Papers with multi-class classification
Systematic Evaluation of Predictive Fairness (2022.aacl-main)
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| Challenge: | Several methods have been proposed to mitigate bias in training on biased datasets. |
| Approach: | They propose to examine the effect of target class imbalance and stereotyping on model performance by analyzing binary classification, profession prediction and regression tasks. |
| Outcome: | The proposed methods show that data conditions have a strong influence on relative model performance. |
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
| Approach: | They propose to use emotional patterns identified by clinical practitioners to enhance the prediction capabilities of a mental illness detection model built using a deep neural network architecture. |
| Outcome: | The proposed method achieves a task-specific AUC higher than 0.90 . it compares multi-task learning with multi-channel convolutional neural network and multiple inputs to methods such as multi-class classification . |
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
| Approach: | They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model . |
| Outcome: | The proposed model achieves comparable performance with reported results in the literature. |
FastFit: Fast and Effective Few-Shot Text Classification with a Multitude of Classes (2024.naacl-demo)
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| Challenge: | Few-shot prompting of large language models (LLMs) via API calls presents a unique challenge when dealing with a multitude of classes that share similar semantic meanings. |
| Approach: | They present a Python package that integrates batch contrastive learning and token-level similarity score to provide fast few-shot classification. |
| Outcome: | The proposed method significantly improves multi-class classification speed and accuracy across English and Multilingual datasets. |
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text (D19-55)
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| Challenge: | Contemporary datasets on tobacco consumption focus on one of two topics, public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. |
| Approach: | They propose to use a dataset of 3144 tweets to analyze slang related to smoking and then use it to create a binary and multi-class classification mechanism. |
| Outcome: | The proposed method is able to identify a topic, a general mention or a more fine-grained classification based on the semantics of the tweets. |
Geolocation with Attention-Based Multitask Learning Models (D19-55)
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| Challenge: | predicting the location of a social media post requires discretization of the coordinates, but results in poor performance. |
| Approach: | They propose to combine two approaches to predict location using supervised models . they evaluate a multitask convolutional neural network that predicts both discrete locations and continuous coordinates . |
| Outcome: | The proposed model outperforms singletask models and prior work on one dataset and shows that correlation between labels and coordinates has a marked impact on the effectiveness of a regression task. |
Weakly supervised hierarchical multi-task classification of customer questions (2023.acl-industry)
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Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Sricharan Mukku, Manan Soni, Rashmi Patange
| Challenge: | Identifying granular and actionable topics from customer questions helps improve the overall customer experience. |
| Approach: | They propose a weakly supervised Hierarchical Multi-task Classification Framework to identify granular topics from customer questions . a clustering based taxonomy creation and data labeling module is used to create taxonomies and labelled data with minimal supervision. |
| Outcome: | The proposed model achieves 13% better accuracy over single-task classification frameworks . it can adapt to constantly evolving taxonomy without need of re-training . |
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
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| Challenge: | Existing studies have attempted to personalize models to improve performance on NLP tasks such as sentiment analysis but they did not estimate subjective input. |
| Approach: | They propose a method of modeling personal biases in word meanings with personalized word embeddings by solving a task on subjective text while regarding words used by different individuals as different words. |
| Outcome: | The proposed method improves sentiment analysis and target task with reviews retrieved from RateBeer. |
Punctuation as Native Language Interference (C18-1)
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| Challenge: | Numerous aspects of written language have been studied for native language identification (NLI) but its impact has not been studied. |
| Approach: | They propose to use punctuation marks as indicators of native language . they propose to apply them to native language identification . |
| Outcome: | The proposed methods support the hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, even when a high proficiency level in a non-native language is achieved. |
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
| Outcome: | The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively. |
Exploring BERT-Based Classification Models for Detecting Phobia Subtypes: A Novel Tweet Dataset and Comparative Analysis (2024.lrec-main)
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| Challenge: | Phobias are characterized by an intense and irrational fear of specific objects, situations, or activities despite there being no real risk or only a minor threat involved. |
| Approach: | They propose to use a dataset of 811,569 English tweets from user timelines spanning 102 phobia subtypes over six months to classify users into 65 specific phobias. |
| Outcome: | The proposed dataset includes 47,614 self-diagnosed phobia users and a high f1 score for binary classification and multi-class classification. |
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection (2020.acl-main)
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| Challenge: | Word embeddings, pre-trained language models, and deep learning methods are becoming effective for text classification. |
| Approach: | They propose a method for removing information from neural representations using null-space projection. |
| Outcome: | The proposed method mitigates bias in word embeddings and increases fairness in multi-class classification. |
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification (2023.findings-emnlp)
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| Challenge: | Recent prompt-based fine-tuning techniques have garnered considerable interest as a core technique for few-shot text classification tasks. |
| Approach: | They propose a prompt-based fine-tuning approach that reformulates the fine-uning objective to align with the Masked Language Modeling objective. |
| Outcome: | The proposed method has shown superior performance on five multi-class classification datasets. |