2/18/2023 0 Comments Forspoken wikiWe believe the challenge is prevalent in research from both academia and industry. In this workshop, we aim to cover challenges in a lifelong process where new users or functionalities are added, and existing functionalities are modified. The approach also needs to be scalable to learn from both small limited-data sets at the beginning of a system’s life-cycle to larger data sets with millions of annotated data and/or billions of unannotated data as deployed systems expand to larger user bases and use cases. To address such issues, efforts for real-world applications need improved methods for targeting new use cases, features or classes. Thus, domain transfer (especially from limited annotated data or using only unsupervised techniques) is needed to make the technology work for new scenarios. For example, current machine reading comprehension models do very well answering general, factoid style questions, but perform poorly on new specialized domains such as legal documents, operational manuals, financial policies, etc. The ability to efficiently move real-world systems to new domains and languages, or to adapt to changing conditions over time also often requires a complex mixture of techniques including active learning, transfer learning, continuous on-line learning, semi-supervised learning, and data augmentation as the models used by existing systems rarely generalize well to new circumstances. Privacy (transfer learning from one language to another often requires to move data from one continent to another, which violates privacy policies) Ĭontinual learning (introducing new classes but also merging or removing old ones). The literature on bootstrapping ML systems often overlooks the constraints of real-world applications related to:Īnnotation processes (examples are often annotated by batches instead of one by one) This slows down the development of new features and products. However, data collection and manual annotation is a time-consuming, expensive process often requiring a variety of bootstrapping methods to produce models that are “good enough”. Machine learning for speech and language understanding tasks often strongly relies on large annotated data-sets to train the models. The workshop will be collocated with AACL 2020.
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