Papers by Guy Tevet
Evaluating Text GANs as Language Models (N19-1)
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| Challenge: | Generative Adversarial Networks (GANs) do not suffer from the problem of exposure bias. |
| Approach: | They propose to approximate the distribution of text generated by a GAN and compare it to traditional probability-based LM metrics. |
| Outcome: | The proposed method performs significantly worse than state-of-the-art LMs on several GAN-based models and can accelerate progress in GAN text generation. |
Evaluating the Evaluation of Diversity in Natural Language Generation (2021.eacl-main)
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| Challenge: | Existing methods for controlling diversity by tuning a “decoding parameter” affect form but not meaning. |
| Approach: | They propose a framework that measures correlation between a diversity metric and a parameter that controls some aspect of diversity in generated text. |
| Outcome: | The proposed framework outperforms existing methods in estimating diversity . it shows that humans outperformed existing methods but affect form but not meaning . |