Self-Training for Jointly Learning to Ask and Answer Questions
By Mrinmaya Sachan, Eric P. Xing. 2018
88 citations recorded by the source corpus.
Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Yejin Choi
- Mohit Bansal
- Ronan Le Bras
- Chandra Bhagavatula
- Hannaneh Hajishirzi
- Vered Shwartz
- Swarat Chaudhuri
- Luke S. Zettlemoyer
In Natural Language Processing
- Yejin Choi
- Mohit Bansal
- Ronan Le Bras
- Chandra Bhagavatula
- Vered Shwartz
- Swarat Chaudhuri
- Hannaneh Hajishirzi
- Peter West
Citations in the corpus
- Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, background): “Sachan and Xing (2018) also designed question selection oracles based on curriculum learning strategy in their QA-QG self-training circle.”
- Unsupervised Commonsense Question Answering with Self-Talk (2020, background): “Motivations vary from data augmentation to QA tasks (Du et al., 2017; Dhingra et al., 2018; Du and Cardie, 2018; Sachan and Xing, 2018; Fabbri et al., 2020) through conversational machine read-”
- Span Selection Pre-training for Question Answering (2019, methodology): “Self training (Sachan and Xing, 2018) has also been used to jointly train to construct questions and gen-”
- Neurosymbolic Programming (2021, methodology): “Pseudo-labels have been used most prominently in the context of self-training (Daumé III, 2008; Sachan and Xing, 2018), whereby a model trained a small labeled dataset is used to generate”