Timezone: »
In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into dependency trees or abstract meaning representation (AMR) graphs. On the other hand, ensemble methods combine predictions from multiple models to create a new one that is more robust and accurate than individual predictions. In the literature, there are many ensembling techniques proposed for classification or regression problems, however, ensemble graph prediction has not been studied thoroughly. In this work, we formalize this problem as mining the largest graph that is the most supported by a collection of graph predictions. As the problem is NP-Hard, we propose an efficient heuristic algorithm to approximate the optimal solution. To validate our approach, we carried out experiments in AMR parsing problems. The experimental results demonstrate that the proposed approach can combine the strength of state-of-the-art AMR parsers to create new predictions that are more accurate than any individual models in five standard benchmark datasets.
Author Information
Thanh Lam Hoang (IBM Research)
Gabriele Picco (IBM)
Yufang Hou (Institute for Computational Linguistics, Heidelberg University, Heidelberg University)
Young-Suk Lee (IBM, International Business Machines)
Lam Nguyen (IBM Research, Thomas J. Watson Research Center)
Dzung Phan (IBM Research, T. J. Watson Research Center)
Vanessa Lopez (IBM Research Europe)
Ramon Fernandez Astudillo (IBM Research)
More from the Same Authors
-
2022 : c-MBA: Adversarial Attack for Cooperative MARL Using Learned Dynamics Model »
Nhan H Pham · Lam Nguyen · Jie Chen · Thanh Lam Hoang · Subhro Das · Lily Weng -
2021 Workshop: New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership »
Nghia Hoang · Lam Nguyen · Pin-Yu Chen · Tsui-Wei Weng · Sara Magliacane · Bryan Kian Hsiang Low · Anoop Deoras -
2021 Poster: On the Equivalence between Neural Network and Support Vector Machine »
Yilan Chen · Wei Huang · Lam Nguyen · Tsui-Wei Weng -
2021 Poster: FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization »
Quoc Tran Dinh · Nhan H Pham · Dzung Phan · Lam Nguyen -
2021 Poster: Cardinality-Regularized Hawkes-Granger Model »
Tsuyoshi Ide · Georgios Kollias · Dzung Phan · Naoki Abe -
2020 Poster: Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function »
Quoc Tran Dinh · Deyi Liu · Lam Nguyen -
2020 Poster: A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees »
Haoran Zhu · Pavankumar Murali · Dzung Phan · Lam Nguyen · Jayant Kalagnanam -
2019 Poster: Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD »
Ha Nguyen · Lam Nguyen · Marten van Dijk