146 citations · 656 across the 31 of their papers we have counts for
7 papers · 1 filter
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
Takumi Kawashima, Qing Yu, Akari Asai +2
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend…
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo +2
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based…
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval
Akari Asai, Eunsol Choi
Recent pretrained language models "solved" many reading comprehension benchmarks, where questions are written with access to the evidence document. However, datasets containing inf…
XOR QA: Cross-lingual Open-Retrieval Question Answering
Akari Asai, Jungo Kasai, Jonathan H. Clark +3
Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where l…
Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
Akari Asai, Hannaneh Hajishirzi
Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy…
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning
Daya Guo, Akari Asai, Duyu Tang +6
We study the problem of generating inferential texts of events for a variety of commonsense like \textit{if-else} relations. Existing approaches typically use limited evidence from…
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
Lichao Sun, Kazuma Hashimoto, Wenpeng Yin +4
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, ho…