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SUMMARY:Measuring Factuality in Text Generation: When Language Models Are 
 Twisting the Facts - Roee Aharoni\, Google Research
DTSTART:20211028T100000Z
DTEND:20211028T110000Z
UID:TALK164572@talks.cam.ac.uk
CONTACT:Marinela Parovic
DESCRIPTION:Text generation is at the core of many NLP tasks like question
  answering\, dialog generation\, machine translation or text summarization
 . While current text generation models produce text that seems fluent and 
 informative\, their outputs often contain factual inconsistencies with res
 pect to the inputs they rely on (a.k.a. "hallucinations")\, making it hard
  to deploy such models in real-world applications.\n\nIn this talk I will 
 present two of our recent works tackling those issues. First\, I will desc
 ribe KOBE (Gekhman et al.\, EMNLP Findings 2020)\, a knowledge-based appro
 ach for evaluating the quality of machine translation models\, which uses 
 multilingual entity resolution instead of human reference translations. I 
 will then present Q^2 (Honovich et al.\, EMNLP 2021)\, an automatic evalua
 tion metric that combines question generation\, question answering and nat
 ural language inference to validate the outputs of dialogue generation mod
 els.
LOCATION:https://cam-ac-uk.zoom.us/j/97599459216?pwd=QTRsOWZCOXRTREVnbTJBd
 XVpOXFvdz09
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