Papers with supervised approach
Bootstrapping Neural Relation and Explanation Classifiers (2023.acl-short)
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| Challenge: | supervised approaches that use only rules to explain the outputs of the relation classifier are data hungry and expensive to obtain. |
| Approach: | They propose a method that self trains (or bootstraps) neural relation and explanation classifiers by iterating the outputs into rules and applying them to unlabeled text to produce new annotations. |
| Outcome: | The proposed method outperforms the rule-based model on the TACRED dataset by 15 F1 points and performs comparatively with the prompt-based approach without an additional natural language inference component. |
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets (P18-3)
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| Challenge: | Suicidal ideation on social media websites is associated with higher suicide rates . suicide is the second leading cause of death among 15-29-year-olds . |
| Approach: | They propose a supervised method for detecting suicidal ideation in tweets using a dataset of manually annotated tweets. |
| Outcome: | The proposed method is compared against four baselines to validate its utility. |
Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning (2024.findings-eacl)
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| Challenge: | Existing NLP methods treat morality as binary, ranging from right to wrong. |
| Approach: | They propose to build a pluralist moral sentence embedding space using contrastive learning methods to examine relationships among moral elements. |
| Outcome: | The proposed method shows that pluralism can be captured in an embedding space. |
A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
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Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
| Challenge: | Existing methods to segment unformatted text and transcripts explicitly train to predict segment boundaries, but they fail to provide a large annotated dataset. |
| Approach: | They propose a method to generate hierarchical segmentation structures based on Wikipedia annotations by using a neural conditional random field. |
| Outcome: | The proposed method outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms. |
Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (2020.coling-main)
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| Challenge: | Misinformation spreads across media, community, and knowledge graphs in the Web by human agents and information extraction algorithms. |
| Approach: | They propose a rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble. |
| Outcome: | The proposed approach outperforms the state-of-the-art unsupervised approaches by up to 0.12 AUC-ROC and even outperfies the supervised approach by up 0.05 AUC. |
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records (2024.emnlp-main)
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| Challenge: | State-of-the-art explainability methods rely on human annotations, which are costly. |
| Approach: | They propose an approach to produce plausible and faithful explanations without annotations . they use adversarial robustness training to improve plausibility and AttInGrad . |
| Outcome: | The proposed method produces plausible explanations without human annotations on a medical coding task. |
Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
| Outcome: | The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (2020.emnlp-main)
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| Challenge: | a new study suggests a minimally supervised approach for identifying nuanced political frames in news articles on politically divisive topics. |
| Approach: | They propose a minimally supervised approach for identifying nuanced policy frames in news coverage of politically divisive topics. |
| Outcome: | The proposed subframes can capture differences in political ideology better . the proposed frameworks were tested on immigration, gun control and abortion topics . |
Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)
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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun
| Challenge: | Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain. |
| Approach: | They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task . |
| Outcome: | The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision. |