An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of- the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model's learned saliency and entailment skills.

For many, the worst-case privacy scenario involves corporations and governments that listen to what we say in the privacy of our own homes. Unfortunately, that dystopian future may not be as distant as you thought. When is Google Voice listening to what you say and how can you stop it? The key is in your Google Voice Search history. Google Voice Search (commonly known as Google Voice, although this is technically the name of Google's unrelated telephony service) allows users to perform Google searches, set reminders or alarms, and perform other functions using only their voices. Every single request a user makes is stored on their account, and those recordings can be reviewed (and listened to) by the user at any time.

Apple recently announced their partnership with IBM to leverage IBM's Watson service to train machine learning models for CoreML. So that mean you now can build apps that leverage Watson machine learning models on iPhone and iPad, even when your device is offline. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services. With this video series you will learn to onboard with not only pre-trained Watson models but customize and train models that continuously learn over time. In Apple's own words "You can build apps that seamlessly integrate with IBM Cloud using the IBM Cloud Developer Console for Apple. This allows you to quickly tap into Watson Services for Core ML, as well as other IBM cloud services including authentication, data, analytics, and more. The console provides a catalog of starter kits designed for common frameworks that integrate with IBM Cloud."

We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.

IBM has laid off approximately 50 and 70 per cent of staff this week in its Watson Health division, according to inside sources. The axe, we're told, is largely falling on IBMers within companies the IT goliath has taken over in the past few years to augment Watson's credentials in the health industry. These include medical data biz Truven, which was acquired in 2016 for $2.6bn, medical imaging firm Merge, bought in 2015 for $1bn, and healthcare management business Phytel, also snapped up in 2015. Yesterday and today, staff were let go at IBM's offices in Dallas, Texas, as well as in Ann Arbor, Michigan, Cleveland, Ohio, and Denver, Colorado, in the US, and elsewhere, it is claimed. A spokesperson for Big Blue was not available for comment.

Guided troubleshooting is an inherent task in the domain of technical support services. When a customer experiences an issue with the functioning of a technical service or a product, an expert user helps guide the customer through a set of steps comprising a troubleshooting procedure. The objective is to identify the source of the problem through a set of diagnostic steps and observations, and arrive at a resolution. Procedures containing these set of diagnostic steps and observations in response to different problems are common artifacts in the body of technical support documentation. The ability to use machine learning and linguistics to understand and leverage these procedures for applications like intelligent chatbots or robotic process automation, is crucial. Existing research on question answering or intelligent chatbots does not look within procedures or deep-understand them. In this paper, we outline a system for mining procedures from technical support documents. We create models for solving important subproblems like extraction of procedures, identifying decision points within procedures, identifying blocks of instructions corresponding to these decision points and mapping instructions within a decision block. We also release a dataset containing our manual annotations on publicly available support documents, to promote further research on the problem.

When dealing with audio files a lot of work is required to index them properly so that is easy to lookup for them when needed. In this example we'll show how to use IBM Watson Speech to Text to recognize speech from audio files stored on Box and enrich their metadata with the extracted text. Go to Service Credentials and copy the username & password values, we're going to use them soon. In less then 1 minute you can have this up and running by using the project Blueprint, a pre-built template to help you get started with proven integration solutions. To get started with the Blueprint just click here.

I am trying to reproduced result from the paper "DYNAMIC COATTENTION NETWORKS FOR QUESTION ANSWERING" (https://arxiv.org/abs/1611.01604). I have implemented the code in pytorch but it is overfitting. In the paper it is mention that the authors use dropout for regularization. I added dropout and it helps a bit but not too much. I am also curious if it is possible to get feedback on my model code.

Recently I've been working on a customer service chatbot based on IBM Watson Assistant (formerly known as "IBM Watson Conversation Service") for a large Austrian telecommuncation provider. The chatbot was trained to answer questions on the website and to lead the user to the right website section. It currently handles 60k-80k conversations per months and covers 25% of the customer service interactions. It happened several times that minor changes in the dialog design or training caused previously working dialogs to fail -- so we were in need of regression testing. With Botium it was possible to generate test cases from the IBM Watson Assistant workspace and setup automated testing within some minutes.

Watson services on IBM Cloud are a set of REST APIs. This makes them quite simple to be used as a piece of a solution within an application. It also means they need to be integrated with various other parts of the solution to allow your users to interact with your instance of Watson. With the launch of Watson Assistant, integrating with other channels (Facebook, Slack, Intercom) has never been easier. Building a skill for Alexa is possible with Watson.