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What is Your Article Based On? Inferring Fine-grained Provenance ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.458 Abstract: When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of \emph{claim provenance}, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of \emph{multiple} interacting claims, including how to capture \emph{fine-grained} information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel \emph{rank-aware cross attention} mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/j601-8q69
https://underline.io/lecture/25899-what-is-your-article-based-onquestion-inferring-fine-grained-provenance
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2
{Z}ero-shot {L}abel-Aware {E}vent {T}rigger and {A}rgument {C}lassification ...
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3
Coreference Reasoning in Machine Reading Comprehension ...
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4
Event-Centric Natural Language Processing ...
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5
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights ...
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Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary ...
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7
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition ...
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