Unifying Question Answering, Text Classification, and Regression via Span Extraction
By Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher. 2019
44 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Hannaneh Hajishirzi
- Mohit Bansal
- Ashish Sabharwal
- Jianfeng Gao
- Peter Clark
- Luke S. Zettlemoyer
- Yejin Choi
- Tushar Khot
Citations in the corpus
- UnifiedQA: Crossing Format Boundaries With a Single QA System (2020, methodology): “The YN datasets we use are BoolQ (Clark et al., 2019a) and a naturally-perturbed version of this dataset, BoolQNP (Khashabi et al., 2020), and the binary (yes/no) subset of MultiRC (Khashabi et al., 2018).”
- BAM! Born-Again Multi-Task Networks for Natural Language Understanding (2019, background): “Concurrently with our work, several other recent works also explore fine-tuning BERT using multiple tasks (Phang et al., 2018; Liu et al., 2019b; Keskar et al., 2019).”
- mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2020, background): “Similar unifying frameworks were proposed by Keskar et al. (2019) and McCann et al. (2018).”
- The Flan Collection: Designing Data and Methods for Effective Instruction Tuning (2023, background): “UnifiedQA and others (Khashabi et al., 2020; McCann et al., 2018; Keskar et al., 2019) unified a wide range of NLP tasks into a single generative question answering format, using prompt instructions for multi-task finetuning and evaluation.”
- Improving In-Context Few-Shot Learning via Self-Supervised Training (2022, background): “…on unifying task formats to maximize the benefits of human annotations, including question answering (Zhong et al., 2021), textual entailment (Yin et al., 2019, 2020; Wang et al., 2021a), and many other tasks (McCann et al., 2018; Keskar et al., 2019; Raffel et al., 2020; Bragg et al., 2021).”
- HUBERT Untangles BERT to Improve Transfer across NLP Tasks (2019, methodology): “Transfer learning for transformer-based models has been studied recently: Keskar et al. (2019) and Wang et al. (2019) report improvements in accuracy over BERT after training on an intermediate task from GLUE; an approach which has come to be known as Supplementary Training on Intermediate Labeled…”