Advances in Natural Language Question Answering: A Review

Question Answering has recently received high attention from artificial intelligence communities due to the advancements in learning technologies. Early question answering models used rule-based approaches and moved to the statistical approach to address the vastly available information. However, statistical approaches are shown to underperform in handling the dynamic nature and the variation of language. Therefore, learning models have shown the capability of handling the dynamic nature and variations in language. Many deep learning methods have been introduced to question answering. Most of the deep learning approaches have shown to achieve higher results compared to machine learning and statistical methods. The dynamic nature of language has profited from the nonlinear learning in deep learning. This has created prominent success and a spike in work on question answering. This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges

Speech Processes for Brain-Computer Interfaces

Speech interfaces have become widely used and are integrated in many applications and devices. However, speech interfaces require the user to produce intelligible speech, which might be hindered by loud environments, concern to bother bystanders or the general in- ability to produce speech due to disabilities. Decoding a usera s imagined speech instead of actual speech would solve this problem. Such a Brain-Computer Interface (BCI) based on imagined speech would enable fast and natural communication without the need to actually speak out loud. These interfaces could provide a voice to otherwise mute people. This dissertation investigates BCIs based on speech processes using functional Near In- frared Spectroscopy (fNIRS) and Electrocorticography (ECoG), two brain activity imaging modalities on opposing ends of an invasiveness scale. Brain activity data have low signal- to-noise ratio and complex spatio-temporal and spectral coherence. To analyze these data, techniques from the areas of machine learning, neuroscience and Automatic Speech Recog- nition are combined in this dissertation to facilitate robust classification of detailed speech processes while simultaneously illustrating the underlying neural processes. fNIRS is an imaging modality based on cerebral blood flow. It only requires affordable hardware and can be set up within minutes in a day-to-day environment. Therefore, it is ideally suited for convenient user interfaces. However, the hemodynamic processes measured by fNIRS are slow in nature and the technology therefore offers poor temporal resolution. We investigate speech in fNIRS and demonstrate classification of speech processes for BCIs based on fNIRS. ECoG provides ideal signal properties by invasively measuring electrical potentials artifact- free directly on the brain surface. High spatial resolution and temporal resolution down to millisecond sampling provide localized information with accurate enough timing to capture the fast process underlying speech production. This dissertation presents the Brain-to- Text system, which harnesses automatic speech recognition technology to decode a textual representation of continuous speech from ECoG. This could allow to compose messages or to issue commands through a BCI. While the decoding of a textual representation is unparalleled for device control and typing, direct communication is even more natural if the full expressive power of speech - including emphasis and prosody - could be provided. For this purpose, a second system is presented, which directly synthesizes neural signals into audible speech, which could enable conversation with friends and family through a BCI. Up to now, both systems, the Brain-to-Text and synthesis system are operating on audibly produced speech. To bridge the gap to the final frontier of neural prostheses based on imagined speech processes, we investigate the differences between audibly produced and imagined speech and present first results towards BCI from imagined speech processes. This dissertation demonstrates the usage of speech processes as a paradigm for BCI for the first time. Speech processes offer a fast and natural interaction paradigm which will help patients and healthy users alike to communicate with computers and with friends and family efficiently through BCIs

The effect of high variability and individual differences on phonetic training of Mandarin tones

High variability phonetic training (HVPT) has been found to be more effective than low variability phonetic training (LVPT) in learning various non-native phonetic contrasts. However, little research has considered whether this applies to the learning of tone contrasts. Two relevant studies suggested that the effect of high variability training depends on the perceptual aptitude of participants (Perrachione, Lee, Ha, & Wong, 2011; Sadakata & McQueen, 2014). It is also unclear how different types of individual difference measures interact with the learning of tonal language. What work there is, suggests that musical ability is related to discriminating tonal information and in general attention and working memory are linked to language learning. The present study extends these findings by examining the interaction between individual aptitude and input variability and between learning outcomes and individual measures using natural, meaningful L2 input (both previous studies used pseudowords). In Study 1, forty English speakers took part in an eight-session phonetic training paradigm. They were assigned to high/low variability training groups. High variability used four speakers during the training sessions while low variability used one. All participants learned real Mandarin tones and words. Individual aptitude was measured using an identification and a categorisation task. Learning was measured using a categorical discrimination task, an identification task and two production tasks. Overall, all groups improved in both production and perception of tones which transferred to novel voices and items, demonstrating the effectiveness of training despite the increased complexity of the training material compared with previous research. Although the low variability group exhibited better learning during training than the high variability group, there was no evidence that the different variability training conditions led to different performances in any of the tests of generalisation. Moreover, although performance on one of the aptitude tasks significantly predicted overall performance in categorical discrimination, identification and training tasks, it did not predict improvement from pre- to post- test. Critically, there was also no interaction between individual aptitude and variability-condition, contradicting with previous findings. One possibility was that the high variability condition was too difficult as speakers were randomly presented during training, resulting in low trial-by-trial consistency. This greater difficulty might block any advantage of variability for generalisation. In order to examine this, Study 2 recruited additional 20 native English speakers and tested them in a further condition, identical to the previous high variability condition except that each speaker was presented in their own block during the training. Although participants performed better in training compared with the high variability group from study 1, there was again no difference in generalisation compared with the previous conditions, and again no interaction between individual aptitude and variability-condition was found. Bayes Factors were also used to assess the null results. There was evidence for the null for the benefits of high variability for generalisation but only ambiguous evidence regarding whether there was interaction between variability and individual aptitude. The HPVT used in Study 1 and Study 2 did not replicate the interaction between variability-condition and aptitude found in previous studies. Moreover, although one of the measures of aptitude did correlate with the baseline measures of performance, there was no evidence that it predicted learning due to training. Additionally, the two individual aptitude measures used in Study 1 and 2 – taken from Perrachione, et al. (2011) and Sadakata and McQueen (2013) – are not comprehensive. They are natural language-related tasks which directly measure tone perception itself, rather than the underlying cognitive factors which could underpin this ability. Another interesting question is whether these different cognitive factors might contribute to learners at different stages differently, particularly since language training studies vary as to whether they use current learners of the language or naïve participants, a factor may contribute towards differing findings in the literature. To explore these issues, Study 3 investigated the relationship between a battery of cognitive individual difference measures and Mandarin tone learning. Sixty native English speakers (forty of whom were currently studying Mandarin at undergraduate level, twenty of whom were naïve learners) took part in a six-session training paradigm. With high-variability training stimuli similar to that used in Study 2 (four speakers blocked), their learning outcomes were assessed by identification, categorical discrimination and production tasks similar to Study 1. Their working memory, attention and musical ability were also measured. Overall, both groups showed improvements during training and in the generalisation tasks. Although Mandarin learner participants performed better than naïve participants overall, the improvements were not generally greater than naïve participants. Each of the individual difference measures was used to predict participant’s performance at pre-test and their improvement due to training. Bayes Factors were used as the key method of inference. For Mandarin learner participants, both performances at pre-test and pre- to- post improvement were strongly predicted by attention measures while for naïve speakers, musical ability was the dominant predictor for pre- to- post improvement. This series of studies demonstrates that Mandarin lexical tones can be trained using natural stimuli embedded in a word learning task and learning generalises to untrained voices and items as well as to production. Although there is no evidence in the current data that the type of training materials affected learning outcomes, tone learning is indeed affected by individual cognitive factors, such as attention and musical ability, with these playing a different role for learners at different stages

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Itzulpen automatiko gainbegiratu gabea

192 p.Modern machine translation relies on strong supervision in the form of parallel corpora. Such arequirement greatly departs from the way in which humans acquire language, and poses a major practicalproblem for low-resource language pairs. In this thesis, we develop a new paradigm that removes thedependency on parallel data altogether, relying on nothing but monolingual corpora to train unsupervisedmachine translation systems. For that purpose, our approach first aligns separately trained wordrepresentations in different languages based on their structural similarity, and uses them to initializeeither a neural or a statistical machine translation system, which is further trained through iterative backtranslation.While previous attempts at learning machine translation systems from monolingual corporahad strong limitations, our work¿along with other contemporaneous developments¿is the first to reportpositive results in standard, large-scale settings, establishing the foundations of unsupervised machinetranslation and opening exciting opportunities for future research