Papers with NN models
An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints (2024.acl-srw)
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| Challenge: | Existing work on NN models with output constraints has not been able to categorize them in a unified manner. |
| Approach: | They propose new algorithms to integrate the information of main task and constraint injection . they use the H-score as a metric for considering main task metric and constrain infringement simultaneously . |
| Outcome: | The proposed algorithms integrate the information of main task and constraint injection, inspired by continual-learning algorithms. |
Scaling Hidden Markov Language Models (2020.emnlp-main)
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| Challenge: | Hidden Markov models are a fundamental tool for sequence modeling that separates the hidden state from the emission structure. |
| Approach: | They propose methods for scaling hidden Markov models to massive state spaces while maintaining efficient exact inference and effective regularization. |
| Outcome: | The proposed methods are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models. |
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment (L18-1)
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| Challenge: | a method to improve the quality of training data is needed . annotation errors of dialog act corpus mislead learning results of Bayesian network . |
| Approach: | They propose to introduce a null hypothesis for predictability of textual entailment labels and test it using a Naive Bayes model. |
| Outcome: | The proposed method does not reject the null hypothesis, but it improves on the existing models. |
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data (2020.acl-main)
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| Challenge: | Recent studies have shown that simpler, properly tuned models are at least competitive across NLP tasks. |
| Approach: | They propose to use a table-to-text and neural question generation tasks to generate text from structured and unstructured data. |
| Outcome: | The proposed task generates biographies based on Wikipedia infoboxes . the proposed model can achieve the state of the art in both tasks . |
Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios (2022.lrec-1)
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| Challenge: | Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. |
| Approach: | They propose to incorporate a widely-used psycholinguistic lexicon into NN models to improve human trait and behavior analysis in low resource scenarios. |
| Outcome: | The proposed methods perform significantly better than baselines that use only LIWC or NN-based feature learning methods. |