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Found 36 papers, 3 papers with code
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.
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).
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
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.
Priming Ancient Korean Neural Machine Translation
In recent years, there has been an increasing need for the restoration and translation of historical languages.
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.
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.
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.
Translation of Multifaceted Data without Re-Training of Machine Translation Systems
In our MT pipeline, all the components in a data point are concatenated to form a single translation sequence and subsequently reconstructed to the data components after translation.
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplification of problem-solving ability.
Evalverse: Unified and Accessible Library for Large Language Model Evaluation
This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework.
sDPO: Don't Use Your Data All at Once
As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important.
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core.
Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism
This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches.
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
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.
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
We introduce SOLAR 10. 7B, a large language model (LLM) with 10. 7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks.
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.
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.
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.
Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)
This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps).
Inter-Annotator Agreement in the Wild: Uncovering Its Emerging Roles and Considerations in Real-World Scenarios
Inter-Annotator Agreement (IAA) is commonly used as a measure of label consistency in natural language processing tasks.
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.
DMOps: Data Management Operation and Recipes
Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline.
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.
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.
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.
