Investigating the effect of auxiliary objectives for the automated grading of learner english speech transcriptions

We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.Cambridge Assessmen

Skills embeddings: A neural approach to multicomponent representations of students and tasks

Educational systems use models of student skill to inform decision-making processes. Defining such a model manually is challenging due to the large number of relevant factors. We introduce an alternative approach by learning multidimensional representations (embeddings) from student activity data. Such embeddings are fixed-length real vectors with three desirable characteristics: co-location of similar students and items in a vector space; magnitude increases with skill, and that absence of a skill can be represented. Based on the Multicomponent Latent Trait Model, we use a neural network with complementary trainable weights to learn these embeddings by backpropagation in an unsupervised manner. We evaluate using synthetic student activity data that provides a ground-truth of student skills in order to understand the impact of number of students, question items and knowledge components in the domain. We find that our data-mined parameter values can recreate the synthetic datasets up to the accuracy of the model that generated them, for domains containing up to 10 simultaneously active knowledge components, which can be effectively mined using relatively small quantities of data (1000 students, 100 items). We describe a procedure to estimate the number of components in a domain, and propose a component-masking logic mechanism that improves performance on high-dimensional datasets.Cambridge Assessmen

Accurate modelling of language learning tasks and students using representations of grammatical proficiency

Adaptive learning systems aim to learn the relationship between curriculum content and students in order to optimise a student’s learning process. One form of such a system is content recommendation in which the system attempts to predict the most suitable content to next present to the student. In order to develop such a system, we must learn reliable representations of the curriculum content and the student. We consider this in the context of foreign language learning and present a novel neural network architecture to learn such representations. We also show that by incorporating grammatical error distributions as a feature in our neural architecture, we can substantially improve the quality of our representations. Different types of grammatical error are automatically detected in essays submitted by students to an online learning platform. We evaluate our model and representations by predicting student scores and grammatical error distributions on unseen language tasks. We also discuss further uses for our model beyond content recommendation such as inferring student knowledge components for a given domain and optimising spacing and repetition of content for efficient long term retention.Cambridge Assessmen

CAMsterdam at SemEval-2019 task 6: Neural and graph-based feature extraction for the identification of offensive tweets

We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offen-sive language identification in Twitter data.Our proposed model learns to extract tex-tual features using a multi-layer recurrent net-work, and then performs text classification us-ing gradient-boosted decision trees (GBDT). A self-attention architecture enables the model to focus on the most relevant areas in the text.We additionally learn globally optimised em-beddings for hashtags using node2vec, which are given as additional tweet features to the GBDT classifier.Our best model obtains78.79% macro F1-score on detecting offensive language (subtask A), 66.32% on categorising offence types (targeted/untargeted; subtask B),and 55.36% on identifying the target of of-fence (subtask C)

Evidence for chemical equilibration at RHIC

This contribution focuses on the results of statistical model calculations at RHIC energies, including recently available experimental data. Previous calculations of particle yield ratios showed good agreement with measurements at SPS and lower energies, suggesting that the composite system possesses a high degree of chemical equilibrium at freeze-out. The effect of feeddown contamination on the model parameters is discussed, and the sensitivity of individual ratios to the model parameters ($T$, $\mu_B$) is illustrated.Comment: Talk presented at Strange Quarks in Matter 2001, Frankfurt, September 24-29, 2001. Proceedings to be published by J. Phys. G. 8 pages with 4 figure

Strangeness Production at RHIC in the Perturbative Regim

We investigate strange quark production in Au-Au collisions at RHIC in the framework of the Parton Cascade Model(PCM). The yields of (anti-) strange quarks for three production scenarios -- primary-primary scattering, full scattering, and full production -- are compared to a proton-proton baseline. Enhancement of strange quark yields in central Au-Au collisions compared to scaled p-p collisions increases with the number of secondary interactions. The centrality dependence of strangeness production for the three production scenarios is studied as well. For all production mechanisms, the strangeness yield increases with $(N_{\rm part})^{4/3}$. The perturbative QCD regime described by the PCM is able to account for up to 50% of the observed strangeness at RHIC.Comment: 10 pages, 4 figures, IOP forma

Nonextensive statistical effects in the hadron to quark-gluon phase transition

We investigate the relativistic equation of state of hadronic matter and quark-gluon plasma at finite temperature and baryon density in the framework of the nonextensive statistical mechanics, characterized by power-law quantum distributions. We study the phase transition from hadronic matter to quark-gluon plasma by requiring the Gibbs conditions on the global conservation of baryon number and electric charge fraction. We show that nonextensive statistical effects play a crucial role in the equation of state and in the formation of mixed phase also for small deviations from the standard Boltzmann-Gibbs statistics.Comment: 13 pages, 10 figure

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor