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Found 18 papers, 4 papers with code
Abusive Span Detection for Vietnamese Narrative Texts
Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health.
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education
Our evaluation of six well-known LLMs, namely BLOOMZ-7. 1B-MT, LLaMA-2-7B, LLaMA-2-70B, GPT-3, GPT-3. 5, and GPT-4. 0, on the ViMMRC 1. 0 and ViMMRC 2. 0 benchmarks and our proposed dataset shows promising results on the MCSB ability of LLMs for Vietnamese.
ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks.
Vietnamese Language Models
Vietnamese Social Media Text Processing
+1
Efficient Finetuning Large Language Models For Vietnamese Chatbot
Furthermore, the availability of pretrained LLMs and instruction-tune datasets for Vietnamese language is limited.
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension
The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies.
Machine Reading Comprehension
Vietnamese Machine Reading Comprehension
+1
Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese
The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language.
Natural Language Inference
Natural Language Understanding
+2
Is word segmentation necessary for Vietnamese sentiment classification?
To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling
Previous studies on joint Chinese word segmentation and part-of-speech tagging mainly follow the character-based tagging model focusing on modeling n-gram features.
Span Detection for Aspect-Based Sentiment Analysis in Vietnamese
Aspect-based sentiment analysis plays an essential role in natural language processing and artificial intelligence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Span Labeling Approach for Vietnamese and Chinese Word Segmentation
In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG.
Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese
In this paper, we implement this idea to improve word segmentation and part of speech tagging the Vietnamese language by employing a simplified constituency parser.
An Empirical Study for Vietnamese Constituency Parsing with Pre-training
In this work, we use a span-based approach for Vietnamese constituency parsing.
A Vietnamese Dataset for Evaluating Machine Reading Comprehension
Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models.
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles
In particular, we develop a process of creating a corpus for the Vietnamese machine reading comprehension.
Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capture
In this paper, we approach Vietnamese word segmentation as a binary classification by using the Support Vector Machine classifier.
Emotion Recognition for Vietnamese Social Media Text
In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear, and surprise.
NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector
This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019.
