Search Results for author:
Found 6 papers, 1 papers with code
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding
The success of Natural Language Understanding (NLU) benchmarks in various languages, such as GLUE for English, CLUE for Chinese, KLUE for Korean, and IndoNLU for Indonesian, has facilitated the evaluation of new NLU models across a wide range of tasks.
AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions
In this paper, we demonstrate the usefulness of this AGent pipeline by creating two sets of unanswerable questions from answerable questions in SQuAD and HotpotQA.
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension
From the analysis results, we suggest new directions for developing Vietnamese language models.
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models
Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks.
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source
Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models.
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese
We propose a conversion algorithm to create the dataset for sentence extraction-based machine reading comprehension and three types of approaches for sentence extraction-based machine reading comprehension in Vietnamese.
