2 citations · 5 across the 7 of their papers we have counts for
8 papers
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking
Björn Bebensee, Haejun Lee
In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen…
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering
Akhil Kedia, Mohd Abbas Zaidi, Haejun Lee
Generative models have recently started to outperform extractive models in Open Domain Question Answering, largely by leveraging their decoder to attend over multiple encoded passa…
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
Youngjune Lee, Oh Joon Kwon, Haeju Lee +3
Data scarcity and noise are important issues in industrial applications of machine learning. However, it is often challenging to devise a scalable and generalized approach to addre…
You Only Need One Model for Open-domain Question Answering
Haejun Lee, Akhil Kedia, Jongwon Lee +3
Recent approaches to Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank passages with a separate reranker model and gener…
SLM: Learning a Discourse Language Representation with Sentence Unshuffling
Haejun Lee, Drew A. Hudson, Kangwook Lee +1
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training…
Answering Open-Domain Questions of Varying Reasoning Steps from Text
Peng Qi, Haejun Lee, Oghenetegiri "TG" Sido +1
We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model…