21 research outputs found
Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech
Converting written texts into their spoken forms is an essential problem in
any text-to-speech (TTS) systems. However, building an effective text
normalization solution for a real-world TTS system face two main challenges:
(1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates,
ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable
syllables, such as URL, email address, hashtag, and contact name. In this
paper, we propose a new two-phase normalization approach to deal with these
challenges. First, a model-based tagger is designed to detect NSWs. Then,
depending on NSW types, a rule-based normalizer expands those NSWs into their
final verbal forms. We conducted three empirical experiments for NSW detection
using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF
models on a manually annotated dataset including 5819 sentences extracted from
Vietnamese news articles. In the second phase, we propose a forward
lexicon-based maximum matching algorithm to split down the hashtag, email, URL,
and contact name. The experimental results of the tagging phase show that the
average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%,
reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our
approach has low sentence error rates, at 8.15% with CRF and 7.11% with
BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.Comment: The 14th International Conference on Knowledge and Systems
Engineering (KSE 2022
RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification
In this study, we present a novel and challenging multilabel Vietnamese
dataset (RMDM) designed to assess the performance of large language models
(LLMs), in verifying electronic information related to legal contexts, focusing
on fake news as potential input for electronic evidence. The RMDM dataset
comprises four labels: real, mis, dis, and mal, representing real information,
misinformation, disinformation, and mal-information, respectively. By including
these diverse labels, RMDM captures the complexities of differing fake news
categories and offers insights into the abilities of different language models
to handle various types of information that could be part of electronic
evidence. The dataset consists of a total of 1,556 samples, with 389 samples
for each label. Preliminary tests on the dataset using GPT-based and BERT-based
models reveal variations in the models' performance across different labels,
indicating that the dataset effectively challenges the ability of various
language models to verify the authenticity of such information. Our findings
suggest that verifying electronic information related to legal contexts,
including fake news, remains a difficult problem for language models,
warranting further attention from the research community to advance toward more
reliable AI models for potential legal applications.Comment: ISAILD@KSE 202
Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs
This paper presents a knowledge graph construction method for legal case
documents and related laws, aiming to organize legal information efficiently
and enhance various downstream tasks. Our approach consists of three main
steps: data crawling, information extraction, and knowledge graph deployment.
First, the data crawler collects a large corpus of legal case documents and
related laws from various sources, providing a rich database for further
processing. Next, the information extraction step employs natural language
processing techniques to extract entities such as courts, cases, domains, and
laws, as well as their relationships from the unstructured text. Finally, the
knowledge graph is deployed, connecting these entities based on their extracted
relationships, creating a heterogeneous graph that effectively represents legal
information and caters to users such as lawyers, judges, and scholars. The
established baseline model leverages unsupervised learning methods, and by
incorporating the knowledge graph, it demonstrates the ability to identify
relevant laws for a given legal case. This approach opens up opportunities for
various applications in the legal domain, such as legal case analysis, legal
recommendation, and decision support.Comment: ISAILD@KSE 202
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization
Multi-document summarization is challenging because the summaries should not
only describe the most important information from all documents but also
provide a coherent interpretation of the documents. This paper proposes a
method for multi-document summarization based on cluster similarity. In the
extractive method we use hybrid model based on a modified version of the
PageRank algorithm and a text correlation considerations mechanism. After
generating summaries by selecting the most important sentences from each
cluster, we apply BARTpho and ViT5 to construct the abstractive models. Both
extractive and abstractive approaches were considered in this study. The
proposed method achieves competitive results in VLSP 2022 competition.Comment: In Proceedings of the 9th International Workshop on Vietnamese
Language and Speech Processing (VLSP 2022
NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment
In recent years, natural language processing has gained significant
popularity in various sectors, including the legal domain. This paper presents
NeCo Team's solutions to the Vietnamese text processing tasks provided in the
Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on
legal domain knowledge acquisition for low-resource languages through data
enrichment. Our methods for the legal document retrieval task employ a
combination of similarity ranking and deep learning models, while for the
second task, which requires extracting an answer from a relevant legal article
in response to a question, we propose a range of adaptive techniques to handle
different question types. Our approaches achieve outstanding results on both
tasks of the competition, demonstrating the potential benefits and
effectiveness of question answering systems in the legal field, particularly
for low-resource languages.Comment: ISAILD@KSE 202
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models
This paper describes the NOWJ1 Team's approach for the Automated Legal
Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal
task performance by integrating classical statistical models and Pre-trained
Language Models (PLMs). For the document retrieval task, we implement a
pre-processing step to overcome input limitations and apply learning-to-rank
methods to consolidate features from various models. The question-answering
task is split into two sub-tasks: sentence classification and answer
extraction. We incorporate state-of-the-art models to develop distinct systems
for each sub-task, utilizing both classic statistical models and pre-trained
Language Models. Experimental results demonstrate the promising potential of
our proposed methodology in the competition.Comment: ISAILD@KSE 202
Prospects for Food Fermentation in South-East Asia, Topics From the Tropical Fermentation and Biotechnology Network at the End of the AsiFood Erasmus+Project
Fermentation has been used for centuries to produce food in South-East Asia and some foods of this region are famous in the whole world. However, in the twenty first century, issues like food safety and quality must be addressed in a world changing from local business to globalization. In Western countries, the answer to these questions has been made through hygienisation, generalization of the use of starters, specialization of agriculture and use of long-distance transportation. This may have resulted in a loss in the taste and typicity of the products, in an extensive use of antibiotics and other chemicals and eventually, in a loss in the confidence of consumers to the products. The challenges awaiting fermentation in South-East Asia are thus to improve safety and quality in a sustainable system producing tasty and typical fermented products and valorising by-products. At the end of the “AsiFood Erasmus+ project” (www.asifood.org), the goal of this paper is to present and discuss these challenges as addressed by the Tropical Fermentation Network, a group of researchers from universities, research centers and companies in Asia and Europe. This paper presents current actions and prospects on hygienic, environmental, sensorial and nutritional qualities of traditional fermented food including screening of functional bacteria and starters, food safety strategies, research for new antimicrobial compounds, development of more sustainable fermentations and valorisation of by-products. A specificity of this network is also the multidisciplinary approach dealing with microbiology, food, chemical, sensorial, and genetic analyses, biotechnology, food supply chain, consumers and ethnology
Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries.
BACKGROUND: Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. METHODS: This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. RESULTS: We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0-14.0) and the median awareness score was 29.6 (IQR = 26.6-32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a 'great-extent-of-confidence' in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. INTERPRETATION: There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into disproportionate SARS-CoV-2 burden of disease by gender or HCW type