Domain adaptation with BERT-based domain classification and data selection

2019
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The performance of deep neural models can deteriorate substantially when there is a domain shift between training and test data. For example, the pre-trained BERT model can be easily fine-tuned with just one additional output layer to create a state-of-the-art model for a wide range of tasks. However, the fine-tuned BERT model suffers considerably at zero-shot when applied to a different domain. In this paper, we present a novel two-step domain adaptation framework based on curriculum learning and domain-discriminative data selection. The domain adaptation is conducted in a mostly unsupervised manner using a small target domain validation set for hyper-parameter tuning. We tested the framework on four large public datasets with different domain similarities and task types. Our framework outperforms a popular discrepancy-based domain adaptation method on most transfer tasks while consuming only a fraction of the training budget.
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LU, Luxembourg
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GB, London
How can Amazon improve the advertising experience for customers around the world? How can we help advertisers and customers find each other in a meaningful way? Amazon Advertising creates and transforms the connection between retailers/service providers and customers. Our teams strive to reinvent the way advertisers and agencies build brands and drive performance in their advertising. By using Amazon's foundation in e-commerce, we help brands connect with the right customers through creative solutions and formats across screens and devices, and in the physical world. Amazon Advertising seeks a Data Scientist with strong Data Analysis skills to join the ADSP engineering team split across Edinburgh and London. We make Guidance products that help optimise our customer's advertising campaign workflows and performance. As a scientist on the team, you will be involved in many aspects of the process - from idea generation, business analysis and scientific research, through to development - giving you a real sense of ownership. The systems that you help to build will operate at massive scale to advertising customers around the world. Our ideal candidate is an experienced Data scientist who has a track-record of performing analysis, applying statistical techniques and building basic ML models to solve real business problems, who has great leadership and communication skills, and who is motivated to achieve results in a fast-paced environment. Key job responsibilities Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. Promote the culture of experimentation at Amazon.
US, NY, New York
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US, CA, Sunnyvale
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JP, 13, Tokyo
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US, WA, Seattle
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