Our team advances the state of the art in natural language understanding and generation and deploys these systems at scale to break down language barriers, enable people to understand and communicate with anyone and provide a safe experience—no matter what language they speak.
The opportunities and challenges of this work are immense. Billions of people use our services to connect and communicate in their preferred language, but many of these languages lack traditional NLP resources and our systems need to be robust to the informal tone, slang, and typos often found in daily communication.
Our research spans multiple areas across NLP and machine learning, including deep learning/neural networks, machine translation, natural language understanding and generation, low-resource NLP, question answering, dialogue, and cross-lingual and cross-domain transfer learning.
Natural Language Processing (NLP) research at Froxt focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Froxt, impacting user experience in Cloud, mobile, apps, and more.
Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.
Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data and recently have incorporated neural net technology.