Papers with Gibbs sampling
Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)
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| Challenge: | Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles. |
| Approach: | They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments . |
| Outcome: | The proposed model can achieve comparable results to existing methods on two widely-used datasets. |
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
Analyzing Bayesian Crosslingual Transfer in Topic Models (N19-1)
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| Challenge: | a theoretical analysis of crosslingual transfer in probabilistic topic models is presented . we use Gibbs sampling to quantify the loss of knowledge across languages . |
| Approach: | They propose a method to quantify the loss of knowledge across languages during crosslingual transfer in probabilistic topic models. |
| Outcome: | The proposed model quantifies the loss of knowledge across languages during this process . it is validated on a diverse set of five languages and discusses best practices for data collection and model design . |
Evaluating Topic Quality with Posterior Variability (D19-1)
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| Challenge: | Probabilistic topic models such as latent Dirichlet allocation (LDA) are widely used for NLP tasks which require the extraction of latent themes. |
| Approach: | They propose to measure topic quality using the variability of posterior distributions of probabilistic topic models. |
| Outcome: | The proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. |
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors (2020.emnlp-main)
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| Challenge: | Recent advances in NLP focus on simple approaches to model the output label space . graphical models are often limited to (heuristic) greedy search and its variants . |
| Approach: | They propose an approach for efficiently training and decoding hybrids of graphical and graphical models based on Gibbs sampling. |
| Outcome: | The proposed approach improves on Dutch and Dutch with graphical models . the proposed model improves over a strong baseline on three languages . |
GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)
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| Challenge: | Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. |
| Approach: | They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description. |
| Outcome: | The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution. |