Papers with multi-task learning model
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing (2021.naacl-demos)
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| Challenge: | PhoNLP is a multi-task learning model for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. |
| Approach: | They propose a multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
| Outcome: | The proposed model outperforms a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
Neural Multi-Task Learning for Stance Prediction (D19-66)
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| Challenge: | Existing models for fact checking are limited in size due to limited data available . stance detection is a key component of fact checking for journalists and news agencies . |
| Approach: | They propose to use textual information from existing datasets to improve stance prediction. |
| Outcome: | The proposed model outperforms state-of-the-art systems on a public benchmark dataset by 6.0 and 14.4 points in weighting. |
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding (2023.eacl-main)
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| Challenge: | State-of-the-art entity tracking approaches either design complicated model architectures or rely on task-specific pre-training to achieve good results. |
| Approach: | They propose a multi-task learning-enabled entity tracking approach that utilizes knowledge gained from general domain tasks to improve entity tracking. |
| Outcome: | The proposed approach achieves state-of-the-art on two popular entity tracking datasets, even though it does not require any task-specific architecture design or pre-training. |
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions (2024.naacl-long)
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Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng, Roshan Sharma, Yosuke Kashiwagi, Emiru Tsunoo, Karen Livescu, Shinji Watanabe
| Challenge: | Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model’s behavior and surpassing performance of task-specific models. |
| Approach: | They adapt a pre-trained automatic speech recognition model to additional tasks using single-token task specifiers. |
| Outcome: | The proposed model can generalize to new datasets and languages for seen task types. |
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (2021.naacl-main)
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| Challenge: | Existing methods to relation extraction require labeled data, but labeling is difficult . Existing models cannot recognize rare instances that are never covered by training data . |
| Approach: | They propose a multi-task learning model that directly predicts unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. |
| Outcome: | The proposed model outperforms existing methods by 13.54% on two well-known datasets. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)
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| Challenge: | Existing automated generation of articles' characteristics is inconsistent if they are generated individually. |
| Approach: | They propose a multi-task learning model with a shared encoder and multiple decoders for each task. |
| Outcome: | The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines . |
Multi-Task Learning with Language Modeling for Question Generation (D19-1)
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| Challenge: | Existing work on answer-aware questions generates a sentence and answer span as input . previous work on QG was mainly tackled by rule-based approach and neural-based one . |
| Approach: | They propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. |
| Outcome: | The proposed model improves on SQuAD and MARCO datasets and human evaluation proves it. |
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection (2022.acl-long)
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| Challenge: | Existing ‘Stereotype Detection’ datasets adopt a diagnostic approach toward large PLMs. |
| Approach: | They propose a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance. |
| Outcome: | The proposed model achieves significant gains over baselines on hate speech detection, offensive language detection, misogyny detection, etc. |
Modeling Long Context for Task-Oriented Dialogue State Generation (2020.acl-main)
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
| Approach: | They propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the MultiWOZ 2.0 dataset. |
Generalizable Cross-Lingual Cognitive Distortion Detection with Standardized Annotations and Multi-Task Learning (2025.findings-acl)
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| 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. |