Papers with sequence-to-sequence learning model
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
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| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
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. |