Papers with deep reinforcement learning approach
Quantitative Day Trading from Natural Language using Reinforcement Learning (2021.naacl-main)
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| Challenge: | Existing approaches to stock prediction are not optimized to make profitable investment decisions. |
| Approach: | They propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. |
| Outcome: | The proposed method outperforms state-of-the-art in terms of risk-adjusted returns on two benchmarks: Tweets (English) and financial news (Chinese) |
Paraphrase Generation with Deep Reinforcement Learning (D18-1)
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| Challenge: | Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT) |
| Approach: | They propose a deep reinforcement learning approach to automatic paraphrase generation . they propose supervised learning and reinforcement learning for evaluators . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in paraphrase generation on two datasets. |