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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…
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…
Syllable-level Neural Language Model for Agglutinative Language
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes. We propose a metho…
An Embedded Deep Learning based Word Prediction
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying…