| 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. |
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DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)
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| Challenge: | Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text. |
| Approach: | They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora. |
| Outcome: | The proposed model performs state-of-the-art on 21 of 28 datasets. |
Prompting Language Models for Linguistic Structure (2023.acl-long)
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| Challenge: | Existing prompting methods can test this hypothesis on autoregressive PLMs. |
| Approach: | They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs. |
| Outcome: | The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks. |
Promptly Predicting Structures: The Return of Inference (2024.naacl-long)
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| 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. |
Mapping the Course for Prompt-based Structured Prediction (2026.eacl-long)
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| 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. |
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction (2020.findings-emnlp)
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| Challenge: | Recent studies have shown that pre-training contextualized encoders with language model objectives is effective for structured prediction. |
| Approach: | They propose a semi-supervised method for pre-training contextualized encoders with language model objectives. |
| Outcome: | The proposed method is effective on three typical structured prediction tasks in four languages. |
Debiasing Large Language Models with Structured Knowledge (2024.findings-acl)
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
| Approach: | They propose a method that utilizes structured knowledge to mitigate bias in LLMs . their method obviates the need for training from scratch, thus offering enhanced scalability . |
| Outcome: | The proposed method outperforms state-of-the-art (SOTA) baselines in the debiasing ability. |
Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
| Approach: | They propose a framework that learns to attack a structured prediction model with feedbacks from multiple reference models. |
| Outcome: | The proposed framework is able to attack state-of-the-art models and boost them with training . it is based on a sequence-to-sequence model with feedbacks from multiple reference models . |
Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (2024.findings-eacl)
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| Challenge: | Existing methods for text classification using autoregressive language models are limited . authors propose a novel technique for text classification using autoreregressives . |
| Approach: | They propose a two-step technique for text classification using autoregressive language models . they use a set of perplexity and log-likelihood based numeric features to elicit a text instance . |
| Outcome: | The proposed technique eliminates parameter updates in LMs and does not limit training examples . it is evaluated across 5 datasets and compares with multiple competent baselines . |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| 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. |
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL (2024.naacl-long)
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| Challenge: | Structured data is prevalent in tables, databases, and knowledge graphs, but there is a gap in our understanding of how these linearization-based methods handle structured data, which is inherently non-linear. |
| Approach: | They investigate the linear handling of structured data in encoder-decoder language models, specifically T5. |
| Outcome: | The proposed model can mimic human-designed processes such as schema linking and syntax prediction, and it can be compressed due to modality fusion redundancy. |