Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection (C18-1)
Copied to clipboard
| Challenge: | idioms and metaphors are often studied in isolation, challenging the distinction . e.g., metaphorical concept mappings are ubiquitous in everyday life, thus they are ubiquitous . |
| Approach: | They propose to view the detection problem as a generalized non-literal language classification problem. |
| Outcome: | The proposed model improves on four metaphor and idiom detection tasks in two languages, English and German. |
Similar Papers
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
Copied to clipboard
| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification (2021.naacl-main)
Copied to clipboard
| Challenge: | Currently, most work on targeted sentiment analysis is focused on improving the overall results. |
| Approach: | They propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks to create English-language models that are more robust to linguistic phenomena. |
| Outcome: | The proposed method improves on negation and speculation datasets but there is room for improvement. |
A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to detect metaphor and hyperbole independently have not explored their relationship computationally. |
| Approach: | They propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously by annotating two hyperbolic datasets with metaphor labels. |
| Outcome: | The proposed framework improves state-of-the-art hyperbole detection by 12% over existing methods. |
What can we learn from Semantic Tagging? (D18-1)
Copied to clipboard
| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
| Outcome: | The proposed model improves for part-of-speech tagging, universal dependency parsing and natural language inference. |
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models (2022.findings-aacl)
Copied to clipboard
| Challenge: | Zero-shot cross-lingual transfer learning has been shown to be challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between languages, such as hate speech detection. |
| Approach: | They propose to train on multilingual auxiliary tasks to improve zero-shot transfer of hate speech detection models across languages by bringing a cross-lingual knowledge proxy to the task. |
| Outcome: | The proposed methods improve zero-shot transfer of hate speech detection models across languages and domains using multilingual auxiliary tasks fine-tuned. |
Multi-Task Networks with Universe, Group, and Task Feature Learning (P19-1)
Copied to clipboard
| Challenge: | In multi-task learning, multiple related tasks are learned together. |
| Approach: | They propose methods that take advantage of natural groupings of related tasks . they propose parallel and serial architectures that can learn different feature spaces . |
| Outcome: | The proposed methods improve performance on natural language understanding (NLU) tasks. |
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. |
| Approach: | They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks. |
| Outcome: | The proposed architecture does surprisingly well across a range of diverse tasks. |
Generalizable Cross-Lingual Cognitive Distortion Detection with Standardized Annotations and Multi-Task Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on cognitive distortion have limited generalizability and performance of models in large-scale and cross-linguistic contexts. |
| Approach: | They propose a multi-task learning model based on teacher student architecture solution which improves generalization performance. |
| Outcome: | The proposed model improves generalizability and interpretability of the proposed model. |
Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (D18-1)
Copied to clipboard
| Challenge: | In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem. |
| Approach: | They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task. |
| Outcome: | The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations. |
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
Copied to clipboard
| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
| Approach: | They propose to combine multi-task learning problems with multi-lingual Transformers to model zero-shot transfer across languages. |
| Outcome: | The proposed model can predict zero-shot transfer across languages with a multi-task learning problem with pretraining data in very few languages. |