Papers with machine learning approach
Computational Ad Hominem Detection (P19-2)
Copied to clipboard
| Challenge: | ad hominem attacks are introduced in debates as an easy win, but their impact on argumentation is limited . a machine learning approach to detect the personal attack is insufficient, we show . |
| Approach: | They propose a machine learning approach that detects ad hominem attacks using social media data . they propose TF-IDF approaches that are insufficient to detect the personal attack . |
| Outcome: | The proposed method has a recall of 80% for a social media data source. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
Copied to clipboard
| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
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. |
Learning the Human Judgment for the Automatic Evaluation of Chatbot (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing evaluation methods for dialogue systems rely on human judges to label quality of generated text. |
| Approach: | They propose a machine learning approach to reduce the effort of human evaluation by learning the human judgment on comparing two generative dialogue systems. |
| Outcome: | The proposed method reduces the effort of human evaluation by learning which generative models is better in each dialog context. |
Categorizing and Inferring the Relationship between the Text and Image of Twitter Posts (P19-1)
Copied to clipboard
| Challenge: | Social media posts often contain images to provide content, provide context, or express feelings. |
| Approach: | They build and release a dataset of image tweets annotated with four different classes which express whether the text or the image provides additional information to the other modality. |
| Outcome: | The proposed method can be used in several downstream applications including pre-training image tagging models and collecting distantly supervised data for image captioning. |
Model Selection for Cross-lingual Transfer (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. |
| Approach: | They propose a machine learning approach that uses the fine-tuned model’s internal representations to predict its cross-lingual capabilities. |
| Outcome: | The proposed model selects better than English validation data across twenty five languages, including eight low-resource languages, and often achieves comparable results to model selection using target language development data. |
Multimodal and Multilingual Laughter Detection in Stand-Up Comedy Videos (2024.lrec-main)
Copied to clipboard
| Challenge: | Using TED talks, we use laughter detection software to capture humor in the sitcom genre. |
| Approach: | They develop a multimodal multilingual dataset in Russian and English with a particular emphasis on laughter detection techniques. |
| Outcome: | The proposed model outperforms peak detection and machine learning, while the latter shows promise and warrants further study. |
Lemmatization of Polish Multi-word Expressions (2025.emnlp-main)
Copied to clipboard
| Challenge: | PolEval task is complicated by linguistic irregularities and historical factors. |
| Approach: | They propose to use fine-tuned plT5 and mT5 models to improve lemmatization of multi-word expressions and proper names in Polish instead of rule-based methods. |
| Outcome: | The proposed model achieved 86.23% AccCS, 89.43% accCI and 88.79% on a gold-standard dataset derived from Wikipedia . |