Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.
Code (1)
Tasks
Semantic Role LabelingSimilar Papers 제목 키워드 기반
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discou…
Common Sense ReasoningSemantic Role LabelingMulti-Task Learning for Joint Semantic Role and Proto-Role Labeling
We put forward an end-to-end multi-step machine learning model which jointly labels semantic roles and the proto-roles of Dowty (1991), given a sentence and the predicates therein. Our best architecture first learns argu…
Multi-Task LearningSentenceTransfer LearningIterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling
Semantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic arguments…
Semantic Role LabelingHigh-order Semantic Role Labeling
Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has …
Semantic Role LabelingVocal Bursts Intensity PredictionUnsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Semantic role labeling (SRL) is an NLP task involving the assignment of predicate arguments to types, called semantic roles. Though research on SRL has primarily focused on verbal predicates and many resources available …
DecoderSemantic Role LabelingSentence