Papers with Transfer Learning
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models (C18-1)
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| Challenge: | Existing models for Named Entity Recognition (NER) are trained on data with the same NE label set, but they are not able to recognize previously unseen NE categories. |
| Approach: | They propose to use a sequence to sequence model for Named Entity Recognition (NER) and propose to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs. |
| Outcome: | The proposed model can recognize previously unseen NE categories while keeping the knowledge of previously seen categories. |
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)
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| Challenge: | Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP. |
| Approach: | They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks . |
| Outcome: | The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus . |
Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
| Outcome: | The proposed method can achieve 91% accuracy on a fake news dataset, reducing error by 14% compared to established baselines. |
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts (L18-1)
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| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
| Approach: | They propose a neural network model for part-of-speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums that uses character and word representations. |
| Outcome: | The proposed model is end-to-end and uses character and word representations . it is compared with existing models on social media in English, german, french, italian and spanish . |
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution (2020.lrec-1)
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| Challenge: | Negation is an important characteristic of language, and a major component of information extraction from text. |
| Approach: | They propose to use a popular transfer learning model to solve Negation Detection and Scope Resolution tasks in 3 datasets that have gained popularity over the years. |
| Outcome: | The proposed model outperforms existing systems on the BioScope Corpus, the Sherlock dataset and the SFU Review Corpus in scope resolution. |
TunArTTS: Tunisian Arabic Text-To-Speech Corpus (2024.lrec-main)
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| Challenge: | Historically, TTS relied on classical methods that proved expensive in terms of data storage and often resulted in robotic-sounding output known as concatenative speech. |
| Approach: | They propose to extract a mono-speaker speech corpus from an online dictionary and use it to develop end-to-end TTS systems for the Tunisian dialect. |
| Outcome: | The proposed system is based on two approaches: training from scratch and transfer learning. |