Aaron Chan Profile picture
PhD Student @CSatUSC / @nlp_usc / @USC_ISI. Designing #NLProc systems to be more knowledgeable and trustworthy. Prev: @Google @Penn @UofMaryland.
3 May
🚨 Our #ICLR2021 paper shows that KG-augmented models are surprisingly robust to KG perturbation! 🧐

arXiv: arxiv.org/abs/2010.12872
Code: github.com/INK-USC/deceiv…

To learn more, come find us at Poster Session 9 (May 5, 5-7PM PDT): iclr.cc/virtual/2021/p….

KGs have helped neural models perform better on knowledge-intensive tasks and even “explain” their predictions, but are KG-augmented models really using KGs in a way that makes sense to humans?

We primarily investigate this question by measuring how the performance of KG-augmented models changes when the KG’s semantics and/or structure are perturbed, such that the KG becomes less human-comprehensible.

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