Papers with semi-automatic approach
Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts (2024.findings-emnlp)
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| Challenge: | Existing methods for automating data generation with Large Language Models (LLMs) are difficult, and we propose a semi-automatic approach to generate dialogs with attributions. |
| Approach: | They propose a semi-automatic approach to generate dialog queries and responses with Large Language Models followed by human verification and identification of attribution spans. |
| Outcome: | The proposed approach improves the quality of the response generation and attribution quality of MISeD datasets while reducing time and effort. |
RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis (2026.findings-eacl)
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| Challenge: | Multimodal Large Language Models (MLLMs) provide no visibility into which parts of visual data informed their conclusions. |
| Approach: | They propose a semi-automatic approach to attribute reasoning process by highlighting regions in charts and graphs that justify model answers. |
| Outcome: | The proposed method improves attribution accuracy by up to 15 percentage points compared to baseline methods and achieves high semantic similarity with ground truth responses. |
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification. (2022.lrec-1)
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Rémi Uro, David Doukhan, Albert Rilliard, Laetitia Larcher, Anissa-Claire Adgharouamane, Marie Tahon, Antoine Laurent
| Challenge: | Existing methods for creating diachronic corpus of voices are based on speaker characteristics and require human intervention. |
| Approach: | They propose to use a semi-automatic pipeline to create a diachronic corpus of voices balanced for speaker’s age, gender and recording period, according to 32 categories. |
| Outcome: | The proposed method cut down on manual annotations by ten and provides high quality speech for most of the selected excerpts. |
“A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia (D19-1)
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| Challenge: | Vossian Antonomasia is a stylistic device which attributes a property to a person by naming another person as a reference point. |
| Approach: | They propose a method for the extraction of Vossian Antonomasias that works completely automatically . they use named entity recognition, distant supervision and a bi-directional LSTM . |
| Outcome: | The proposed method outperforms the only existing semi-automatic method for VA identification by more than 30 percentage points in precision. |