Emotion Classification in a Resource Constrained Language Using Transformer-based Approach (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods to classify Bengali text into six basic emotions are infancy for resource-constrained languages like English, Arabic, Chinese and French. |
| Approach: | They propose a transformer-based technique to classify Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. |
| Outcome: | The proposed technique outperforms all other techniques by achieving highest weighted f_1-score on the test data. |
Similar Papers
The MERSA Dataset and a Transformer-Based Approach for Speech Emotion Recognition (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models for speech emotion recognition lack a comprehensive dataset to design accurate models. |
| Approach: | They propose to use a multimodal dataset to build a model that integrates pre-trained wav2vec 2.0 and BERT to learn hidden representations from fused representations of speech and text. |
| Outcome: | The proposed model predicts emotions on dimensions of arousal, valence, and dominance . it achieved competitive results on the MSP-PODCAST dataset . |
An Analysis of Annotated Corpora for Emotion Classification in Text (C18-1)
Copied to clipboard
| Challenge: | Several datasets have been annotated and published for classification of emotions. |
| Approach: | They aggregated emotion corpora in a common file format with a shared annotation schema . they perform cross-corpus classification experiments to gain insight and a better understanding of differences . |
| Outcome: | The proposed model can be trained on a subset of corpora, but not on all corporata. |
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System (2020.coling-main)
Copied to clipboard
| Challenge: | In smart speakers and conversational robots, the demand for expressive speech synthesis has increased. |
| Approach: | They propose to annotate a news dataset with emotion labels for each sentence and to evaluate its effectiveness using the constructed dataset. |
| Outcome: | The proposed method improves the performance of the proposed model by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework. |
EmoNoBa: A Dataset for Analyzing Fine-Grained Emotions on Noisy Bangla Texts (2022.aacl-short)
Copied to clipboard
| Challenge: | EmoNoBa is a dataset for fine-grained emotion detection on Bangla text . it is based on 22698 comments from social media sites on 12 domains . |
| Approach: | They propose a manually annotated dataset of 22,698 Bangla comments from social media sites on 12 different domains to use for fine-grained emotion detection. |
| Outcome: | The proposed dataset of 22,698 public comments on 12 domains shows that hand-crafted features perform better than neural networks and pre-trained language models. |
BengaliLCP: A Dataset for Lexical Complexity Prediction in the Bengali Texts (2024.lrec-main)
Copied to clipboard
| Challenge: | Lexical Complexity Prediction (LCP) is a task for predicting the complexity score of a word or phrase based on its context. |
| Approach: | They propose a deep neural approach to predict lexical complexity of Bengali tokens using an annotated dataset. |
| Outcome: | The proposed neural approach outperforms state-of-the-art models for Bengali language. |
Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in South Asia – Current Stage and Challenges (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a survey examines the current efforts and challenges of NLP models for South Asian languages . there are more than 650 languages in South Asia, but many have very limited computational resources or are missing from existing models. |
| Approach: | a survey examines efforts and challenges of NLP for South Asian languages . they focus on transformer-based models such as BERT, T5, & GPT . findings highlight substantial issues, including missing data in critical domains . |
| Outcome: | The findings highlight significant issues, including missing data in critical domains . the survey aims to raise awareness within the NLP community for more targeted data curation . |
Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text (N18-4)
Copied to clipboard
| Challenge: | Emotion Prediction is a natural language processing task dealing with detection and classification of emotions in monolingual and bilingual texts. |
| Approach: | They propose a machine learning system which uses various machine learning techniques to detect emotion associated with tweets. |
| Outcome: | The proposed system uses various machine learning techniques to detect emotion associated with the text. |
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that results from high-resource languages cannot be easily transferred to realistic, low-resourced scenarios. |
| Approach: | They analyse performance of multilingual transformer models using available resources for Hausa, isiXhosa and NER and topic classification. |
| Outcome: | The proposed models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. |
Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on emotion prediction for resource-rich languages has focused on code-mixed social media corpus but not on Kannada-English code-mixed Twitter data. |
| Approach: | They analyze Kannada-English code-mixed Twitter corpus annotated with their respective ‘Emotion’ for each tweet. |
| Outcome: | The proposed model based on Kannada-English code-mixed Twitter corpus yielded an accuracy of 30% and 32% respectively. |