Span Extraction
Related Papers
A Simple and Effective Model for Answering Multi-span Questions
AbstractModels for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid …...
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AbstractThe most common metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency …...
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension
AbstractSpan extraction is an essential problem in machine reading comprehension. Most of the existing algorithms predict the start and end positions of an answer span in the given corresponding context …...
Enhanced Language Representation with Label Knowledge for Span Extraction
AbstractSpan extraction, aiming to extract text spans (such as words or phrases) from plain text, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance …...
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
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End-to-End Chinese Speaker Identification
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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
AbstractConversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing …...
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
AbstractFew-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, which ignores the information of entity …...
NLPeople at SemEval-2023 Task 2: A Staged Approach for Multilingual Named Entity Recognition
AbstractThe MultiCoNER II shared task aims at detecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful …...