Papers with sampling procedure
Gradient-based Constrained Sampling from Language Models (2022.emnlp-main)
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| Challenge: | Large pretrained language models are successful at generating fluent text but are notoriously hard to controllably sample from. |
| Approach: | They propose a sampling procedure that combines the log-likelihood of the language model with arbitrary constraints in a single energy function and then generates samples in . non-autoregressive manner. |
| Outcome: | The proposed method improves on text generation with soft and hard constraints and keyword-guided generation. |
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation (2021.acl-long)
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| Challenge: | Experimental results show that enhancing the learning on uncertain monolingual sentences improves the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side. |
| Approach: | They propose to use monolingual data to augment model training with synthetic parallel data by selecting the most informative monolingual sentences to complement the parallel data. |
| Outcome: | The proposed approach improves the performance of natural language models by selecting the most informative monolingual sentences. |
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes (2023.emnlp-main)
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| Challenge: | Recent advances in deep generative models have succeeded in synthesizing human-like speech. |
| Approach: | They propose a text-to-speech model with a prosody diversifying module that considers perceptual diversity in each sample and among multiple samples. |
| Outcome: | The proposed model generates speech samples with more diversified prosody than baselines in the side-by-side comparison test considering the naturalness of speech at the same time. |
Bayesian Learning for Neural Dependency Parsing (N19-1)
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| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
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| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |