Papers with F1-Score
Handling Ontology Gaps in Semantic Parsing (2024.starsem-1)
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| Challenge: | Existing methods to detect hallucinations in closed-ontology models are limited by ontology gaps. |
| Approach: | They propose a framework for stimulating and analyzing NSP model hallucinations . they propose 'hallucination simulation framework' to detect hallucinosities in presence of ontology gaps . |
| Outcome: | The proposed framework improves the F1-Score and the IQ Pro benchmark datasets. |
Hengam: An Adversarially Trained Transformer for Persian Temporal Tagging (2022.aacl-main)
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| Challenge: | A wide array of natural language processing (NLP) applications relies on accurately identifying events and their respective occurrence times. |
| Approach: | They propose an adversarially trained transformer for Persian temporal tagging that can generalize over the HengamTagger’s rules. |
| Outcome: | The proposed tool outperforms state-of-the-art methods on a diverse and manually created dataset. |
Curras + Baladi: Towards a Levantine Corpus (2022.lrec-1)
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| Challenge: | The processing of the Arabic language is a complex field of research due to the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics. |
| Approach: | They propose to revise the Palestinian morphologically annotated corpus and a Lebanese corpus to bridge nuanced linguistic gaps between the two highly mutually intelligible dialects. |
| Outcome: | The revised corpus can be used as a more general Levantine corpus. |
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages (2024.acl-long)
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| Challenge: | Existing pre-trained language models are weak in addressing cross-lingual transfer tasks. |
| Approach: | They propose a method for initializing embeddings and choosing the right vocabulary size for cross-lingual systems. |
| Outcome: | The proposed method improves the F1-Score in several languages . |
Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (N18-1)
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| Challenge: | a recent study examined deception detection in several domains, including fake reviews, mock crime scenes, and opinions about topics such as abortion or the death penalty. |
| Approach: | They analyze linguistic features in truthful and deceptive interview dialogues . they also examine interviewer perceptions of deception, identifying characteristics of deceptives . |
| Outcome: | The proposed model outperforms human classifications using linguistic features and individual traits. |
R-GDA: Reflective Guidance Data Augmentation with Multi-Agent Feedback for Domain-Specific Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named Entity Recognition (NER) tasks require data augmentation due to the scarcity of annotated corpora. |
| Approach: | They propose a framework that introduces a multi-agent feedback loop to enhance augmentation quality. |
| Outcome: | The proposed framework improves on SciERC and NCBI-disease datasets and achieves low BERTScore in most cases. |
MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector (2024.findings-acl)
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Marta Costa-jussà, Mariano Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alexandre Mourachko, Christophe Ropers, Carleigh Wood
| Challenge: | Existing studies on text-based toxicity detection for other languages are limited, especially for languages other than English. |
| Approach: | They propose a multilingual audio-based toxicity classifier which covers 14 different linguistic families and a dataset of 20,000 audio utterances for English and Spanish. |
| Outcome: | The new classifier improves F1-Score by an average of 100% when compared to existing wordlist-based classifiers. |
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification (2022.lrec-1)
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| Challenge: | Existing disease surveillance systems use indicators to monitor official sources and unofficial sources. |
| Approach: | They propose a way to perform epidemiological document classification by enriching thematic features . they use a pre-trained biomedical language model with a novel approach . |
| Outcome: | The proposed method improves the classifier's ability to avoid false positive alerts on disease surveillance systems. |
Puntuguese: A Corpus of Puns in Portuguese with Micro-edits (2024.lrec-main)
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Marcio Lima Inacio, Gabriela Wick-Pedro, Renata Ramisch, Luís Espírito Santo, Xiomara S. Q. Chacon, Roney Santos, Rogério Sousa, Rafael Anchiêta, Hugo Goncalo Oliveira
| Challenge: | Existing corpus of punning humor in Portuguese is unfit for machine learning due to data leakage. |
| Approach: | They propose to use Puntuguese to create a corpus of punning humor in Portuguese that is significantly more difficult to recognize than the previous corpus. |
| Outcome: | The proposed corpus achieves an F1-Score of 68.9% and is significantly more difficult than the previous corpus. |