Papers with supervised learning methods
Unsupervised Paraphasia Classification in Aphasic Speech (2020.acl-srw)
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| Challenge: | Aphasia is a speech and language disorder which results from brain damage resulting in word retrieval deficit (anomia) . supervised learning methods cant be properly utilized as there is no aphasic speech data. |
| Approach: | They propose an unsupervised method which can be implemented without the need for labeled paraphasia data. |
| Outcome: | The proposed method outperforms supervised learning methods and transfer learning approaches for English without labeled paraphasia data. |
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media (D19-62)
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| Challenge: | Existing computational studies on stress only focus on domains such as speech or Twitter . a corpus of social media text is used to identify stress . |
| Approach: | They propose a text corpus of lengthy social media data for detecting stress . they use 190K posts from five different categories of Reddit communities . |
| Outcome: | The proposed corpus of social media data can be used to identify stress . it includes 190K posts from five different categories of Reddit communities . |
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown excellent performance on various NLP tasks. |
| Approach: | They propose a method that integrates multiple demonstration users into one aggregated demonstration to improve sequential recommendation. |
| Outcome: | The proposed method outperforms state-of-the-art LLM-based sequential recommendation methods on three recommendation datasets. |
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) often suffers from insufficient labeled data when the number of annotations exceeds several tens of labels. |
| Approach: | They propose a model with a fine-to- coarse mapping matrix to leverage hierarchical structure explicitly. |
| Outcome: | The proposed model outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations. |
The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs (L18-1)
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| Challenge: | SSIX corpora provide annotated data for supervised learning methods . polarity annotation is performed on two financial microblog platforms . |
| Approach: | They propose three SSIX corpora for sentiment analysis which provide annotated data for supervised learning methods. |
| Outcome: | The proposed corpora are in English, German and Spanish. |
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network (2020.coling-main)
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| Challenge: | a few-shot text classification method is proposed to solve the few-sshot text problem . supervised learning methods require large corpus of labeled data, making them hindered in practical application. |
| Approach: | They propose a few-shot text classification method that takes advantage of advanced pre-trained language models to extract the semantic features of each document. |
| Outcome: | The proposed method achieves state-of-the-art on sentiment analysis and relation datasets. |
LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)
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| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs (2023.emnlp-main)
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| Challenge: | Named entity recognition datasets are notorious for their noisy nature due to annotation errors, inconsistencies, and subjective interpretations. |
| Approach: | They propose a method that considers NER as a constituency tree parsing problem and uses a tree-structured Conditional Random Fields with uncertainty evaluation for integration. |
| Outcome: | The proposed model exhibits superb performance even in extreme scenarios with 90% annotation noise. |