Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)
Copied to clipboard
Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael Sailer, Elisabeth Bauer, Martin R. Fischer, Frank Fischer, Iryna Gurevych
| Challenge: | Existing deep learning methods require large amounts of training data to achieve reasonable performance. |
| Approach: | They propose to generate automatic annotation suggestions for a discourse-level sequence labelling task that requires extensive domain expertise. |
| Outcome: | The proposed model improves with newly annotated texts while introducing no biases. |
Similar Papers
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
Copied to clipboard
| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
A Fully Automated Pipeline for Conversational Discourse Annotation: Tree Scheme Generation and Labeling with Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have shown promise in automating discourse annotation for conversations. |
| Approach: | They propose a pipeline that uses large language models to construct and perform annotations using speech functions and the Switchboard-DAMSL taxonomies. |
| Outcome: | The proposed pipeline outperforms existing tree annotation schemes and can match or surpass human annotations while significantly reducing time required for annotation. |
Corpus Considerations for Annotator Modeling and Scaling (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions . |
| Approach: | They propose a composite embedding approach to investigate annotator modeling techniques . they show that the commonly used user token model consistently outperforms more complex models . |
| Outcome: | The proposed model outperforms more complex models on a given dataset. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown impressive performance in many annotation tasks, including subjective tasks common in content moderation and text analysis in the social sciences. |
| Approach: | They propose to give crowdworkers LLM-generated annotation suggestions to "review" LLMs for subjective tasks can impact model performance and analysis downstream . |
| Outcome: | The proposed approach improves self-reported confidence in annotators and models . it also significantly improves model performance by analyzing human-approved datasets. |
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction (N19-1)
Copied to clipboard
| Challenge: | Modern NLP systems require high-quality annotations, but experts are expensive and lay annotators may not have the knowledge to provide high- quality annotations. |
| Approach: | They propose to directly model instance difficulty to improve model performance and to route instances to appropriate annotators. |
| Outcome: | The proposed model improves performance on a biomedical information extraction task using expert and lay annotations. |
Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing applications of large language models to IE can be categorized into two lines: prompt engineering-based approaches and instruction-tuning open-weight LLMs. |
| Approach: | They propose to use annotation guidelines to teach large language models for event extraction . they use textual descriptions of event types and arguments to train the models . |
| Outcome: | The proposed approach improves cross-schema generalization and low-frequency event-type performance when there is a decent amount of training data. |
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation (D19-59)
Copied to clipboard
| Challenge: | Recent advances in machine reading and listening comprehension involve the annotation of long texts. |
| Approach: | They propose a way to perform a sentence-by-sentence annotation task with crowd annotators. |
| Outcome: | The proposed approach can be used to identify claims in a debate speech. |
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)
Copied to clipboard
| Challenge: | State-of-the-art NLP models are expensive and inefficient for event annotation. |
| Approach: | They propose to integrate LLMs into a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. |
| Outcome: | The proposed workflow integrates LLMs to alleviate human labor in a holistic pipeline. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
Copied to clipboard
| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |