Learning Constraints for Structured Prediction Using Rectifier Networks (2020.acl-main)
Copied to clipboard
| Challenge: | Various natural language processing tasks require domain expertise to design good constraints. |
| Approach: | They propose a framework for learning constraints in a network of linear inequalities over the output variables. |
| Outcome: | The proposed framework can be used to learn constraints from data on natural language processing tasks. |
Similar Papers
Promptly Predicting Structures: The Return of Inference (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for structured prediction rely on large labeled datasets. Existing approaches for structured predictions require detailed annotation guidelines about the task, the label set, and the interactions between labels. |
| Approach: | They propose a framework for constructing zero- and few-shot linguistic structure predictors using structural constraints and combinatorial inferences. |
| Outcome: | The proposed framework can be extended to build zero- and few-shot label predictors on two structured prediction tasks and five datasets. |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
Copied to clipboard
| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
Distilling Knowledge for Search-based Structured Prediction (P18-1)
Copied to clipboard
| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
| Outcome: | The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines. |
Training Structured Prediction Energy Networks with Indirect Supervision (N18-2)
Copied to clipboard
| Challenge: | a new rank-based training method for structured prediction energy networks is proposed . structured prediction is important in many domains, including computer vision, computational biology and natural language processing. |
| Approach: | They propose a rank-based training method for structured prediction energy networks . they use a scoring function defined with domain knowledge to train the models . |
| Outcome: | The proposed method minimizes ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
Copied to clipboard
| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |
Autoregressive Structured Prediction with Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent years have seen a paradigm shift in NLP towards using pretrained language models for a wide range of tasks. |
| Approach: | They propose to model structures as sequences of actions in autoregressive manner with PLMs . their approach allows in-structure dependencies to be learned without any loss . |
| Outcome: | The proposed approach achieves state-of-the-art on all structured prediction tasks. |
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
Copied to clipboard
| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Mapping the Course for Prompt-based Structured Prediction (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models have demonstrated strong performance in a wide-range of language tasks without task-specific fine-tuning. |
| Approach: | They combine large language models with combinatorial inference to marry predictive power of LLMs with structural consistency provided by inference methods. |
| Outcome: | The proposed model incorporates symbolic inference to provide consistent and accurate predictions on challenging tasks. |
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to extract event temporal relations from text data are limited by hard constraints and large datasets. |
| Approach: | They propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge to improve the baseline neural network models. |
| Outcome: | The proposed framework improves baseline models with strong statistical significance on two widely used datasets in news and clinical domains. |