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Found 28 papers, 14 papers with code
Evaluating How Users Game and Display Conversation with Human-Like Agents
Recently, with the advent of high-performance generative language models, artificial agents that communicate directly with the users have become more human-like.
How Does the Hate Speech Corpus Concern Sociolinguistic Discussions? A Case Study on Korean Online News Comments
Furthermore, we check how the final corpus corresponds with the definition and scope of hate speech, and confirm that the overall procedure and outcome is in concurrence with the sociolinguistic discussions.
OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation
As this problem originates from the conventional scheme used when creating a POS tagging corpus, we propose an improvement to the existing scheme, which makes it friendlier to generative tasks.
Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text
WARNING: This article contains contents that may offend the readers.
VUS at IWSLT 2021: A Finetuned Pipeline for Offline Speech Translation
In this technical report, we describe the fine-tuned ASR-MT pipeline used for the IWSLT shared task.
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis
In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA).
When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus
Building a natural language dataset requires caution since word semantics is vulnerable to subtle text change or the definition of the annotated concept.
DAGAM: Data Augmentation with Generation And Modification
Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques for a boosted performance.
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets
In hate speech detection, developing training and evaluation datasets across various domains is the critical issue.
Kosp2e: Korean Speech to English Translation Corpus
Most speech-to-text (S2T) translation studies use English speech as a source, which makes it difficult for non-English speakers to take advantage of the S2T technologies.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands
Paraphrasing is often performed with less concern for controlled style conversion.
Open Korean Corpora: A Practical Report
Korean is often referred to as a low-resource language in the research community.
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition
To reduce this computational burden, knowledge distillation (KD), which is a popular model compression method, has been used to transfer knowledge from a deep and complex model (teacher) to a shallower and simpler model (student).
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined.
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation
We transfer the knowledge from a concrete Transformer-based text LM to an SLU module which can face a data shortage, based on recent cross-modal distillation methodologies.
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset
Assessing the similarity of sentences and detecting paraphrases is an essential task both in theory and practice, but achieving a reliable dataset requires high resource.
Towards an Efficient Code-Mixed Grapheme-to-Phoneme Conversion in an Agglutinative Language: A Case Study on To-Korean Transliteration
Code-mixed grapheme-to-phoneme (G2P) conversion is a crucial issue for modern speech recognition and synthesis task, but has been seldom investigated in sentence-level in literature.
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives
Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations, including but not limited to discourse with no clear objective.
Text Matters but Speech Influences: A Computational Analysis of Syntactic Ambiguity Resolution
Analyzing how human beings resolve syntactic ambiguity has long been an issue of interest in the field of linguistics.
Investigating an Effective Character-level Embedding in Korean Sentence Classification
Different from the writing systems of many Romance and Germanic languages, some languages or language families show complex conjunct forms in character composition.
On Measuring Gender Bias in Translation of Gender-neutral Pronouns
However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define.
Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency
This paper suggests a system which identifies the inherent intention of a spoken utterance given its transcript, in some cases using auxiliary acoustic features.
Giving Space to Your Message: Assistive Word Segmentation for the Electronic Typing of Digital Minorities
For readability and disambiguation of the written text, appropriate word segmentation is recommended for documentation, and it also holds for the digitized texts.
Extracting Arguments from Korean Question and Command: An Annotated Corpus for Structured Paraphrasing
Intention identification is a core issue in dialog management.
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection
This paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets.
