services that perform information retrieval for Points of Interest (POI) utilize a Lucene-based setup with spatial filtering. While this type of system is easy to implement it does not make use of semantics but relies on direct word matches between a query and reviews leading to a loss in both precision and recall. To study the challenging task of semantically enriching POIs from unstructured data in order to support open-domain search and question answering (QA), we introduce a new dataset POIReviewQA. It consists of 20k questions (e.g."is this restaurant dog friendly?") for 1022 Yelp business types. For each question we sampled 10 reviews, and annotated each sentence in the reviews whether it answers the question and what the corresponding answer is. To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question. We build a Lucene-based baseline model, which achieves 77.0% AUC and 48.8% MAP. A sentence embedding-based model achieves 79.2% AUC and 41.8% MAP, indicating that the dataset presents a challenging problem for future research by the GIR community. The result technology can help exploit the thematic content of web documents and social media for characterisation of locations.