Papers with task-specific networks

2 papers
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)

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Challenge: In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages.
Approach: They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks.
Outcome: The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks.
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.

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