Your solutions will apply to all of Amazon’s consumer and digital businesses including but not limited to, Alexa, Kindle, Amazon Go, Prime Video and more. Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems. A commitment to team work, hustle and strong communication skills (to both business and technical partners) are absolute requirements. The ideal candidate has a great passion for data and an insatiable desire to learn and innovate. You can opt-in for being part of one of the existing or newly formed engineering teams who will contribute to Amazon mission to meet external customers’ privacy rights: Personal Data Classification, The Right to be forgotten, The right of access, or Digital Markets Act – The Right of Portability. Our internal customers are services within Amazon who operate with personal data, Legal Representatives, and Customer Service Agents. Seller central, App / Skill Developers), and Amazon Subsidiaries. Our external customers are world-wide customers of Amazon Retail Website, Amazon B2B services (e.g. We are working backwards from the customers and world-wide privacy regulations, think long term, and propose solutions which will assure Amazon Privacy compliance. Our mission is to develop services which will enable every Amazon service operating with personal data to satisfy the privacy rights of Amazon customers. Amazon SDO Privacy engineering operates in Austin – TX, US and Iasi, Bucharest – Romania. Spoken summarization results are reported on a new dataset: Spoken-Gigaword.Īmazon’s mission is to be earth’s most customer-centric company and our team is the guardian of our customer’s privacy. We obtain state-of-the-art results on spoken language understanding tasks such as SLURP and ATIS. The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model 2) multitask learning objective to exploit shared knowledge from different tasks 3) end-to-end training of ASR and downstream NLP task based on sequence loss. MTL-SLT takes speech as input, and outputs transcription, intent, named entities, summaries, and answers to text queries, supporting the tasks of spoken language understanding, spoken summarization and spoken question answering respectively. To facilitate the development of spoken language research, we introduce MTL-SLT, a multi-task learning framework for spoken language tasks. Prior works focus on independent research by the automatic speech recognition (ASR) and natural language processing (NLP) communities, or on jointly modeling the speech and NLP problems focusing on a single dataset or single NLP task. Language understanding in speech-based systems has attracted extensive interest from both academic and industrial communities in recent years with the growing demand for voice-based applications.
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