Search Results for author:
Found 47 papers, 11 papers with code
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?
As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.
Dealing with the Paradox of Quality Estimation
In quality estimation (QE), the quality of translation can be predicted by referencing the source sentence and the machine translation (MT) output without access to the reference sentence.
Two Heads are Better than One? Verification of Ensemble Effect in Neural Machine Translation
In the field of natural language processing, ensembles are broadly known to be effective in improving performance.
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation
However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i. e., generative commonsense reasoning).
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing
This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs.
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text
Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models.
Priming Ancient Korean Neural Machine Translation
In recent years, there has been an increasing need for the restoration and translation of historical languages.
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization
In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems.
Alternative Speech: Complementary Method to Counter-Narrative for Better Discourse
We introduce the concept of "Alternative Speech" as a new way to directly combat hate speech and complement the limitations of counter-narrative.
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline
To effectively address this, it is imperative to consider both the speech-level, crucial for recognition accuracy, and the text-level, critical for user-friendliness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
KoBigBird-large: Transformation of Transformer for Korean Language Understanding
This work presents KoBigBird-large, a large size of Korean BigBird that achieves state-of-the-art performance and allows long sequence processing for Korean language understanding.
Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems
First, we build a dataset to train a critic model capable of evaluating the citation, correctness, and fluency of responses generated by LLMs in QA systems.
Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation
In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages.
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction
Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively.
Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding.
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks
In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers.
Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboards
Leaderboard systems allow researchers to objectively evaluate Natural Language Processing (NLP) models and are typically used to identify models that exhibit superior performance on a given task in a predetermined setting.
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona
The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder.
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue
The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dialog turn (intent clustering) and generating a set of intents based on the intent clustering methods (intent induction).
QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation
With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing.
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
This can be attributed to the fact that the amount of available training data in each language follows the power-law distribution, and most of the languages belong to the long tail of the distribution.
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks
As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential.
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP).
Ranked #2 on
Dialog Relation Extraction
on DialogRE
Dialog Relation Extraction
Emotion Recognition in Conversation
+1
Multimodal Frame-Scoring Transformer for Video Summarization
Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors.
There is no rose without a thorn: Finding weaknesses on BlenderBot 2.0 in terms of Model, Data and User-Centric Approach
BlenderBot 2. 0 is a dialogue model that represents open-domain chatbots by reflecting real-time information and remembering user information for an extended period using an internet search module and multi-session.
Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to.
FreeTalky: Don't Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models.
A Self-Supervised Automatic Post-Editing Data Generation Tool
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions.
A New Tool for Efficiently Generating Quality Estimation Datasets
Building of data for quality estimation (QE) training is expensive and requires significant human labor.
Automatic Knowledge Augmentation for Generative Commonsense Reasoning
Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge.
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus
This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available.
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT.
Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning
To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task.
PicTalky: Augmentative and Alternative Communication Software for Language Developmental Disabilities
In this study, we propose PicTalky, which is an AI-based AAC system that helps children with language developmental disabilities to improve their communication skills and language comprehension abilities.
Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification
We derive an optimal subword tokenization result for Korean-English machine translation by conducting a case study that combines the subword tokenization method, morphological segmentation, and vocabulary method.
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge.
An Evaluation Protocol for Generative Conversational Systems
These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets.
Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
Despite its notable success in adversarial learning approaches to multi-domain task-oriented dialog system, training the dialog policy via adversarial inverse reinforcement learning often fails to balance the performance of the policy generator and reward estimator.
Multi-View Attention Network for Visual Dialog
To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e. g., question, dialog history, and image) is required.
Ranked #11 on
Visual Dialog
on VisDial v0.9 val
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text.
Ranked #58 on
Relation Extraction
on DocRED
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
We focus on multi-turn response selection in a retrieval-based dialog system.
EmotionX-KU: BERT-Max based Contextual Emotion Classifier
We propose a contextual emotion classifier based on a transferable language model and dynamic max pooling, which predicts the emotion of each utterance in a dialogue.
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging
Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i. e., a dictionary of known morphological transformation forms, or actions).
Character-Level Feature Extraction with Densely Connected Networks
Generating character-level features is an important step for achieving good results in various natural language processing tasks.
