Task Demands Modulate the Effects of Speech on Text Processing

Task-irrelevant background sound can disrupt performance of visually-based cognitive tasks. The cross-modal breakdown of attentional selectivity in the context of reading was addressed using analyses of eye-movements. Moreover, the study addressed whether task-sensitivity to distraction via background speech on reading was modulated by the cognitive demands of the focal task. Two randomly-assigned groups of native-Chinese participants read the same set of Chinese experimental sentences while being exposed to meaningful speech, meaningless (foreign) speech, or silence. For one group, participants were instructed to judge whether the sentences made sense (i.e., semantic acceptability task); for another, participants were instructed to detect whether the sentences contained a non-character (i.e., non-character detection task). Results showed no significant effect across sound conditions for the non-character detection task. For the semantic acceptability task, however, there was a substantial disruptive effect of the meaningfulness of the speech. Compared with reading with meaningless speech or reading in silence, the meaningful speech increased numbers of fixations, regressions, regression path and total reading times. These results suggest that the disruption of reading by background speech is jointly dependent on the nature of the speech and the task-process deployed, thereby favouring an Interference-by-Process account over Interference-by-Content and Attentional Diversion accounts of distraction to reading by background sound

An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection

Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adversarial learning tasks. Moreover, the Monte Carlo tree search strategy and a policy network are introduced to accomplish the efficient Chinese text correction task with semantic detection. The experiments are executed on three datasets and five comparable methods, and the experimental results show that our method can obtain good performance in Chinese text correction task for better semantic rationality.Comment: Published on 31st International Conference on Artificial Neural Networ

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Statistical Parsing by Machine Learning from a Classical Arabic Treebank

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year

NBS monograph

From Introduction Purpose and Scope of Report: "This report is one of a series prepared for the Research Information Center and Advisory Service on Information Processing (RICASIP), and intended as a contribution to improved cooperation in the fields of information selection systems development, information retrieval research and mechanized translation.

Working Styles of Student Translators in Revision and Post-editing: an Empirical-Experimental Study with Eye-tracking, Keylogging and Cue-based Retrospection

In today’s translation profession, being skilful at revision (including self-revision and other-revision) and post-editing tasks is becoming essential for translators. The exploration of the working styles of student translators in the revision and post-editing processes is vital in helping us to understand the nature of these tasks, and may help in improving pedagogy. Drawing on theories from translation-related studies, cognitive psychology, and text comprehension and production, the aims of this research were to: (1) identify the basic types of reading and typing activity (physical activities) of student translators in the processes of revision and post-editing, and to measure statistically and compare the duration of these activities within and across tasks; (2) identify the underlying purposes (mental activities) behind each type of reading and typing activity; (3) categorise the basic types of working style of student translators and compare the frequency of use of each working style both within and across tasks; (4) identify the personal working styles of student translators in carrying out different tasks, and (5) identify the most efficient working style in each task. Eighteen student translators from Durham University, with Chinese as L1 and English as L2, were invited to participate in the experiment. They were asked to translate, self-revise, other-revise and post-edit three comparable texts in Translog-II with the eye-tracking plugin activated. A cue-based retrospective interview was carried out after each session to collect the student translators’ subjective and conscious data for qualitative analysis. The raw logging data were transformed into User Activity Data and were analysed both quantitatively and qualitatively. This study identified seven types of reading and typing activity in the processes of self-revision, other-revision and post-editing. Three revision phases were defined and four types of working style were recognised. The student translators’ personal working styles were compared in all three tasks. In addition, a tentative model of their cognitive processes in self-revision, other-revision and post-editing was developed, and the efficiency of the four working styles in each task was tested

Quality in subtitling: theory and professional reality

The issue of quality is of great importance in translation studies and, although some studies have been conducted in the field of subtitling, most discussions have been limited to aspects such as how to become a good subtitler and how to produce quality subtitles. Little research has been carried out to investigate other potential factors that may influence the quality of subtitling output in practice. In recent years, some subtitling courses at postgraduate level have attempted to bridge the gap between academia and industry, not only by incorporating the teaching of linguistic and technical skills into the curriculum but also by informing students about ethics, working conditions, market competition, and other relevant professional issues. This instruction is intended to prepare them for promising careers in the subtitling industry, where a progressively deteriorating trend has been observed by some professional subtitlers. The main aim and objective of this study is to explore both theoretical and practical aspects of subtitling quality. The study aspires to call attention to the factors influencing the quality of subtitles and also to provide suggestions to improve the state of affairs within the subtitling industry in terms of quality. In order to examine the potential factors that influence the perception of subtitling quality, particularly in the professional context, two rounds of online surveys were conducted to establish the working conditions of subtitlers. Despite the fact that the participants in the first survey were based in thirty-nine different countries, the data collected is more representative of the situation in Europe, where subtitling is a relatively mature industry compared to other parts of the world. The second survey targeted subtitlers working with the Chinese language in an attempt to study the burgeoning Chinese audiovisual market. This thesis provides a systematic analysis of the numerous parameters that have an impact on the quality of subtitling, both in theory and in professional reality, and offers a detailed insight into the working environment of subtitlers. At the same time, it endeavours to draw attention to the need to ensure decent working conditions in the industry. The general findings are discussed in terms of their implications for the development of the profession as well as for subtitler training and education.Open Acces