Multi-Agent Dual Learning

By Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, Chengxiang Zhai, Tie-Yan Liu. 2019

62 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Tao Qin
2. Yingce Xia
3. Tie-Yan Liu
4. Lijun Wu
5. Fei Tian
6. Charles Sutton
7. Denny Zhou
8. Jason Wei

Citations in the corpus

• PaLM: Scaling Language Modeling with Pathways (2022, background): “…DROP (Dua et al., 2019), CoQA (Reddy et al., 2018), QuAC (Choi et al., 2018), SQuADv2 (Rajpurkar et al., 2018), RACE (Lai et al., 2017) • SuperGLUE (Wang et al., 2019a) • Natural Language Inference (NLI): Adversarial NLI (Nie et al., 2019) Table 4 includes the results for the PaLM 540B…”
• Finetuned Language Models Are Zero-Shot Learners (2021, methodology): “As further analysis, we train continuous prompts for each of the SuperGLUE (Wang et al., 2019a) tasks in accordance with the cluster splits from §2.2 such that when prompt-tuning on task T , no tasks in the same cluster as T were seen during instruction tuning.”
• Microsoft Research Asia’s Systems for WMT19 (2019, background): “Our submitted German → French is a single sys-tem trained by MADL, achieving 37 .”
• Data Diversification: A Simple Strategy For Neural Machine Translation (2019, background): “We demonstrate that data diversification outperforms other related methods – knowledge distillation [13] and dual learning [27], and is complementary to back-translation [17] in semi-supervised setup.”
• Depth Growing for Neural Machine Translation (2019, background): “…deep neural networks, has gained rapid progress in recent years (Bahdanau et al., 2015; Sutskever et al., 2014; Sennrich et al., 2016b; He et al., 2016a; Sennrich et al., 2016a; Xia et al., 2017; Wang et al., 2019) and achieved significant improvement in translation quality (Has-san et al., 2018).”
• Using natural language prompts for machine translation (2022, unknown): “In particular, we use the results of Edunov et al. (2018) for English-French, the results of Wang et al. (2019) for English-German, and the results of Caswell et al. (2019) for English-Romanian.”
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, methodology): “GANs have also been used in other machine learning tasks [290] such as active learning [291], [292], online learning [293], ensemble learning [294], zero-shot learning [295], [296], and multi-task learning [297].”