Papers with deep learning approach
Embedding WordNet Knowledge for Textual Entailment (C18-1)
Copied to clipboard
| Challenge: | Existing deep learning models for textual entailment do not require any feature engineering or linguistic analysis. |
| Approach: | They propose to embed WordNet-derived lexical entailment relations into specially-learned word vectors and incorporate them into a decomposable attention model for textual enlightment. |
| Outcome: | The proposed model significantly improves on the SICK and SNLI datasets. |
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (N18-1)
Copied to clipboard
| Challenge: | a recent shift towards expressive emotion representation models has hampered deep learning in sentiment analysis. |
| Approach: | They propose a multi-task learning problem to solve a language data bottleneck . they propose to use word emotion induction as an individual task to predict emotion . |
| Outcome: | The proposed model outperforms a wide range of other methods on 9 languages and 15 conditions. |
An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)
Copied to clipboard
Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages. |
| Approach: | They propose to annotate 12,000 tweets with a sexism detection scheme in France . they propose to use deep learning to detect if a message with sexist content is really s. |
| Outcome: | The proposed scheme detects sexist content and identifies if it is really sexism . the proposed scheme is the first of its kind in the u.s. |
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to train neural network-based models for code-mixing are limited due to language specificity of code-mixed text. |
| Approach: | They propose a deep learning approach to generate code-mixed text from English to multiple languages without any parallel data. |
| Outcome: | The proposed approach generates a code-mixed text from English to multiple languages without any parallel data. |
Celebrity Profiling (P19-1)
Copied to clipboard
| Challenge: | Using a corpus of 71,706 verified accounts, we construct a profile of a wide cross-section of local and global celebrities. |
| Approach: | They propose to use Twitter feeds of 71,706 verified accounts to build a corpus of celebrity profiles using Wikidata crawling. |
| Outcome: | The proposed corpus contains an average of 29,968 words per profile and up to 239 pieces of personal information. |
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations (N19-1)
Copied to clipboard
| Challenge: | Several methods have been proposed for automatic music style classification, but they are limited in two aspects. |
| Approach: | They propose a deep learning approach to automatically learn and exploit style correlations by reviewing music reviews on websites. |
| Outcome: | The proposed approach performs well in capturing style correlations. |
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
Copied to clipboard
| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
Fine-Tuning a Pre-Trained Wav2Vec2 Model for Automatic Speech Recognition- Experiments with De Zahrar Sproche (2024.lrec-main)
Copied to clipboard
| Challenge: | Developing semi-automatic methods of transcription and annotation based on small amounts of annotated data would free field linguists to focus on tasks that are linguistically and relationally significant during fieldwork. |
| Approach: | They propose to use a pre-trained model to tune a generic pre-trainer model to reduce the transcription workload of field linguists. |
| Outcome: | The proposed system reduces the transcription workload of field linguists by averaging a pre-trained model with a language-specific tuning. |