Papers with hybrid systems
Improving homograph disambiguation with supervised machine learning (L18-1)
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| Challenge: | a new system for text-to-speech synthesis uses rule-based homograph disambiguation . a simple application of machine learning produces significant improvements in homograph ambiguity . |
| Approach: | They propose a rule-based homograph disambiguation system for text-to-speech synthesis at Google . they compare it to a new system which performs disambiguations using classifiers trained on labeled data . |
| Outcome: | The proposed system is more accurate than hand-written rules or machine learning alone. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
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| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
Bridging Perception, Memory, and Inference through Semantic Relations (2021.emnlp-main)
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| Challenge: | Recent studies suggest that it is impossible to learn meaning from surface form alone. |
| Approach: | They propose to develop triadic systems that combine neural and symbolic methods to provide a seamless information flow between them. |
| Outcome: | The proposed systems combine the strengths of neural and symbolic methods to achieve a seamless information flow between them. |