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Few-Shot Learning Evaluation in Natural Language Understanding
Subhabrata Mukherjee · Xiaodong Liu · Guoqing Zheng · Saghar Hosseini · Hao Cheng · Ge Yang · Christopher Meek · Ahmed Awadallah · Jianfeng Gao

Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now matched or exceeded “human-level” performance on many tasks in these benchmarks. Most of these benchmarks, however, allow models to have access to relatively large amounts of labeled data for model training. As such, these models are provided far more data than required by humans to achieve strong performance. That has motivated a line of work that focused on improving few-shot learning performance of NLU models. To help accelerate this line of work, we introduce CLUES, a few-shot learning benchmark for evaluating the few-shot learning capabilities of NLU models. We demonstrate that while recent models reach human performance in the traditional settings, where they have access to large amount of labeled data, there is still a sizable gap in performance in the few-shot setting. We also demonstrate differences between alternative model families and adaptation techniques in the few shot setting. Finally, we discuss and contrast different experimental settings for evaluating few-shot leaning performance and suggest a unified standardized approach to few-shot learning evaluation. We aim to encourage NLU research in the few-shot learning setting to encourage development of general purpose NLU models that can generalize to new tasks with a small number of examples.

Author Information

Subhabrata Mukherjee (Microsoft Research)

I am a senior scientist at Microsoft Research (MSR) working at the intersection of natural language understanding, deep learning and transfer learning. My current research is focused on making AI accessible to all with two major themes: (1) Scaling deep and large-scale natural language understanding models to scenarios with limited computational resources leveraging techniques like self-supervised, weakly supervised and curriculum learning, data augmentation, knowledge distillation, etc. (2) Building trustworthy AI for mitigating misinformation and bias to provide fair and equitable information access to all. Prior to joining MSR, I was leading the information extraction efforts to build the Amazon Product Knowledge Graph, an authoritative knowledge graph for all products in the world. I graduated summa cum laude from the Max Planck Institute for Informatics, Germany with a PhD in 2017. I was awarded the 2018 SIGKDD Doctoral Dissertation Runner-up Award for my thesis on credibility analysis and misinformation.

Xiaodong Liu (Microsoft)
Guoqing Zheng (Carnegie Mellon University)
Saghar Hosseini
Hao Cheng (Microsoft)
Ge Yang (Microsoft Research)
Christopher Meek (Microsoft Research)
Ahmed Awadallah (MICROSOFT RESEARCH)

I am passionate about using AI and Machine Learning to create intelligent user experiences that connect people to information. I lead a research and incubation team in Microsoft Research Technologies. Our work at the Language and Information Technologies team is focused on creating language understanding and user modeling technologies to enable intelligent experiences in multiple products. Our work has been shipped in several products such as Bing, Cortana, Office 365, and Dynamics 365. I have hands-on experience building and shipping state-of-the-art ML/AI algorithms. I also have experience building and managing world-class teams of scientists and engineers. My research interests are at the intersection of machine learning, language understanding, and information retrieval. A key part of my work involves using Machine Learning to model large-scale text and user behavior data with applications to intelligent assistants, search, user modeling, quality evaluation, recommendation and personalization. I received my Ph.D. from the department of Computer Science and Engineering at the University of Michigan Ann Arbor. I Invented, published, and patented new approaches in language understanding, information retrieval and machine learning. I published 60+ peer-reviewed papers in these areas and I am an inventor on 20+ (granted and pending) patents.

Jianfeng Gao (Microsoft Research, Redmond, WA)

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