The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. We are open to hiring candidates to work out of one of the following locations: Beijing, CHN We are also looking for talents with experiences/expertise in building large-scale, high-performing systems. We are hiring in all areas of human language technology: ASR, MT, NLU, text-to-speech (TTS), and Dialog Management, in addition to Computer Vision. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. As part of our AI team in Amazon, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). We demonstrate the effectiveness of our approach by two experiments: 1) we do online A/B testing experiments in multiple countries, which show significant improvements in key business metrics 2) we conduct a human audit to evaluate the quality of the extracted synonym rules, which indicates that 85.5% of the extracted rules are high quality pairs.Īmazon is looking for a passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Speech and Language technology. A two-phase unsupervised synonym discovery framework is proposed for extracting synonym rules from the search log data. Comparing to existing methods, the purposed framework has two main differences: 1) instead of extending and rewriting user-input queries, we make the synonym tokens searchable by adding them into the text descriptions of products 2) the whole process is unsupervised and doesn’t require any human labels.
In particular, we propose an unsupervised synonym extraction framework for document enhancement. In this work, we describe another way to address vocabulary gap issues in the e-commerce search systems. Most of the existing methods deal with the vocabulary gap issues by rewriting user-input queries. The vocabulary gap between search queries and product descriptions is an important problem in modern e-commerce search engines.
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