Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201

Explaining Trained Neural Networks with Semantic Web Technologies: First Steps

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept

Doing Good and Looking Good in Global Humanitarian Reporting: Is Philanthrojournalism good news?

This chapter investigates if and how a private donor’s apparent motivation to ‘look good’ – or to generate symbolic capital – interacts with a news organization’s ability to ‘do good’ by producing public service content. We address this issue by reporting on the findings of a year-long study of the online humanitarian news organisation – IRIN – as it became primarily funded by a new donor. We argue that whilst it is possible that the Foundation’s pursuit of symbolic capital may have had some effect on how IRIN sought to ‘do good’, it did not appear to affect the extent to which IRIN was either willing or able to ‘do good’. Indeed, our analysis makes clear that the influence of the Foundation only had an effect on IRIN when it combined with other factors, especially journalists’ own values and organizational strategies. Ultimately, this case highlights the limits of generalized claims about the likely influence of a donor’s desire to ‘look good’ on a news organization