Learning Autocomplete Systems as a Communication Game
By Mina Lee, Tatsunori B. Hashimoto, Percy Liang. 2019
14 citations recorded by the source corpus.
We study textual autocomplete---the task of predicting a full sentence from a partial sentence---as a human-machine communication game. Specifically, we consider three competing goals for effective communication: use as few tokens as possible (efficiency), transmit sentences faithfully (accuracy), and be learnable to humans (interpretability). We propose an unsupervised approach which tackles all three desiderata by constraining the communication scheme to keywords extracted from a source sentence for interpretability and optimizing the efficiency-accuracy tradeoff. Our experiments show that this approach results in an autocomplete system that is 52% more accurate at a given efficiency level compared to baselines, is robust to user variations, and saves time by nearly 50% compared to typing full sentences.
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
- Michihiro Yasunaga
- Percy Liang
- Jonathan Berant
- Luke S. Zettlemoyer
- Jianfeng Gao
- Wen-tau Yih
- Matt Gardner
- Mohit Bansal
Citations in the corpus
- Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, methodology): “A common approach is to increase the source side data, for instance by adding modifications or sampling from generative models (Hannun et al., 2014; Jia & Liang, 2016; Krizhevsky et al., 2017; Antoniou et al., 2017; Yasunaga et al., 2018; Yu et al., 2018a; Lee et al., 2019; Berthelot et al., 2019; X”