| Challenge: | Existing highlight-based explanations focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. |
| Approach: | They propose a multi-annotator dataset of human span interaction explanations for NLU and FC. |
| Outcome: | The proposed method compares human reasoning processes to those of a fine-tuned large language model. |
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Semantic Span Annotation: An Exploratory Study of LLM Annotation (2026.acl-srw)
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| Challenge: | Structured span extraction research is siloed by context length, annotation task, and domain . Identifying a span within a natural language text and affixing it with a semantic label has been considered a core task in NLP . |
| Approach: | They propose a framework for structured span annotation that integrates five datasets under a common JSONL format with character-level offsets. |
| Outcome: | The proposed framework can generalize across four domains under three prompting configurations. |
Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework (2025.naacl-long)
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| Challenge: | Input feature explanations reveal how a model makes decisions based on a specific input. |
| Approach: | They propose a framework that facilitates an automated comparison between highlight and interactive explanations comprised of four diagnostic properties. |
| Outcome: | The proposed framework compares highlight and interactive explanations across two datasets and two models and shows that interactive span explanations outperform other explanation types across most diagnostic properties. |
Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
| Approach: | They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module. |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm. |
An Empirical Study on Finding Spans (2022.emnlp-main)
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| Challenge: | Various information extraction tasks require a span finding component, which either directly yields the output or serves as an essential component of downstream linking. |
| Approach: | They propose methods for span finding, the selection of consecutive tokens in text for some downstream tasks. |
| Outcome: | The proposed methods perform better on masked language models and pre-trained encoders than on encoder-decoder models. |
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models (2022.emnlp-main)
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| Challenge: | Current models learn from annotation artefacts and dataset biases, but it is unclear to what extent they are learning the task of NLI. |
| Approach: | They propose a logical reasoning framework that allows models to learn from annotation artefacts and dataset biases. |
| Outcome: | The proposed model outperforms humans on in-distribution test sets without using span labels . the model is more robust in a reduced data setting, and out-of-disturbance performance is improved . |
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations (2021.findings-emnlp)
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| Challenge: | Experiments show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods. |
| Approach: | They propose to extract and then normalise PICO information from clinical trial articles and use crowdsourced sentence-level annotations to detect spans. |
| Outcome: | The proposed method achieves much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. |
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (P18-2)
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| Challenge: | Recent models that use gold predicates only use a single predicate at a time. |
| Approach: | They propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. |
| Outcome: | The proposed model can model overlapping spans across different predicates in the same output structure without gold predicate predications. |
Improving Span Representation by Efficient Span-Level Attention (2023.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality span representations are limited by subset of tokens . span-span interactions should play an important role in span encoding, authors argue . |
| Approach: | They propose to introduce span-span interactions and more comprehensive span-token interactions to improve span representations. |
| Outcome: | The proposed model outperforms baseline models on span-related tasks and shows superior performance. |
Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)
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| Challenge: | a large number of natural language processing tasks are generated with specially designed architectures. |
| Approach: | They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning . |
| Outcome: | The proposed model performs comparable to state-of-the-art models on 10 tasks . it also shows that it can analyze differences and similarities in how the model handles different tasks compared to other models . |