We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users' preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/

The NZ Analytics Forum invites you to our event on New Frontiers in Analytics. The event is free of charge and is kindly sponsored by Westpac New Zealand. Registration: The event is free of charge, with capacity of 230 people. Caleb has worked in the Data Science team at Westpac New Zealand for 18 months. Prior to this he was a Data Journalist at NZ Herald.

This post was featured in our Cognilytica Newsletter, with additional details. When people hear about the race for Artificial Intelligence (AI) dominance, they often think that the main competition is between the US and China. After all, the US and China have most of the largest and most well funded AI companies on the planet, and the pace of funding, company growth, and adoption doesn't seem to be slowing anytime soon. However, if you look closely, you'll see that many other countries have a stake in the AI race, and indeed, some countries have AI efforts, funding, technologies, and intellectual property that make them serious contenders in the jostling for AI dominance. In this newsletter, we'll take a look at how countries are strategically positioned with regards to their AI capabilities and ambitions, and see if AI is truly like that of the space race or simply like any other technology trend we've seen come and go.

As global firms with deep pockets invest in machine learning and launch new AI-backed products, services and processes, how should mid-sized organisations with less time and technical expertise respond? Up to now most have chosen to wait and see. But many who have invested have achieved results which show that across industries and company sizes machine learning solutions can slice cost from internal business processes and inspire new product and service possibilities. We've seen voice and image recognition come of age, mapping platforms identify traffic delays and recommend new routes in near real-time and e-commerce platforms find and cluster similar products helping customer find what they need and what they didn't know they needed. For mid-sized organisations, machine learning can lower costs or opens up new markets by offsetting the scale advantages available to larger organisations, and much more.

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader's background knowledge. One example is the task of interpreting regulations to answer "Can I...?" or "Do I have to...?" questions such as "I am working in Canada. Do I have to carry on paying UK National Insurance?" after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as "How long have you been working abroad?" when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.

There has been renewed recent interest in developing effective lower bounds for Dynamic Time Warping (DTW) distance between time series. These have many applications in time series indexing, clustering, forecasting, regression and classification. One of the key time series classification algorithms, the nearest neighbor algorithm with DTW distance (NN-DTW) is very expensive to compute, due to the quadratic complexity of DTW. Lower bound search can speed up NN-DTW substantially. An effective and tight lower bound quickly prunes off unpromising nearest neighbor candidates from the search space and minimises the number of the costly DTW computations. The speed up provided by lower bound search becomes increasingly critical as training set size increases. Different lower bounds provide different trade-offs between computation time and tightness. Most existing lower bounds interact with DTW warping window sizes. They are very tight and effective at smaller warping window sizes, but become looser as the warping window increases, thus reducing the pruning effectiveness for NN-DTW. In this work, we present a new class of lower bounds that are tighter than the popular Keogh lower bound, while requiring similar computation time. Our new lower bounds take advantage of the DTW boundary condition, monotonicity and continuity constraints to create a tighter lower bound. Of particular significance, they remain relatively tight even for large windows. A single parameter to these new lower bounds controls the speed-tightness trade-off. We demonstrate that these new lower bounds provide an exceptional balance between computation time and tightness for the NN-DTW time series classification task, resulting in greatly improved efficiency for NN-DTW lower bound search.

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Previous models of agent responsiveness, termed "strategic manipulation," have analyzed the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm, is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to better capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of potential interventions in which a learner can subsidize members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off--even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.

In the field of statistics and machine learning, the sums-of-squares, commonly referred to as \emph{ordinary least squares}, can be used as a convenient choice of cost function because of its many nice analytical properties, though not always the best choice. However, it has been long known that \emph{ordinary least squares} is not robust to outliers. Several attempts to resolve this problem led to the creation of alternative methods that, either did not fully resolved the \emph{outlier problem} or were computationally difficult. In this paper, we provide a very simple solution that can make \emph{ordinary least squares} less sensitive to outliers in data classification, by \emph{scaling the augmented input vector by its length}. We show some mathematical expositions of the \emph{outlier problem} using some approximations and geometrical techniques. We present numerical results to support the efficacy of our method.

A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.

Children who spend hours playing video games could be at risk of developing a potentially deadly medical condition called deep vein thrombosis, experts warn. Deep vein thrombosis (DVT) is a blood clot that forms in the veins of one's legs - and the risks of getting DVT are higher if you sit still or lie down for extended periods of time without moving. While DVT is more common among the elderly, new research from the Medical Research Institute of New Zealand shows that it can also be triggered in young children who live sedentary lifestyles. This is why children who play video games - whether they're sitting or lying down - for up to three hours or more could potentially develop deep vein thrombosis. In one case, a boy as young as 12 suffered from DVT after he played video games for four hours straight in a kneeling position, The Telegraph reported.