Papers with deep-learning models
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
Copied to clipboard
| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
| Approach: | They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model. |
| Outcome: | The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus . |
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks. |
| Approach: | They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks. |
| Outcome: | The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube . |
Locally Aggregated Feature Attribution on Natural Language Model Understanding (2022.naacl-main)
Copied to clipboard
| Challenge: | a growing popularity of deep-learning models makes model understanding more important . feature attribution methods have shown promising results in computer vision but are not trivial . |
| Approach: | They propose a gradient-based feature attribution method that smooths gradients by aggregating similar reference texts derived from language model embeddings. |
| Outcome: | The proposed method outperforms existing methods on public datasets and key words detection tasks. |
SentiArabic: A Sentiment Analyzer for Standard Arabic (L18-1)
Copied to clipboard
| Challenge: | Sentiment analysis is a process of applying computational approaches to identify attitudes, emotions and opinions in text, speech and visual data. |
| Approach: | They propose a sentiment analyzer that identifies the overall contextual polarity for Arabic text. |
| Outcome: | The proposed system achieves an F-score of 76.5% when evaluated on a blind test set. |
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT (2020.lrec-1)
Copied to clipboard
| Challenge: | Massive digital data processing can endanger personal data privacy . anonymisation involves removing or replacing sensitive information from data . |
| Approach: | They propose to use a BERT-based sequence labelling model to conduct an experiment on clinical datasets in Spanish. |
| Outcome: | The proposed model outperforms existing models on clinical datasets in Spanish and shows that it is highly competitive with other models. |