![]() ![]() We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are committed to furthering our culture of inclusion. Inclusive Team Culture Here at AWS, we embrace our differences. You will advance the state of the art in recommender engines, rapid experimentation at scale, and marketing science. Your work will be a key input into a few of our S-Team goals. We are at a pivotal moment in our organization where AI/ML, measurement and experimentation velocity has reached an unseen momentum, and we need to scale fast in order to maintain it. You will work with recognized B2B Marketing Science and AI/ML experts to develop large-scale, high-performing AI/ML models and rapid experimentation capabilities. You are an hands-on innovator who can contribute to advancing Marketing AI/ML and experimentation technology in a B2B environment, and push the limits on what’s scientifically possible with a razor sharp focus on measurable customer and business impact. ![]() You will lead strategic AI/ML and experimentation initiatives across AWS Marketing & Sales ranging anywhere between recommender engines, scaling experimentation and measurement science, real-time inference, and cross-channel orchestration. We are looking for a Principal Applied Scientist with expertise in recommender engines, content ranking and rapid experimentation at scale, with strong interest in building scalable solutions in partnership with our engineering organization. ![]() We work globally as a central team and establish standards, benchmarks, and best practices for use throughout AWS Marketing. As the central data and science organization in AWS Marketing, the Data: Science and Engineering (D:SE) team builds measurement products, AI/ML models for targeting, and self-service insights capabilities for AWS Marketing to drive better measurement and personalization, improve data access and analytical self-service, and empower strategic data-driven decisions. Spoken summarization results are reported on a new dataset: Spoken-Gigaword.Īmazon Web Services (AWS) is building a world-class marketing organization that drives awareness and customer engagement with the goal of educating developers, IT and line-of-business professionals, startups, partners, and executive decision makers about AWS services and solutions, their benefits, and differentiation. 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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