Papers with GigaWord
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)
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| Challenge: | LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released. |
| Approach: | They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software. |
| Outcome: | The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released. |
Practical Takes on Federated Learning with Pretrained Language Models (2023.findings-eacl)
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| Challenge: | federated learning with pretrained language models for language tasks entails data privacy constraints when learning from diverse data domains. |
| Approach: | They propose to use pretrained language models to learn from diverse data domains . they elaborate hypotheses over the components in federated NLP architectures based on three tasks . |
| Outcome: | The proposed model can generalize by adapting to the different domains. |