25 citations · 63 across the 17 of their papers we have counts for
10 papers · 1 filter
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Yukyung Lee, Takyoung Kim, Hoonsang Yoon +3
Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past r…
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking
Takyoung Kim, Hoonsang Yoon, Yukyung Lee +2
Dialogue state tracking (DST) aims to extract essential information from multi-turn dialogue situations and take appropriate actions. A belief state, one of the core pieces of info…
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables
Jounghee Kim, Pilsung Kang
Wav2vec 2.0 is an end-to-end framework of self-supervised learning for speech representation that is successful in automatic speech recognition (ASR), but most of the work on the t…
Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances
Takyoung Kim, Yukyung Lee, Hoonsang Yoon +3
The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations.…
Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification
Junghoon Lee, Jounghee Kim, Pilsung Kang
Language models (LMs) pretrained on a large text corpus and fine-tuned on a downstream text corpus and fine-tuned on a downstream task becomes a de facto training strategy for seve…
MultiOIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
Youngbin Ro, Yukyung Lee, Pilsung Kang
In this paper, we propose MultiOIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system wi…
Sentence transition matrix: An efficient approach that preserves sentence semantics
Myeongjun Jang, Pilsung Kang
Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a se…
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Myeongjun Jang, Pilsung Kang
Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a…
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Myeongjun Jang, Seungwan Seo, Pilsung Kang
Sequence-to-sequence (Seq2seq) models have played an important role in the recent success of various natural language processing methods, such as machine translation, text summariz…
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network
Gichang Lee, Jaeyun Jeong, Seungwan Seo +2
In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to…