Papers by Le Fang
Implicit Deep Latent Variable Models for Text Generation (D19-1)
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| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
| Approach: | They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior . |
| Outcome: | The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. |
Surprisal Predicts Code-Switching in Chinese-English Bilingual Text (2020.emnlp-main)
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| Challenge: | a new study examines the propensity of bilinguals to switch languages . word surprisal and word entropy are important predictors of code-switching . |
| Approach: | They propose high cognitive effort as a reason for code-switching . they use a computational model of surprisal and word entropy to model code-changing . |
| Outcome: | The proposed model shows that word surprisal, but not entropy, is a significant predictor . sentence length is also a predictor, which has been related to sentence complexity . |