output
20202023
most citedSelf-Guided Contrastive Learning for BERT Sentence Representations

10 citations

8 papers

eess.AS20234 cited

An empirical study on speech restoration guided by self supervised speech representation

Jaeuk Byun, Youna Ji, Soo Whan Chung +2

Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additive noise, reverberation, clipping…

cs.LG20214 cited

Global-Local Item Embedding for Temporal Set Prediction

Seungjae Jung, Young-Jin Park, Jisu Jeong +4

Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e.g., personalized purchase prediction of shoppi…

cs.CL2021

Weakly Supervised Pre-Training for Multi-Hop Retriever

Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim +2

In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question de…

cs.CL202110 cited

Self-Guided Contrastive Learning for BERT Sentence Representations

Taeuk Kim, Kang Min Yoo, Sang-goo Lee

Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we…

cs.CL2021

NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation

Sungdong Kim, Minsuk Chang, Sang-Woo Lee

We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generate…

cs.CV2021

Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

Song Park, Sanghyuk Chun, Junbum Cha +2

A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the…