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Found 20 papers, 5 papers with code
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.
Collecting Verified COVID-19 Question Answer Pairs
We release a dataset of over 2, 100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites.
Using the Poly-encoder for a COVID-19 Question Answering System
To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19.
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)
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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.
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.
Priming Ancient Korean Neural Machine Translation
In recent years, there has been an increasing need for the restoration and translation of historical languages.
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.
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.
What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?
Humans can effortlessly understand the coordinate structure of sentences such as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, respectively".
Private Meeting Summarization Without Performance Loss
Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns.
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue.
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.
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.
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.
An Evaluation Protocol for Generative Conversational Systems
These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets.
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.
