Active Learning for Post-Editing Based Incrementally Retrained MT

Dara, Aswarth Abhilash; Genabith, Josef van; Li, Qun; Judge, John; Toral, Antonio

doi:10.3115/v1/e14-4036

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…On the other hand, some of the large scale curation efforts, such as ChEMBL, provide funding for expert curators to manually annotate bioassay data, but this is too labor intensive to execute in detail, and is currently limited to identifying the target ( Gaulton et al, 2012 ). Approaches such as Active Learning (AL) have also been applied to classification of domain specific text documents ( Cohn, Ghahraman & Jordan, 1996 ; Dara et al, 2014 ; Tomanek & Hahn, 2007 ). Our objective in this work is to provide the necessary capabilities to annotate bioassay protocols, in a significant level of detail, such that the semantic content is a relatively complete description of the assay.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Clark

Bunin

Litterman

et al. 2014

PeerJ

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Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

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Section: Introductionmentioning

confidence: 99%

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Clark

Bunin

Litterman

et al. 2014

PeerJ

View full text Add to dashboard Cite

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“…The application of AL techniques to MT involves asking a human oracle to supervise a fraction of the incoming data (Bloodgood and Callison-Burch, 2010). Once the human has revised these samples, they are used to improve the MT system, via incremental (González-Rubio et al, 2012) or batch learning (Dara et al, 2014). Therefore, a key element of AL is the so-called sampling strategy, which determines the sentences that should be corrected by the human.…”

Section: Active Learning For the Interactive-predictive Translation Omentioning

confidence: 99%

Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning

Abril¹

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Chapter 1 frames the scope of this thesis, introducing the pattern recognition field and, more specifically, the MT field. It reviews the different historical approaches devised to tackle this problem. Moreover, it sets the experimental framework followed in this thesis and the main scientific objectives. Chapter 2 describes the mathematical model that represents the core of the thesis: neural networks. It addresses the parameter estimation process, describes different neural architectures and a number of techniques used along the thesis to improve the generalization capability of the model. Chapter 3 introduces the neural machine translation technology, describing the most common architectures and decoding process. Moreover, it reviews different aspects relating the NMT field that nowadays receive the attention of the research community. It also compares NMT in the different translation tasks that will be tackled in the thesis. Chapter 4 introduces the interactive-predictive pattern recognition field, that aims to minimize the effort spent by the user while supervising an automatic system. It proposes the application of this theoretical framework to the neural technology, introducing alternative interaction protocols. After that, these interactivepredictive neural systems are evaluated. Chapter 5 describes the adaptation of NMT systems via online learning techniques. After receiving a corrected sample, the system can be updated to include this new knowledge. Here are described the methods to perform this adaptation and introduces two novel alternatives. In addition, an active learning framework for neural systems is proposed, useful for a situation that requires the translation of large amounts of data. All these scenarios are thoroughly evaluated in a variety of conditions, including a user evaluation involving professional post-editors. Chapter 6 departs from the MT problem to tackle different multimodal sequenceto-sequence tasks. More precisely, it is focused on the generation textual descriptions of videos. These techniques are also applied to the captioning of daily events, captured with an egocentric camera. Finally, the interactive-predictive framework described in Chapter 4 is applied to these multimodal systems. Chapter 7 draws the main conclusions of the thesis, describing the scientific contributions and publications derived from it and traces several lines of future research. These chapters are complemented by two appendices. Appendix A describes NMT-Keras, an open-source library developed to build neural models, that has been used to carry out most of the experiments described in the thesis. In Appendix B we provide the results of a survey carried out in the scope of Chapter 5.

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Active Learning for Post-Editing Based Incrementally Retrained MT

Cited by 2 publications

References 7 publications

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning

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