Papers with BOW
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)
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| Challenge: | Existing topic models rely on bag-of-words (BOW) representations to capture word order information. |
| Approach: | They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information. |
| Outcome: | The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents. |
Comparing Text Representations: A Theory-Driven Approach (2021.emnlp-main)
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| Challenge: | Recent advances in NLP have been made by learning representations that transform complex tasks into simple classification tasks. |
| Approach: | They propose a method to evaluate the compatibility between representations and tasks by fitting text features to specific characteristics of text datasets. |
| Outcome: | The proposed model provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task. |
Sound Signal Processing with Seq2Tree Network (L18-1)
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| Challenge: | Recent LSTM models have been used to model sequential data processing tasks because of their ability to preserve previous information weighted on distance. |
| Approach: | They propose to use a tree-structured tree-based neural network architecture to solve the problem of unbalanced connections between data units inside and outside semantic groups. |
| Outcome: | The proposed model outperforms the state-of-the-art Bidirectional LSTM model on a signal and noise separation task. |
Afaan Oromo Hate Speech Detection and Classification on Social Media (2022.lrec-1)
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| Challenge: | Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo. |
| Approach: | They develop a model to detect and classify Afaan Oromo hate speech on social media using different machine learning algorithms. |
| Outcome: | The proposed model outperforms existing models in gender, religion, race, and offensive speech on social media. |
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition (2020.acl-main)
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| Challenge: | Natural language inference (NLI) is an increasingly important task for natural language understanding . however, the ability of NLI models to make pragmatic inferences remains understudied . |
| Approach: | They use semi-automatically generated sentence pairs to evaluate whether NLI models make pragmatic inferences. |
| Outcome: | The proposed model trains on multiNLI and shows that it learns to draw pragmatic inferences. |