Papers with multi-task learning models
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions (2021.eacl-main)
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| Challenge: | Populist rhetoric has risen across the political sphere in recent years, but computational approaches to it have been scarce. |
| Approach: | They propose a dataset of 6861 reddit comments annotated for populist attitudes and a set of multi-task learning models that leverage emotion and group identification as auxiliary tasks. |
| Outcome: | The proposed models leverage emotion and group identification as auxiliary tasks to model populist rhetoric tasks. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
All-in-one: Multi-task Learning for Rumour Verification (C18-1)
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| Challenge: | Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components . |
| Approach: | They propose a multi-task learning approach that allows joint training of main and auxiliary tasks, improving the performance of rumour verification. |
| Outcome: | The proposed approach improves the performance of rumour verification by combining main and auxiliary tasks into one pipeline. |
How to Parse a Creole: When Martinican Creole Meets French (2022.coling-1)
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| Challenge: | a lack of annotated gold standard data is a major challenge for underresourced languages. |
| Approach: | They propose to use a French treebank to develop a dependency parser for Martinican Creole. |
| Outcome: | The proposed model is based on a French treebank and has 80 Martinican Creole sentences. |
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments (2020.emnlp-main)
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| Challenge: | Existing work on dialect prediction is limited to coarse-grained varieties . a new language model, MARBERT, can predict micro-dialects with 9.9% F1, 76 better than a majority class baseline. |
| Approach: | They propose a new task of Micro-Dialect Identification (MDI) that can predict a fine-grained variety given a single message. |
| Outcome: | The proposed model predicts micro-dialects with 9.9% F1, 76 better than a majority class baseline. |
A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis (2020.emnlp-main)
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| Challenge: | Sentiment analysis is an increasingly popular natural language processing task in academia and industry. |
| Approach: | They propose to use category name encoding network to weaken catastrophic forgetting problem . they set both encoder and decoder shared among all categories to weaker the catastrophic forgetting problem a . |
| Outcome: | The proposed model achieves state-of-the-art on two (T)ACSA benchmark datasets. |