539 research outputs found
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Large Corpus of Czech Parliament Plenary Hearings
No full textWe present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion
Two Huge Title and Keyword Generation Corpora of Research Articles
No full textRecent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines
Efficiently Reusing Old Models Across Languages via Transfer Learning
No full textRecent progress in neural machine translation is directed towards larger neural networks trained on an increasing amount of hardware resources.
As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint.
It is especially true in transfer learning for its additional cost of training
the ``parent'' model before transferring knowledge and training the desired ``child'' model.
In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture.
Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs.
We achieve better translation quality and shorter convergence times than when training from random initialization
Removing European Language Barriers with Innovative Machine Translation Technology
No full textThis paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech
translation for conferences and remote meetings live subtitling. The platform has been designed with a focus on very low latency and
high flexibility while allowing research prototypes of speech and text processing tools to be easily connected, regardless of where they
physically run. We outline our architecture solution and also briefly compare it with the ELG platform. Technical details are provided
on the most important components and we summarize the test deployment events we ran so far
Presenting Simultaneous Translation in Limited Space
No full textSome methods of automatic simultaneous translation of a long-form speech allow revisions of outputs,
trading accuracy for low latency. Deploying these systems
for users faces the problem of presenting subtitles in a limited space, such as two lines on a television screen. The
subtitles must be shown promptly, incrementally, and with
adequate time for reading. We provide an algorithm for
subtitling. Furthermore, we propose a way how to estimate
the overall usability of the combination of automatic translation and subtitling by measuring the quality, latency, and
stability on a test set, and propose an improved measure
for translation latency
Extending Ptakopět for Machine Translation User Interaction Experiments
No full textThe problems of outbound translation, machine translation user confidence and user interaction are not yet fully explored. The goal of the online modular system Ptakopět is to provide tools for studying these phenomena. Ptakopět is a proof-of-concept system for examining user interaction with enhanced machine translation. It can be used either for actual translation or running experiments on human annotators. In this article, we aim to describe its main components and to show how to use Ptakopět for further research. We also share tips for running experiments and setting up a similar online annotation environment.
Ptakopět was already used for outbound machine translation experiments, and we cover the results of the latest experiment in a demonstration to show the research potential of this tool. We show quantitatively that even though backward translation improves machine-translation user experience, it mainly increases users' confidence and not the translation quality
How Many Pages? Paper Length Prediction from the Metadata
No full textBeing able to predict the length of a scientific paper may be helpful in numerous situations. This work defines the paper length prediction task as a regression problem and reports several experimental results using popular machine learning models. We also create a huge dataset of publication metadata and the respective lengths in
number of pages. The dataset will be freely available and is intended to foster research in this domain. As future work, we would like to
explore more advanced regressors based on neural networks and big pretrained language models
Cross-Lingual Information Retrieval
No full textCross-Lingual Information Retrieval (for Elitr LangTools workshop at Eurosai 2020
European Language Grid: An Overview
No full textWith 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes
Expand and Filter: CUNI and LMU Systems for the WNGT 2020 Duolingo Shared Task
No full textWe present our submission to the Simultaneous Translation And Paraphrase for Language Education (STAPLE) challenge. We used a standard Transformer model for translation, with a crosslingual classifier predicting correct translations on the output n-best list. To increase the diversity of the outputs, we used additional data to train the translation model, and we trained a paraphrasing model based on the Levenshtein Transformer architecture to generate further synonymous translations. The paraphrasing results were again filtered using our classifier. While the use of additional data and our classifier filter were able to improve results, the paraphrasing model produced too many invalid outputs to further improve the output quality. Our model without the paraphrasing component finished in the middle of the field for the shared task, improving over the best baseline by a margin of 10-22 % weighted F1 absolute