Let Me Know What to Ask: Interrogative-Word-Aware Question Generation

Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.Comment: Accepted at 2nd Workshop on Machine Reading for Question Answering (MRQA), EMNLP 201

The Blended Learning Unit, University of Hertfordshire: A Centre for Excellence in Teaching and Learning, Evaluation Report for HEFCE

The University of Hertfordshire’s Blended Learning Unit (BLU) was one of the 74 Centres for Excellence in Teaching and Learning (CETLs) funded by the Higher Education Funding Council for England (HEFCE) between 2005 and 2010. This evaluation report follows HEFCE’s template. The first section provides statistical information about the BLU’s activity. The second section is an evaluative reflection responding to 13 questions. As well as articulating some of our achievements and the challenges we have faced, it also sets out how the BLU’s activity will continue and make a significant contribution to delivery of the University of Hertfordshire’s 2010-2015 strategic plan and its aspirations for a more sustainable future. At the University of Hertfordshire, we view Blended Learning as the use of Information and Communication Technology (ICT) to enhance the learning and learning experience of campus-based students. The University has an excellent learning technology infrastructure that includes its VLE, StudyNet. StudyNet gives students access to a range of tools, resources and support 24/7 from anywhere in the world and its robustness, flexibility and ease of use have been fundamental to the success of the Blended Learning agenda at Hertfordshire. The BLU has comprised a management team, expert teachers seconded from around the University, professional support and a Student Consultant. The secondment staffing model was essential to the success of the BLU. As well as enabling the BLU to become fully staffed within the first five months of the CETL initiative, it has facilitated access to an invaluable spectrum of Blended Learning, research and Change Management expertise to inform pedagogically sound developments and enable change to be embedded across the institution. The BLU used much of its capital funding to reduce barriers to the use of technology by, for example, providing laptop computers for all academic staff in the institution, enhancing classroom technology provision and wirelessly enabling all teaching accommodation. Its recurrent funding has supported development opportunities for its own staff and staff around the institution; supported evaluation activities relating to individual projects and of the BLU’s own impact; and supported a wide range of communication and dissemination activities internally and externally. The BLU has led the embedding a cultural change in relation to Blended Learning at the University of Hertfordshire and its impact will be sustained. The BLU has produced a rich legacy of resources for our own staff and for others in the sector. The University’s increased capacity in Blended Learning benefits all our students and provides a learning experience that is expected by the new generation of learners in the 21st century. The BLU’s staffing model and partnership ways of working have directly informed the structure and modus operandi of the University’s Learning and Teaching Institute (LTI). Indeed a BLU team will continue to operate within the LTI and help drive and support the implementation of the University’s 2010-2015 Strategic plan. The plan includes ambitions in relation to Distance Learning and Flexible learning and BLU will be working to enable greater engagement with students with less or no need to travel to the university. As well as opening new markets within the UK and overseas, even greater flexibility for students will also enable the University to reduce its carbon footprint and provide a multifaceted contribution to our sustainability agenda. We conclude this executive summary with a short paragraph, written by Eeva Leinonen, our former Deputy Vice-Chancellor, which reflects our aspiration to transform Learning and Teaching at the University of Hertfordshire and more widely in the sector. ‘As Deputy Vice Chancellor at Hertfordshire I had the privilege to experience closely the excellent work of the Blended Learning Unit, and was very proud of the enormous impact the CETL had not only across the University but also nationally and internationally. However, perhaps true impact is hard to judge at such close range, but now as Vice Principal (Education) at King's College London, I can unequivocally say that Hertfordshire is indeed considered as the leading Blended Learning university in the sector. My new colleagues at King's and other Russell Group Universities frequently seek my views on the 'Hertfordshire Blended Learning' experience and are keen to emulate the successes achieved at an institutional wide scale. The Hertfordshire CETL undoubtedly achieved not only what it set out to achieve, but much more in terms of scale and impact. All those involved in this success can be justifiably proud of their achievements.’ Professor Eeva Leinonen, Vice Principal (Education), King's College, Londo

A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

Preface to Computational Humor 2012

Like its predecessors in 1996 (University of Twente, the Netherlands) and 2002 (ITC-irst, Trento, Italy), this Third International Workshop on Computational Humor (IWCH 2012) focusses on the possibility to find algorithms that allow understanding and generation of humor. There is the general aim of modeling humor, and if we can do that, it will provide us with lots of information about our cognitive abilities in general, such as reasoning, remembering, understanding situations, and understanding conversational partners. But it also provides us with information about being creative, making associations, storytelling and language use. Many more subtleties in face-to-face and multiparty interaction can be added, such as using humor to persuade and dominate, to soften or avoid a face threatening act, to ease a tense situation or to establish a friendly or romantic relationship. One issue to consider is: when is a humorous act appropriate

Computational Batik Motif Generation: Innovation of Traditional Heritage by Fractal Computation\ud

Human-computer interaction has been the cause of the emerging innovations in many fields, including in design and art, architectural, technological artifacts, and even traditional heritage. In the case of Indonesian traditional heritages, the computation of fractal designs has been introduced to develop batik design – the genuine textile art and skill that becomes a symbol of Indonesian culture. The uniqueness of Batik, which depicted in the richness of its motifs, is regarded as one of interesting aspect to be researched and innovated using computational techniques. Recent studies of batik motifs have discovered conjecture to the existence of fractal geometry in batik designs. This finding has given some inspiration of implementing certain fractal concepts, such escape-time fractal (complex plane) and iterated function system to generate batik motifs. We develop motif generator based upon the Collage Theorem by using Java TM platform. This software is equipped by interface that can be used by user to generate basic patterns, which could be interpreted and painted as batik motif. Experimentally, we found that computationally generated fractal motifs are appropriated to be implemented as batik motif. However, human made batik motifs are less detail and some of them differ significantly with the computationally generated ones for tools used to draw batik and human aesthetic constraints

An audio-visual approach to teaching the social aspects of sustainable product design

This paper considers the impact of audio-visual resources in enabling students to develop an understanding of the social aspects of sustainable product design. Building on literature concerning the learning preferences of ‘Net Generation’ learners, three audio-visual workshops were developed to introduce students to the wider social aspects of sustainability and encourage students to reflect upon the impact of their practice. The workshops were delivered in five universities in Britain and Ireland among undergraduate and postgraduate students. They were designed to encourage students to reflect upon carefully designed audio-visual materia ls in a group-based environment, seeking to foster the preferences of Net Generation learners through collaborative learning and learning through discovery. It also sought to address the perceived weaknesses of this generation of learners by encouraging critical reflection. The workshops proved to be popular with students and were successful in enabling them to grasp the complexity of the social aspects of sustainable design in a short span of time, as well as in encouraging personal responses and creative problem solving through an exploration of design thinking solutions

A new metric for patent retrieval evaluation

Patent retrieval is generally considered to be a recall-oriented information retrieval task that is growing in importance. Despite this fact, precision based scores such as mean average precision (MAP) remain the primary evaluation measures for patent retrieval. Our study examines different evaluation measures for the recall-oriented patent retrieval task and shows the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and user search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the expected search effort of patent searchers