A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation
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Arxiv:2002.06235V1
Semantic Relatedness and Taxonomic Word Embeddings Magdalena Kacmajor John D. Kelleher Innovation Exchange ADAPT Research Centre IBM Ireland Technological University Dublin [email protected] [email protected] Filip Klubickaˇ Alfredo Maldonado ADAPT Research Centre Underwriters Laboratories Technological University Dublin [email protected] [email protected] Abstract This paper1 connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representa- tions encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of ex- periments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined. 1 Introduction Deep learning has revolutionised natural language processing over the last decade. A key enabler of deep learning for natural language processing has been the development of word embeddings. One reason for this is that deep learning intrinsically involves the use of neural network models and these models only work with numeric inputs. Consequently, applying deep learning to natural language processing first requires developing a numeric representation for words. Word embeddings provide a way of creating numeric representations that have proven to have a number of advantages over traditional numeric rep- resentations of language. -
Integrating Optical Character Recognition and Machine
Integrating Optical Character Recognition and Machine Translation of Historical Documents Haithem Afli and Andy Way ADAPT Centre School of Computing Dublin City University Dublin, Ireland haithem.afli, andy.way @adaptcentre.ie { } Abstract Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimenta- tion shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30% relative improvement. 1 Introduction While research on improving Optical Character Recognition (OCR) algorithms is ongoing, our assess- ment is that Machine Translation (MT) will continue to produce unacceptable translation errors (or non- translations) based solely on the automatic output of OCR systems. The problem comes from the fact that current OCR and Machine Translation systems are commercially distinct and separate technologies. There are often mistakes in the scanned texts as the OCR system occasionally misrecognizes letters and falsely identifies scanned text, leading to misspellings and linguistic errors in the output text (Niklas, 2010). Works involved in improving translation services purchase off-the-shelf OCR technology but have limited capability to adapt the OCR processing to improve overall machine translation perfor- mance. -
Combining Word Embeddings with Semantic Resources
AutoExtend: Combining Word Embeddings with Semantic Resources Sascha Rothe∗ LMU Munich Hinrich Schutze¨ ∗ LMU Munich We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guar- antees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks. 1. Introduction Unsupervised methods for learning word embeddings are widely used in natural lan- guage processing (NLP). The only data these methods need as input are very large corpora. However, in addition to corpora, there are many other resources that are undoubtedly useful in NLP, including lexical resources like WordNet and Wiktionary and knowledge bases like Wikipedia and Freebase. We will simply refer to these as resources. In this article, we present AutoExtend, a method for enriching these valuable resources with embeddings for non-word objects they describe; for example, Auto- Extend enriches WordNet with embeddings for synsets. The word embeddings and the new non-word embeddings live in the same vector space. Many NLP applications benefit if non-word objects described by resources—such as synsets in WordNet—are also available as embeddings. -
Machine Translation for Academic Purposes Grace Hui-Chin Lin
Proceedings of the International Conference on TESOL and Translation 2009 December 2009: pp.133-148 Machine Translation for Academic Purposes Grace Hui-chin Lin PhD Texas A&M University College Station Master of Science, University of Southern California Paul Shih Chieh Chien Center for General Education, Taipei Medical University Abstract Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a valuable approach to apply by professional translators for diverse professional fields. For learning translating skills and finding a way to learn and teach through bi-lingual/multilingual translating functions in software, machine translation is an ideal approach that translation trainers, translation learners, and professional translators should be familiar with. In fact, theories for machine translation and computer assistance had been highly valued by many scholars. (e.g., Hutchines, 2003; Thriveni, 2002) Based on MIT’s Open Courseware into Chinese that Lee, Lin and Bonk (2007) have introduced, this paper demonstrates how MT can be efficiently applied as a superior way of teaching and learning. This article predicts the translated courses utilizing MT for residents of global village should emerge and be provided soon in industrialized nations and it exhibits an overview about what the current developmental status of MT is, why the MT should be fully applied for academic purpose, such as translating a textbook or teaching and learning a course, and what types of software can be successfully applied. -
Word-Like Character N-Gram Embedding
Word-like character n-gram embedding Geewook Kim and Kazuki Fukui and Hidetoshi Shimodaira Department of Systems Science, Graduate School of Informatics, Kyoto University Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project fgeewook, [email protected], [email protected] Abstract Table 1: Top-10 2-grams in Sina Weibo and 4-grams in We propose a new word embedding method Japanese Twitter (Experiment 1). Words are indicated called word-like character n-gram embed- by boldface and space characters are marked by . ding, which learns distributed representations FNE WNE (Proposed) Chinese Japanese Chinese Japanese of words by embedding word-like character n- 1 ][ wwww 自己 フォロー grams. Our method is an extension of recently 2 。␣ !!!! 。␣ ありがと proposed segmentation-free word embedding, 3 !␣ ありがと ][ wwww 4 .. りがとう 一个 !!!! which directly embeds frequent character n- 5 ]␣ ございま 微博 めっちゃ grams from a raw corpus. However, its n-gram 6 。。 うござい 什么 んだけど vocabulary tends to contain too many non- 7 ,我 とうござ 可以 うござい 8 !! ざいます 没有 line word n-grams. We solved this problem by in- 9 ␣我 がとうご 吗? 2018 troducing an idea of expected word frequency. 10 了, ください 哈哈 じゃない Compared to the previously proposed meth- ods, our method can embed more words, along tion tools are used to determine word boundaries with the words that are not included in a given in the raw corpus. However, these segmenters re- basic word dictionary. Since our method does quire rich dictionaries for accurate segmentation, not rely on word segmentation with rich word which are expensive to prepare and not always dictionaries, it is especially effective when the available. -
Learned in Speech Recognition: Contextual Acoustic Word Embeddings
LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS Shruti Palaskar∗, Vikas Raunak∗ and Florian Metze Carnegie Mellon University, Pittsburgh, PA, U.S.A. fspalaska j vraunak j fmetze [email protected] ABSTRACT model [10, 11, 12] trained for direct Acoustic-to-Word (A2W) speech recognition [13]. Using this model, we jointly learn to End-to-end acoustic-to-word speech recognition models have re- automatically segment and classify input speech into individual cently gained popularity because they are easy to train, scale well to words, hence getting rid of the problem of chunking or requiring large amounts of training data, and do not require a lexicon. In addi- pre-defined word boundaries. As our A2W model is trained at the tion, word models may also be easier to integrate with downstream utterance level, we show that we can not only learn acoustic word tasks such as spoken language understanding, because inference embeddings, but also learn them in the proper context of their con- (search) is much simplified compared to phoneme, character or any taining sentence. We also evaluate our contextual acoustic word other sort of sub-word units. In this paper, we describe methods embeddings on a spoken language understanding task, demonstrat- to construct contextual acoustic word embeddings directly from a ing that they can be useful in non-transcription downstream tasks. supervised sequence-to-sequence acoustic-to-word speech recog- Our main contributions in this paper are the following: nition model using the learned attention distribution. On a suite 1. We demonstrate the usability of attention not only for aligning of 16 standard sentence evaluation tasks, our embeddings show words to acoustic frames without any forced alignment but also for competitive performance against a word2vec model trained on the constructing Contextual Acoustic Word Embeddings (CAWE). -
Machine Translation for Language Preservation
Machine translation for language preservation Steven BIRD1,2 David CHIANG3 (1) Department of Computing and Information Systems, University of Melbourne (2) Linguistic Data Consortium, University of Pennsylvania (3) Information Sciences Institute, University of Southern California [email protected], [email protected] ABSTRACT Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of document- ing the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected in documentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands of Papua New Guinea. KEYWORDS: endangered languages, documentary linguistics, language resources, bilingual texts, comparative lexicons. Proceedings of COLING 2012: Posters, pages 125–134, COLING 2012, Mumbai, December 2012. 125 1 Introduction Most of the world’s 6800 languages are relatively unstudied, even though they are no less im- portant for scientific investigation than major world languages. For example, before Hixkaryana (Carib, Brazil) was discovered to have object-verb-subject word order, it was assumed that this word order was not possible in a human language, and that some principle of universal grammar must exist to account for this systematic gap (Derbyshire, 1977). -
Knowledge-Powered Deep Learning for Word Embedding
Knowledge-Powered Deep Learning for Word Embedding Jiang Bian, Bin Gao, and Tie-Yan Liu Microsoft Research {jibian,bingao,tyliu}@microsoft.com Abstract. The basis of applying deep learning to solve natural language process- ing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually con- tains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it. Fortunately, text itself already contains well- defined morphological and syntactic knowledge; moreover, the large amount of texts on the Web enable the extraction of plenty of semantic knowledge. There- fore, it makes sense to design novel deep learning algorithms and systems in order to leverage the above knowledge to compute more effective word embed- dings. In this paper, we conduct an empirical study on the capacity of leveraging morphological, syntactic, and semantic knowledge to achieve high-quality word embeddings. Our study explores these types of knowledge to define new basis for word representation, provide additional input information, and serve as auxiliary supervision in deep learning, respectively. Experiments on an analogical reason- ing task, a word similarity task, and a word completion task have all demonstrated that knowledge-powered deep learning can enhance the effectiveness of word em- bedding. 1 Introduction With rapid development of deep learning techniques in recent years, it has drawn in- creasing attention to train complex and deep models on large amounts of data, in order to solve a wide range of text mining and natural language processing (NLP) tasks [4, 1, 8, 13, 19, 20]. -
Local Homology of Word Embeddings
Local Homology of Word Embeddings Tadas Temcinasˇ 1 Abstract Intuitively, stratification is a decomposition of a topologi- Topological data analysis (TDA) has been widely cal space into manifold-like pieces. When thinking about used to make progress on a number of problems. stratification learning and word embeddings, it seems intu- However, it seems that TDA application in natural itive that vectors of words corresponding to the same broad language processing (NLP) is at its infancy. In topic would constitute a structure, which we might hope this paper we try to bridge the gap by arguing why to be a manifold. Hence, for example, by looking at the TDA tools are a natural choice when it comes to intersections between those manifolds or singularities on analysing word embedding data. We describe the manifolds (both of which can be recovered using local a parallelisable unsupervised learning algorithm homology-based algorithms (Nanda, 2017)) one might hope based on local homology of datapoints and show to find vectors of homonyms like ‘bank’ (which can mean some experimental results on word embedding either a river bank, or a financial institution). This, in turn, data. We see that local homology of datapoints in has potential to help solve the word sense disambiguation word embedding data contains some information (WSD) problem in NLP, which is pinning down a particular that can potentially be used to solve the word meaning of a word used in a sentence when the word has sense disambiguation problem. multiple meanings. In this work we present a clustering algorithm based on lo- cal homology, which is more relaxed1 that the stratification- 1. -
Word Embedding, Sense Embedding and Their Application to Word Sense Induction
Word Embeddings, Sense Embeddings and their Application to Word Sense Induction Linfeng Song The University of Rochester Computer Science Department Rochester, NY 14627 Area Paper April 2016 Abstract This paper investigates the cutting-edge techniques for word embedding, sense embedding, and our evaluation results on large-scale datasets. Word embedding refers to a kind of methods that learn a distributed dense vector for each word in a vocabulary. Traditional word embedding methods first obtain the co-occurrence matrix then perform dimension reduction with PCA. Recent methods use neural language models that directly learn word vectors by predicting the context words of the target word. Moving one step forward, sense embedding learns a distributed vector for each sense of a word. They either define a sense as a cluster of contexts where the target word appears or define a sense based on a sense inventory. To evaluate the performance of the state-of-the-art sense embedding methods, I first compare them on the dominant word similarity datasets, then compare them on my experimental settings. In addition, I show that sense embedding is applicable to the task of word sense induction (WSI). Actually we are the first to show that sense embedding methods are competitive on WSI by building sense-embedding-based systems that demonstrate highly competitive performances on the SemEval 2010 WSI shared task. Finally, I propose several possible future research directions on word embedding and sense embedding. The University of Rochester Computer Science Department supported this work. Contents 1 Introduction 3 2 Word Embedding 5 2.1 Skip-gramModel................................ -
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-Mail: Olivia [email protected]
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-mail: [email protected] Author: Constanza Gerding-Salas E-mail: [email protected] Author: Susan Stringer-O’Keeffe E-mail: [email protected] Source: http://www.translationdirectory.com The Need for Translation IT has produced a screen culture that Technology tends to replace the print culture, with printed documents being dispensed Advances in information technology (IT) with and information being accessed have combined with modern communication and relayed directly through computers requirements to foster translation automation. (e-mail, databases and other stored The history of the relationship between information). These computer documents technology and translation goes back to are instantly available and can be opened the beginnings of the Cold War, as in the and processed with far greater fl exibility 1950s competition between the United than printed matter, with the result that the States and the Soviet Union was so intensive status of information itself has changed, at every level that thousands of documents becoming either temporary or permanent were translated from Russian to English and according to need. Over the last two vice versa. However, such high demand decades we have witnessed the enormous revealed the ineffi ciency of the translation growth of information technology with process, above all in specialized areas of the accompanying advantages of speed, knowledge, increasing interest in the idea visual -
Language Service Translation SAVER Technote
TechNote August 2020 LANGUAGE TRANSLATION APPLICATIONS The U.S. Department of Homeland Security (DHS) AEL Number: 09ME-07-TRAN established the System Assessment and Validation for Language translation equipment used by emergency services has evolved into Emergency Responders commercially available, mobile device applications (apps). The wide adoption of (SAVER) Program to help cell phones has enabled language translation applications to be useful in emergency responders emergency scenarios where first responders may interact with individuals who improve their procurement speak a different language. Most language translation applications can identify decisions. a spoken language and translate in real-time. Applications vary in functionality, Located within the Science such as the ability to translate voice or text offline and are unique in the number and Technology Directorate, of available translatable languages. These applications are useful tools to the National Urban Security remedy communication barriers in any emergency response scenario. Technology Laboratory (NUSTL) manages the SAVER Overview Program and conducts objective operational During an emergency response, language barriers can challenge a first assessments of commercial responder’s ability to quickly assess and respond to a situation. First responders equipment and systems must be able to accurately communicate with persons at an emergency scene in relevant to the emergency order to provide a prompt, appropriate response. The quick translations provided responder community. by mobile apps remove the language barrier and are essential to the safety of The SAVER Program gathers both first responders and civilians with whom they interact. and reports information about equipment that falls within the Before the adoption of smartphones, first responders used language interpreters categories listed in the DHS and later language translation equipment such as hand-held language Authorized Equipment List translation devices (Figure 1).