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cs.CL2021
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten +1
State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019). Such heuristics include lexical overlap with the training…
cs.CL2021
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
Clément Rebuffel, Thomas Scialom, Laure Soulier +5
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.…
cs.CL2021
Controlling Hallucinations at Word Level in Data-to-Text Generation
Clément Rebuffel, Marco Roberti, Laure Soulier +3
Data-to-Text Generation (DTG) is a subfield of Natural Language Generation aiming at transcribing structured data in natural language descriptions. The field has been recently boos…