Negative Statements Considered Useful

Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Improving Asynchronous Interview Interaction with Follow-up Question Generation

The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available

Negative Statements Considered Useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statisticalinference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Interactivity, computers and orthodontic training for undergraduates

This phenomenological study investigates the interactivity taking place when students use computer-assisted-learning (CAL) in orthodontics and what can be inferred about why these interactions occur. CAL has been proposed in orthodontics because it provides an opportunity to follow a case through to completion. This training is needed if only to give all dentists sufficient knowledge to identify and refer cases for treatment. Two programs have been developed for the pilot: an introductory e-book and a narrative case study based on real records that takes students through a series of decisions relating to case assessment, treatment planning and appliance design.The mixed-methodology approach of the main study uses activity theory to provide a. framework combining qualitative and quantitative data to analyse the interactivity of 48 students as they work through the case study. Observations and transcripts of recordings of conversations between pairs of students, together with post-session interviews, facilitate a deeper understanding of students' conceptions of orthodontics particularly when they explain their reasoning in negotiations over answers, clarified where necessary by data recorded by computer activity log-files. The linear sequence of questions in the program allows students'interactions to be compared on a "like-for-like" basis.Activity systems are used to identify various tensions in students' responses whilst using CAL, facilitating a deeper understanding of the observed interactivity. A phenomenological profile of the students has been developed based on these interactions, particularly in response to the unexpected caused by the complex reality of the case. Further supporting quantitative data is obtained from a questionnaire survey and end-of-year examination results used to provide contextual background material particularly when presenting the results to a domain heavily dominated by a scientific epistemology.Throughout the program many students seem to ignore features not in their immediate focus. Students' reactions to the unexpected (extraction of 7s) indicates about half of the students are so reliant on simplified taught procedure they are unable to relate the extractions to these "hidden" features. Other students adopt a deeper approach and are able to identify reasons why the unexpected occurs. The program has been found to promote an active approach to learning in most students, whether their approach is surface or deep. Most students learn from the feedback provided by the program, even when this feedback is not explicit on a point. Students also benefit from working with a partner. The deeper understanding of students' misconceptions afforded by the adopted research methodology enables the development of guidelines for the future design of CAL in dentistry

Assessing the collaborative knowledge management of the market dominant organization

Dominant firms enjoy economic strengths which enable them to compete effectively in relevant markets through the use of collaborative knowledge management (CKM). While the literature is replete with general guiding principles for companies to adopt successful business strategies, there is very limited empirical research on effectively using CKM to improve company performance and market domination. The purpose of this study was to evaluate strategies for information sharing by companies to achieve better operations management and control, a wider range of customers, and stronger competitive edge in the global economy. Epistemological foundation for the study was provided by the literature on knowledge management and organizational dynamics. Data were collected by an electronically self-administered questionnaire on a convenience sample of 80 employees of three small businesses in Memphis, Tennessee. A quantitative method using Poisson regression was applied to test the hypotheses about relationships between six independent variables of value proposition, culture building, responsibilities, information technology, approaches and assessment and the dependent variable, collaborative knowledge management. Results indicate that value proposition, information technology, and building an organizational culture of responsibilities and best practices play significant roles in effective CKM. Social change implications of the study suggest that high-intensity collaborative knowledge management would produce creative leaders and workers, improved leader-worker collaboration, and more effective use of information technologies in organizational intelligence and decision making

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option