Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension
The current Machine Reading Comprehension (MRC) model has poor interpretability. Interpretable semantic features can enhancethe interpretability of the model. Semantic role labeling (SRL) captures predicate-argument relations, such as "who did what to whom," which are critical to comprehension and interpretation. To enhance the interpretability of the model, we propose the semantic role relation table, which represents the semantic relation of the sentence itself and the semantic relations among sentences. We use the name of entities to integrate into the semantic role relation table to establish the semantic relation between sentences. This paper makes the fi rst attempt to utilize contextual semantic's explicit relation to the recognition supporting fact of reading comprehension. We have established nine semantic relationtables between target sentence, question, and article. Then we take each semantic relationship table's overall semantic role relevance and each semantic role relevance as important judgment information. Detailed experiments on HotpotQA, a challenging multi-hop MRC data set, our method achieves better performance. With few training data sets, the model performance is still stable.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine Reading ComprehensionReading ComprehensionRelationSemantic Role LabelingSentenceSimilar Papers 제목 키워드 기반
Effective QA-driven Annotation of Predicate-Argument Relations Across Languages
Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such semantic structures requires costly an…
Semantic Role LabelingInterpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretab…
BIG-bench Machine LearningInterpretable Machine LearningRelationExamining Interpretable Feature Relationships in Deep Networks for Action recognition
A number of recent methods to understand neural networks have focused on quantifying the role of individual features. One such method, NetDissect identifies interpretable features of a model using the Broden dataset of …
Action RecognitionTransitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Recent advances in fine-grained representation learning leverage local-to-global (emergent) relationships for achieving state-of-the-art results. The relational representations relied upon by such methods, however, are a…
Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptu…
Interpretable Machine Learning