To help advance question answering (QA) and create smarter assistants, Facebook AI is sharing the first large-scale data set, code, and baseline models for long-form QA, which requires machines to provide long, complex answers -- something that existing algorithms have not been challenged to do before. Current systems are focused on trivia-type questions, like whether jellyfish have a brain. Our data set goes further by requiring machines to elaborate with in-depth answers to open-ended questions, such as "How do jellyfish function without a brain?" Furthermore, our data set provides researchers with hundreds of thousands of examples to advance AI models that can synthesize information from multiple sources and provide explanations to complex questions across a wide range of topics. For truly intelligent assistants that can help us with myriad daily tasks, AI should be able to answer a wide variety of questions from people beyond straightforward, factual queries such as "Which artist sings this song?"

Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus on linguistic understanding, Open-BookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years. In this article, we provide an overview over these recent advancements, focusing on neural network based question answering systems over knowledge graphs. We introduce readers to the challenges in the tasks, current paradigms of approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point, and ease their process of making informed decisions while creating their own QA system.

To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark. It is under consideration for acceptance in TPLP.

Before Siri and Alexa, there was Watson. Appearing as a contestant on "Jeopardy!" made IBM's Watson a household name. But since its debut -- and win -- in 2011, the computer has morphed into something else entirely: An artificial intelligence tool for business. The company opened up Watson in the cloud wars, making the technology available on competitors' clouds last month. Behind the Watson branding are career technologists making the tool work for business customers.

While acquiring visual knowledge of objects and relations from static images has advanced hugely in recent years [7], How does machine learn to reason about the content of a deep video understanding remains elusive. Compared to video in answering a question? A Video QA system must simultaneously static images, video poses new challenges, primarily due understand language, represent visual content to the inherent dynamic nature of visual content over time over space-time, and iteratively transform these representations [6, 34]. At the lowest level, we have correlated motion in response to lingual content in the query, and finally and appearance [6]. At a higher level, we have objects that arriving at a sensible answer. While recent advances in are persistent over time, actions that are local in time, and textual and visual question answering have come up with the relations that can span over an extended length. Thus sophisticated visual representation and neural reasoning searching for an answer from a video facilitates solving mechanisms, major challenges in Video QA remain on dynamic simultaneous sub-tasks in both the visual and lingual spaces, grounding of concepts, relations and actions to support probably in an iterative and compositional fashion.

The increasing rate of information pollution on the Web requires novel solutions to tackle that. Question Answering (QA) interfaces are simplified and user-friendly interfaces to access information on the Web. However, similar to other AI applications, they are black boxes which do not manifest the details of the learning or reasoning steps for augmenting an answer. The Explainable Question Answering (XQA) system can alleviate the pain of information pollution where it provides transparency to the underlying computational model and exposes an interface enabling the end-user to access and validate provenance, validity, context, circulation, interpretation, and feedbacks of information. This position paper sheds light on the core concepts, expectations, and challenges in favor of the following questions (i) What is an XQA system?, (ii) Why do we need XQA?, (iii) When do we need XQA? (iv) How to represent the explanations? (iv) How to evaluate XQA systems?

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weak Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the whole passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

We introduce a modular system that can be deployed on any Kubernetes cluster for question answering via REST API. This system, called Katecheo, includes four configurable modules that collectively enable identification of questions, classification of those questions into topics, a search of knowledge base articles, and reading comprehension. We demonstrate the system using publicly available, pre-trained models and knowledge base articles extracted from Stack Exchange sites. However, users can extend the system to any number of topics, or domains, without the need to modify any of the model serving code. All components of the system are open source and available under a permissive Apache 2 License.