Comparable Multilingual Patents as Large-Scale Parallel Corpora

2013 ◽  
pp. 167-187
Author(s):  
Bin Lu ◽  
Ka Po Chow ◽  
Benjamin K. Tsou
Keyword(s):  
Large Scale ◽  
Parallel Corpora

scholarly journals The Web as a Parallel Corpus

2003 ◽  
Vol 29 (3) ◽  
pp. 349-380 ◽  
Author(s):  
Philip Resnik ◽  
Noah A. Smith
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Structural Features ◽  
Classification Performance ◽  
Internet Archive ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Original Algorithm ◽  
Parallel Text ◽  
The Web

Parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the STRAND system for mining parallel text on the World Wide Web, first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new content-based measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale. Finally, the value of these techniques is demonstrated in the construction of a significant parallel corpus for a low-density language pair.

scholarly journals From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots

2019 ◽  
Author(s):  
Shizhe Chen ◽  
Qin Jin ◽  
Jianlong Fu
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Learning Approach ◽  
Model Learning ◽  
Neural Machine Translation ◽  
Parallel Corpora ◽  
Translation Model ◽  
Word Level ◽  
Sentence Level ◽  
Progressive Learning

The neural machine translation model has suffered from the lack of large-scale parallel corpora. In contrast, we humans can learn multi-lingual translations even without parallel texts by referring our languages to the external world. To mimic such human learning behavior, we employ images as pivots to enable zero-resource translation learning. However, a picture tells a thousand words, which makes multi-lingual sentences pivoted by the same image noisy as mutual translations and thus hinders the translation model learning. In this work, we propose a progressive learning approach for image-pivoted zero-resource machine translation. Since words are less diverse when grounded in the image, we first learn word-level translation with image pivots, and then progress to learn the sentence-level translation by utilizing the learned word translation to suppress noises in image-pivoted multi-lingual sentences. Experimental results on two widely used image-pivot translation datasets, IAPR-TC12 and Multi30k, show that the proposed approach significantly outperforms other state-of-the-art methods.

scholarly journals The Language Demographics of Amazon Mechanical Turk

2014 ◽  
Vol 2 ◽  
pp. 79-92 ◽  
Author(s):  
Ellie Pavlick ◽  
Matt Post ◽  
Ann Irvine ◽  
Dmitry Kachaev ◽  
Chris Callison-Burch
Keyword(s):  
Large Scale ◽  
Language Skills ◽  
Statistical Machine Translation ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Indian Languages ◽  
Parallel Corpora ◽  
Bilingual Dictionaries ◽  
Bilingual Speakers ◽  
Translation Systems

We present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk). We establish a methodology for determining the language skills of anonymous crowd workers that is more robust than simple surveying. We validate workers’ self-reported language skill claims by measuring their ability to correctly translate words, and by geolocating workers to see if they reside in countries where the languages are likely to be spoken. Rather than posting a one-off survey, we posted paid tasks consisting of 1,000 assignments to translate a total of 10,000 words in each of 100 languages. Our study ran for several months, and was highly visible on the MTurk crowdsourcing platform, increasing the chances that bilingual workers would complete it. Our study was useful both to create bilingual dictionaries and to act as census of the bilingual speakers on MTurk. We use this data to recommend languages with the largest speaker populations as good candidates for other researchers who want to develop crowdsourced, multilingual technologies. To further demonstrate the value of creating data via crowdsourcing, we hire workers to create bilingual parallel corpora in six Indian languages, and use them to train statistical machine translation systems.

scholarly journals ClinkNotes: Towards a Corpus-Based, Machine-Aided Programme of Translation Teaching

2010 ◽  
Vol 55 (2) ◽  
pp. 387-408 ◽  
Author(s):  
Chunshen Zhu ◽  
Po-Ching Yip
Keyword(s):  
Large Scale ◽  
Translation Studies ◽  
Pilot Project ◽  
Theoretical Framework ◽  
Primary Data ◽  
Tertiary Level ◽  
Parallel Corpora ◽  
Actual Design ◽  
Further Development

This article presents a report on a pilot project designed to construct a platform for large-scale teaching of translation or bilingual training at tertiary level. The programme, ClinkNotes, has the potential of accommodating parallel corpora of any language pairs, although the primary data used in this project are in English and Chinese. The report begins with a brief overview of the development of corpus-based approach to translation studies in relation to that of translation teaching as a profession. It then proceeds to describe the actual design (i.e., the theoretical framework, the methodology of annotation, and the simple execution of the software programme), and how it helps to cater to the pressing needs of the profession. The prospects of further development of the programme are also discussed.

scholarly journals Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rui Wang
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Large Scale ◽  
Domain Adaptation ◽  
Structural Information ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Great Success ◽  
Parallel Corpora ◽  
Translation Rule

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation.

scholarly journals Exploring Bilingual Parallel Corpora for Syntactically Controllable Paraphrase Generation

2020 ◽  
Author(s):  
Mingtong Liu ◽  
Erguang Yang ◽  
Deyi Xiong ◽  
Yujie Zhang ◽  
Chen Sheng ◽  
...  
Keyword(s):  
Language Processing ◽  
Latent Variable ◽  
Large Scale ◽  
Fine Tuning ◽  
Parallel Corpora ◽  
Shared Content ◽  
Proposed Model ◽  
Parallel Data ◽  
Cross Lingual ◽  
Paraphrase Generation

Paraphrase generation is of great importance to many downstream tasks in natural language processing. Recent efforts have focused on generating paraphrases in specific syntactic forms, which, generally, heavily relies on manually annotated paraphrase data that is not easily available for many languages and domains. In this paper, we propose a novel end-to-end framework to leverage existing large-scale bilingual parallel corpora to generate paraphrases under the control of syntactic exemplars. In order to train one model over the two languages of parallel corpora, we embed sentences of them into the same content and style spaces with shared content and style encoders using cross-lingual word embeddings. We propose an adversarial discriminator to disentangle the content and style space, and employ a latent variable to model the syntactic style of a given exemplar in order to guide the two decoders for generation. Additionally, we introduce cycle and masking learning schemes to efficiently train the model. Experiments and analyses demonstrate that the proposed model trained only on bilingual parallel data is capable of generating diverse paraphrases with desirable syntactic styles. Fine-tuning the trained model on a small paraphrase corpus makes it substantially outperform state-of-the-art paraphrase generation models trained on a larger paraphrase dataset.

scholarly journals Improving Context-Aware Neural Machine Translation with Source-side Monolingual Documents

2021 ◽  
Author(s):  
Linqing Chen ◽  
Junhui Li ◽  
Zhengxian Gong ◽  
Xiangyu Duan ◽  
Boxing Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Context Aware ◽  
Training Task ◽  
Neural Machine Translation ◽  
Parallel Corpora ◽  
Global Context ◽  
Original Sentence ◽  
Training Approach ◽  
Left And Right

Document context-aware machine translation remains challenging due to the lack of large-scale document parallel corpora. To make full use of source-side monolingual documents for context-aware NMT, we propose a Pre-training approach with Global Context (PGC). In particular, we first propose a novel self-supervised pre-training task, which contains two training objectives: (1) reconstructing the original sentence from a corrupted version; (2) generating a gap sentence from its left and right neighbouring sentences. Then we design a universal model for PGC which consists of a global context encoder, a sentence encoder and a decoder, with similar architecture to typical context-aware NMT models. We evaluate the effectiveness and generality of our pre-trained PGC model by adapting it to various downstream context-aware NMT models. Detailed experimentation on four different translation tasks demonstrates that our PGC approach significantly improves the translation performance of context-aware NMT. For example, based on the state-of-the-art SAN model, we achieve an averaged improvement of 1.85 BLEU scores and 1.59 Meteor scores on the four translation tasks.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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