Papers with neural-based approaches
Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)
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| Challenge: | End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters. |
| Approach: | They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems. |
| Outcome: | The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark. |
Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)
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| Challenge: | Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors. |
| Approach: | They propose a model that integrates the strengths of both neural and symbolic models for factor mining. |
| Outcome: | The proposed model surpasses the SOTA RankIC and RankICIR in predicting S&P 500 returns on real-world stock market data. |