Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network

In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.Comment: the experience result is not as good as I excep

Named Entity Extraction and Disambiguation: The Reinforcement Effect.

Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u

Critters in the Classroom: A 3D Computer-Game-Like Tool for Teaching Programming to Computer Animation Students

The brewing crisis threatening computer science education is a well documented fact. To counter this and to increase enrolment and retention in computer science related degrees, it has been suggested to make programming "more fun" and to offer "multidisciplinary and cross-disciplinary programs" [Carter 2006]. The Computer Visualisation and Animation undergraduate degree at the National Centre for Computer Animation (Bournemouth University) is such a programme. Computer programming forms an integral part of the curriculum of this technical arts degree, and as educators we constantly face the challenge of having to encourage our students to engage with the subject. We intend to address this with our C-Sheep system, a reimagination of the "Karel the Robot" teaching tool [Pattis 1981], using modern 3D computer game graphics that today's students are familiar with. This provides a game-like setting for writing computer programs, using a task-specific set of instructions which allow users to take control of virtual entities acting within a micro world, effectively providing a graphical representation of the algorithms used. Whereas two decades ago, students would be intrigued by a 2D top-down representation of the micro world, the lack of the visual gimmickry found in modern computer games for representing the virtual world now makes it extremely difficult to maintain the interest of students from today's "Plug&Play generation". It is therefore especially important to aim for a 3D game-like representation which is "attractive and highly motivating to today's generation of media-conscious students" [Moskal et al. 2004]. Our system uses a modern, platform independent games engine, capable of presenting a visually rich virtual environment using a state of the art rendering engine of a type usually found in entertainment systems. Our aim is to entice students to spend more time programming, by providing them with an enjoyable experience. This paper provides a discussion of the 3D computer game technology employed in our system and presents examples of how this can be exploited to provide engaging exercises to create a rewarding learning experience for our students

Learning scale-variant and scale-invariant features for deep image classification

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scale-invariant and scale-variant representations. We propose a multi- scale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance