Papers with conditional language modeling
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
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| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural Machine Translation models are influenced by two types of context, source and target, but none explicitly evaluates relative contribution to generation decision. |
| Approach: | They propose to adopt a variant of Layerwise Relevance Propagation which evaluates relative contributions to the generation decision by a proportion of token influence. |
| Outcome: | The proposed model can evaluate the relative contribution of source and target to the generation decision by using a variant of Layerwise Relevance Propagation (LRP) |
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond (2020.acl-main)
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| Challenge: | Existing work has shown that the optimization of variational autoencoders suffers from the posterior collapse problem. |
| Approach: | They propose a variational autoencoder that couples a VAE model with a deterministic autoencoding model and improves the parameters via weight sharing and decoder signal matching. |
| Outcome: | The proposed model improves on benchmark datasets and improves diversity of dialogue generation. |
Generating Query Focused Summaries from Query-Free Resources (2021.acl-long)
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| Challenge: | Existing datasets are small for data-hungry neural architectures and are limited to evaluation purposes. |
| Approach: | They propose to decompose QFS into query modeling and conditional language modeling . they propose a Masked ROUGE Regression framework for evidence estimation and ranking . |
| Outcome: | The proposed model achieves state-of-the-art performance despite weak supervision. |