Papers with f-score
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)
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| Challenge: | Existing methods that focus on a single tweet as input are likely to yield high false positive and negative rates. |
| Approach: | They propose a model that leverages intra-user and inter-user representation learning to improve hate speech detection on Twitter by suppressing the noise in a single Tweet. |
| Outcome: | The proposed model significantly improves the f-score of a strong bidirectional LSTM model by 10.1%. |
Neural Ranking Models for Temporal Dependency Structure Parsing (D18-1)
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| Challenge: | a new neural temporal dependency parser is being developed for news reports and narrative stories . a similar system is used for other NLP applications such as timeline construction . |
| Approach: | They build a neural temporal dependency parser that parses time expressions and events in a text . their results shed light on the nature of temporal relation structures in different domains . |
| Outcome: | The proposed model beats baselines on news reports and narrative stories on two data domains. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
Implementation and Evaluation of an LFG-based Parser for Wolof (2020.lrec-1)
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| Challenge: | a parsing system for Wolof is developed based on the Lexical Functional Grammar (LFG) system provides detailed syntactic analysis essential for the further development of NLP applications. |
| Approach: | They propose a parsing system for Wolof based on the Lexical Functional Grammar (LFG) system uses finite-state transducers for word tokenization and morphological analysis . |
| Outcome: | The proposed system achieves 67.2% recall, 92.8% precision and an f-score of 77.9%. |
Logic-driven Indirect Supervision: An Application to Crisis Counseling (2023.acl-long)
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Mattia Medina Grespan, Meghan Broadbent, Xinyao Zhang, Katherine Axford, Brent Kious, Zac Imel, Vivek Srikumar
| Challenge: | Text-based crisis counseling services are increasingly adopted by people seeking confidential mental health support. |
| Approach: | They propose an inexpensive method that exploits declaratively stated structural dependencies between both levels of annotation to improve utterance modeling. |
| Outcome: | The proposed method improves utterance modeling by 3.5% over a strong multitask baseline. |
Geographically-Informed Language Identification (2024.lrec-main)
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| Challenge: | a paper develops a method to identify languages based on geographic origin of text . the model is based in regions where languages are widely spoken and may occur anywhere . |
| Approach: | They propose to incorporate geographic information into a language identification model to ensure coverage of linguae francae regardless of location. |
| Outcome: | The proposed model includes 31 widely-spoken international languages . the model improves on social media data and improves performance on 916 languages compared to baseline models . |