20 citations · 29 across the 12 of their papers we have counts for
4 papers · 1 filter
On Monotonic Aggregation for Open-domain QA
Sang-eun Han, Yeonseok Jeong, Seung-won Hwang +1
Question answering (QA) is a critical task for speech-based retrieval from knowledge sources, by sifting only the answers without requiring to read supporting documents. Specifical…
When to Read Documents or QA History: On Unified and Selective Open-domain QA
Kyungjae Lee, Sang-eun Han, Seung-won Hwang +1
This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources. Two types of sources, QA-pa…
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering
Yongho Song, Dahyun Lee, Myungha Jang +4
The long-standing goal of dense retrievers in abtractive open-domain question answering (ODQA) tasks is to learn to capture evidence passages among relevant passages for any given…
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Joel Jang, Seungone Kim, Seonghyeon Ye +5
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks. Pre…