Papers with multi-task learning method
End-to-End Learning of Task-Oriented Dialogs (N18-4)
Copied to clipboard
| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
| Outcome: | The proposed system can track dialog state, interface with knowledge bases, and integrate structured query results into system responses to successfully complete task-oriented dialog. |
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)
Copied to clipboard
| Challenge: | Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models. |
| Approach: | They propose a multi-task learning method that integrates relational and relevance ranking tasks with target link prediction to improve ranking performance. |
| Outcome: | The proposed method improves ranking performance but still behind state-of-the-art models in Hits@k and Mean Rank metrics. |
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing CSRL parsers struggle to handle conversational structural information. |
| Approach: | They propose a conversational semantic role labeling task which explicitly encodes speaker dependent information and proposes a multi-task learning method to further improve the model. |
| Outcome: | The proposed model outperforms baselines on benchmark datasets on conversation-based tasks. |
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. |
Multi-Task Learning for Japanese Predicate Argument Structure Analysis (N19-1)
Copied to clipboard
| Challenge: | Recent work ignores event-nouns or builds a single model for solving both tasks . however, there are interactions between predicates and event-nons, making it difficult to target only predicate. |
| Approach: | They propose a multi-task learning method that targets event-nouns . their results improve performance of both PASA and ENASA tasks . |
| Outcome: | The proposed model improves both PASA and ENASA tasks compared to a single-task model . it is the first work to employ neural networks in ENASA . |
RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods focus on sentencelevel event extraction (SEE), but they are inconsistent with actual situations. |
| Approach: | They propose a document-level event extraction framework which can model relation dependencies by a relation-augmented Attention Transformer. |
| Outcome: | The proposed framework can achieve state-of-the-art performance on two public datasets. |
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch. |
| Approach: | They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks. |
| Outcome: | The proposed method significantly outperforms baseline models on translation tasks and handling the entities. |
BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to regularize task variance are unexplored in multi-task text classification. |
| Approach: | They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm. |
| Outcome: | The proposed method achieves state-of-the-art in multi-task text classification. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
Copied to clipboard
| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |