528 research outputs found
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of
grammatical errors, such as missing prepositions and mismatched subject-verb
agreement, but also orthographic and semantic errors, such as misspellings and
word choice errors respectively. The field has seen significant progress in the
last decade, motivated in part by a series of five shared tasks, which drove
the development of rule-based methods, statistical classifiers, statistical
machine translation, and finally neural machine translation systems which
represent the current dominant state of the art. In this survey paper, we
condense the field into a single article and first outline some of the
linguistic challenges of the task, introduce the most popular datasets that are
available to researchers (for both English and other languages), and summarise
the various methods and techniques that have been developed with a particular
focus on artificial error generation. We next describe the many different
approaches to evaluation as well as concerns surrounding metric reliability,
especially in relation to subjective human judgements, before concluding with
an overview of recent progress and suggestions for future work and remaining
challenges. We hope that this survey will serve as comprehensive resource for
researchers who are new to the field or who want to be kept apprised of recent
developments
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Automatic annotation of error types for grammatical error correction
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting
grammatical errors in text. Although previous work has focused on developing systems that
target specific error types, the current state of the art uses machine translation to correct all error
types simultaneously. A significant disadvantage of this approach is that machine translation
does not produce annotated output and so error type information is lost. This means we can only
evaluate a system in terms of overall performance and cannot carry out a more detailed analysis
of different aspects of system performance.
In this thesis, I develop a system to automatically annotate parallel original and corrected
sentence pairs with explicit edits and error types. In particular, I first extend the Damerau-
Levenshtein alignment algorithm to make use of linguistic information when aligning parallel
sentences, and supplement this alignment with a set of merging rules to handle multi-token
edits. The output from this algorithm surpasses other edit extraction approaches in terms of
approximating human edit annotations and is the current state of the art. Having extracted the
edits, I next classify them according to a new rule-based error type framework that depends only
on automatically obtained linguistic properties of the data, such as part-of-speech tags. This
framework was inspired by existing frameworks, and human judges rated the appropriateness
of the predicted error types as ‘Good’ (85%) or ‘Acceptable’ (10%) in a random sample of 200
edits. The whole system is called the ERRor ANnotation Toolkit (ERRANT) and is the first
toolkit capable of automatically annotating parallel sentences with error types.
I demonstrate the value of ERRANT by applying it to the system output produced by the participants of the CoNLL-2014 shared task, and carry out a detailed error type analysis of
system performance for the first time. I also develop a simple language model based approach
to GEC, that does not require annotated training data, and show how it can be improved using
ERRANT error types
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The Challenge of Spoken Language Systems: Research Directions for the Nineties
A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area
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The Challenge of Spoken Language Systems: Research Directions for the Nineties
A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area
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The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand.
At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1
EPSRC Tier-2 capital grant EP/P020259/
Advanced Magnetic Resonance Imaging and Quantitative Analysis Approaches in Patients with Refractory Focal Epilepsy
Background Epilepsy has a high prevalence of 1%, which makes it the most common serious neurological disorder. The most difficult to treat type of epilepsy is temporal lobe epilepsy (TLE) with its most commonly associated lesion being hippocampal sclerosis (HS). About 30-50% of all patients undergoing resective surgery of epileptogenic tissue continue to have seizures postoperatively. Indication for this type of surgery is only given when lesions are clearly visible on magnetic resonance images (MRI). About 30% of all patients with focal epilepsy do not show an underlying structural lesion upon qualitative neuroradiological MRI assessment (MRI-negative). Objectives The work presented in this thesis uses MRI data to quantitatively investigate structural differences between brains of patients with focal epilepsy and healthy controls using automated imaging preprocessing and analysis methods. Methods All patients studied in this thesis had electrophysiological evidence of focal epilepsy, and underwent routine clinical MRI prior to participation in this study. There were two datasets and both included a cohort of age-matched controls: (i) Patients with TLE and associated HS who later underwent selective amygdalahippocampectomy (cohort 1) and (ii) MRI-negative patients with medically refractory focal epilepsy (cohort 2). The participants received high- resolution routine clinical MRI as well as additional sequences for gray and white matter (GM/WM) structural imaging. A neuroradiologist reviewed all images prior to analysis. Hippocampal subfield volume and automated tractography analysis was performed in patients with TLE and HS and related to post-surgical outcomes, while images of MRI- negative patients were analyzed using voxel-based morphometry (VBM) and manual/automated tractography. All studies were designed to detect quantitative differences between patients and controls, except for the hippocampal subfield analysis as control data was not available and comparisons were limited to patients with persistent postoperative seizures and those without. Results 1. Automated hippocampal subfield analysis (cohort 1): The high-resolution hippocampal subfield segmentation technique cannot establish a link between hippocampal subfield volume loss and post-surgical outcome. Ipsilateral and contralateral hippocampal subfield volumes did not correlate with clinical variables such as duration of epilepsy and age of onset of epilepsy. 2. Automated WM diffusivity analysis (cohort 1): Along-the-tract analysis showed that ipsilateral tracts of patients with right/left TLE and HS were more extensively affected than contralateral tracts and the affected regions within tracts could be specified. The extent of hippocampal atrophy (HA) was not related to (i) the diffusion alterations of temporal lobe tracts or (ii) clinical characteristics of patients, whereas diffusion alterations of ipsilateral temporal lobe tracts were significantly related to age at onset of epilepsy, duration of epilepsy and epilepsy burden.Patients without any postoperative seizure symptoms (excellent outcomes) had more ipsilaterally distributed WM tract diffusion alterations than patients with persistent postoperative seizures (poorer outcomes), who were affected bilaterally. 3. Automated epileptogenic lesion detection (cohort 2): Comparison of individual patients against the controls revealed that focal cortical dysplasia (FCD) can be detected automatically using statistical thresholds. All sites of dysplasia reported at the start of the study were detected using this technique. Two additional sites in two different patients, which had previously escaped neuroradiological assessment, could be identified. When taking these statistical results into account during re-assessment of the dedicated epilepsy research MRI, the expert neuroradiologist was able to confirm these as lesions. 4. Manual and automated WM diffusion tensor imaging (DTI) analysis (cohort 2): The analysis of consistency across approaches revealed a moderate to good agreement between extracted tract shape, morphology and space and a strong correlation between diffusion values extracted with both methods. While whole-tract DTI-metrics determined using Automated Fiber Quantification (AFQ) revealed correlations with clinical variables such as age of onset and duration of epilepsy, these correlations were not found using the manual technique. The manual approach revealed more differences than AFQ in group comparisons of whole-tract DTI-metrics. Along-the-tract analysis provided within AFQ gave a more detailed description of localized diffusivity changes along tracts, which correlated with clinical variables such as age of onset and epilepsy duration. Conclusions While hippocampal subfield volume loss in patients with TLE and HS was not related with any clinical variables or to post-surgical outcomes, WM tract diffusion alterations were more bilaterally distributed in patients with persistent postoperative seizures, compared to patients with excellent outcomes. This may indicate that HS as an initial precipitating injury is not affected by clinical features of the disorder and automated hippocampal subfield mapping based on MRI is not sufficient to stratify patients according to outcome. Presence of persisting seizures may depend on other pathological processes such as seizure propagation through WM tracts and WM integrity. Automated and time-efficient three-dimensional voxel-based analysis may complement conventional visual assessments in patients with MRI-negative focal epilepsy and help to identify FCDs escaping routine neuroradiological assessment. Furthermore, automated along-the-tract analysis may identify widespread abnormal diffusivity and correlations between WM integrity loss and clinical variables in patients with MRI-negative epilepsy. However, automated WM tract analysis may differ from results obtained with manual methods and therefore caution should be exercised when using automated techniques
Legal translation trainees’ performance in from-scratch translation and post-editing: A product analysis
This study explores the practice of adopting MT tools in the area of legal translation didactics to assess and compare the translation quality of from-scratch vs post-edited translations through an error-based revision. Error analysis highlights both common and unique patterns in the frequency, type and severity of translation errors to possibly determine if and to what extent errors are influenced by the presence of a pre-translated text and which procedure led to higher-quality translations. The study also points out the areas of strength of Machine Translation applied to legal translation didactics alongside its limitations as inferable from the final product.This study explores the practice of adopting MT tools in the area of legal translation didactics to assess and compare the translation quality of from-scratch vs post-edited translations through an error-based revision. Error analysis highlights both common and unique patterns in the frequency, type and severity of translation errors to possibly determine if and to what extent errors are influenced by the presence of a pre-translated text and which procedure led to higher-quality translations. The study also points out the areas of strength of Machine Translation applied to legal translation didactics alongside its limitations as inferable from the final product
Modern Developments in Transcranial Magnetic Stimulation (TMS) – Applications and Perspectives in Clinical Neuroscience
Transcranial magnetic stimulation (TMS) is being increasingly used in neuroscience and clinics. Modern advances include but are not limited to the combination of TMS with precise neuronavigation as well as the integration of TMS into a multimodal environment, e.g., by guiding the TMS application using complementary techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), or magnetoencephalography (MEG). Furthermore, the impact of stimulation can be identified and characterized by such multimodal approaches, helping to shed light on the basic neurophysiology and TMS effects in the human brain. Against this background, the aim of this Special Issue was to explore advancements in the field of TMS considering both investigations in healthy subjects as well as patients
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