A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.
Code (0)
등록된 구현이 없습니다.
Tasks
Discourse ParsingSimilar Papers 제목 키워드 기반
Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain
Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating…
Chemical ProcessMulti-Task LearningWhat happens before and after: Multi-Event Commonsense in Event Coreference Resolution
Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. How…
coreference-resolutionCoreference ResolutionEvent Coreference ResolutionSentenceEnd-to-end Deep Reinforcement Learning Based Coreference Resolution
Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence …
coreference-resolutionCoreference ResolutionDeep Reinforcement Learningreinforcement-learning+2Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. We explic…
coreference-resolutionCoreference ResolutionEvent Coreference ResolutionQuestion AnsweringResolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriente…
Clusteringcoreference-resolutionCoreference ResolutionEvent Coreference Resolution+1