Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering (N19-2)
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| Challenge: | Existing methods for duplicate classification require manual review and assigning bugs to the correct teams. |
| Approach: | They propose a loss function that can detect duplicate bug reports and aggregate them into latent topics without supervision. |
| Outcome: | The proposed model outperforms state-of-the-art methods for duplicate classification on both cases and can learn meaningful latent clusters without supervision. |
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Neural Duplicate Question Detection without Labeled Training Data (D19-1)
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| Challenge: | Recent studies have used alternative methods to train neural models to duplicate question detection in community Question Answering forums. |
| Approach: | They propose two new methods for supervised question detection in community Question Answering forums . they propose weak supervision using title and body of question and automatic generation of duplicate questions . |
| Outcome: | The proposed methods can achieve better performance even without labeled data. |
Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)
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| Challenge: | Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer. |
| Approach: | They propose to use domain adaptation to detect duplicate questions in forums . they find that domain adaptation improves performance over multiple pairs of domains . |
| Outcome: | The proposed approach improves 5.6% over the best baseline across multiple pairs of domains. |
Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents (2021.naacl-main)
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| Challenge: | Recent work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers. |
| Approach: | They propose to use latent structured prediction loss and Transformer models to approach supervised clustering. |
| Outcome: | The proposed approach outperforms the state-of-the-art in recreating intents from public question corpora. |
Neural Topic Modeling based on Cycle Adversarial Training and Contrastive Learning (2023.findings-acl)
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| Challenge: | Neural topic models have been widely used to extract common topics across documents. |
| Approach: | They propose a framework to apply contrastive learning directly to the decoder . they propose 'self-supervised' contrastive loss to make the generator capture similar topic information . |
| Outcome: | The proposed framework outperforms baselines on four benchmark datasets. |
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)
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| Challenge: | Existing methods for event extraction use annotated event types but are expensive and time-consuming. |
| Approach: | They propose a semi-supervised approach to learning new event types using a masked contrastive loss. |
| Outcome: | The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
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| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)
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| Challenge: | Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features. |
| Approach: | They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. |
| Outcome: | The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora. |
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |