Search Results for author:
Found 61 papers, 41 papers with code
Disfluency Detection for Vietnamese
In this paper, we present the first empirical study for Vietnamese disfluency detection.
Investigating the Impact of ASR Errors on Spoken Implicit Discourse Relation Recognition
We present an empirical study investigating the influence of automatic speech recognition (ASR) errors on the spoken implicit discourse relation recognition (IDRR) task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Who's Who: Large Language Models Meet Knowledge Conflicts in Practice
To analyze how current large language models (LLMs) align with our recommendation, we introduce WhoQA, a public benchmark dataset to examine model's behavior in knowledge conflict situations.
RecGPT: Generative Pre-training for Text-based Recommendation
We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation.
Improving Vietnamese-English Medical Machine Translation
Machine translation for Vietnamese-English in the medical domain is still an under-explored research area.
PhoWhisper: Automatic Speech Recognition for Vietnamese
We introduce PhoWhisper in five versions for Vietnamese automatic speech recognition.
JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention
JPIS incorporates the supporting profile information into its encoder and introduces a slot-to-intent attention mechanism to transfer slot information representations to intent detection.
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations.
Ranked #1 on
Slot Filling
on MixATIS
PhoGPT: Generative Pre-training for Vietnamese
The base model, PhoGPT-4B, with exactly 3. 7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20480 token types.
XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech
We present XPhoneBERT, the first multilingual model pre-trained to learn phoneme representations for the downstream text-to-speech (TTS) task.
Joint Multilingual Knowledge Graph Completion and Alignment
The proposed model combines two components that jointly accomplish KG completion and alignment.
Ranked #1 on
Knowledge Graph Completion
on DPB-5L (French)
From Disfluency Detection to Intent Detection and Slot Filling
We present the first empirical study investigating the influence of disfluency detection on downstream tasks of intent detection and slot filling.
A High-Quality and Large-Scale Dataset for English-Vietnamese Speech Translation
In this paper, we introduce a high-quality and large-scale benchmark dataset for English-Vietnamese speech translation with 508 audio hours, consisting of 331K triplets of (sentence-lengthed audio, English source transcript sentence, Vietnamese target subtitle sentence).
Two-view Graph Neural Networks for Knowledge Graph Completion
WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes.
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation
We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3. 02M sentence pairs, which is 2. 9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15.
BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese.
Ranked #4 on
Abstractive Text Summarization
on vietnews
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i. e., link prediction).
COVID-19 Named Entity Recognition for Vietnamese
The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic.
Intent Detection and Slot Filling for Vietnamese
Intent detection and slot filling are important tasks in spoken and natural language understanding.
Ranked #2 on
Intent Classification and Slot Filling
on ATIS (vi)
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing
We present the first multi-task learning model -- named PhoNLP -- for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing.
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets.
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
We compare the two baselines with key configurations and find that: automatic Vietnamese word segmentation improves the parsing results of both baselines; the normalized pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema linking; latent syntactic features extracted from a neural dependency parser for Vietnamese also improve the results; and the monolingual language model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher performances than the recent best multilingual language model XLM-R (Conneau et al., 2020).
VinAI at ChEMU 2020: An accurate system for named entity recognition in chemical reactions from patents
This paper describes our VinAI system for the ChEMU task 1 of named entity recognition (NER) in chemical reactions.
A Label Attention Model for ICD Coding from Clinical Text
In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments.
Ranked #7 on
Medical Code Prediction
on MIMIC-III
BERTweet: A pre-trained language model for English Tweets
We present BERTweet, the first public large-scale pre-trained language model for English Tweets.
Ranked #1 on
Sentiment Analysis
on TweetEval
PhoBERT: Pre-trained language models for Vietnamese
We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese.
A Vietnamese Text-Based Conversational Agent
This paper introduces a Vietnamese text-based conversational agent architecture on specific knowledge domain which is integrated in a question answering system.
A Vietnamese Question Answering System
Question answering systems aim to produce exact answers to users' questions instead of a list of related documents as used by current search engines.
A Vietnamese information retrieval system for product-price
A price information retrieval (IR) system allows users to search and view differences among prices of specific products.
A Fast Template-based Approach to Automatically Identify Primary Text Content of a Web Page
Search engines have become an indispensable tool for browsing information on the Internet.
A Capsule Network-based Model for Learning Node Embeddings
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data.
Ranked #55 on
Node Classification
on Pubmed
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents.
Detecting Chemical Reactions in Patents
Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration.
A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing
We propose the first multi-task learning model for joint Vietnamese word segmentation, part-of-speech (POS) tagging and dependency parsing.
End-to-end neural relation extraction using deep biaffine attention
We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features.
Ranked #5 on
Relation Extraction
on CoNLL04
Improving Topic Models with Latent Feature Word Representations
Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks.
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks.
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).
Ranked #42 on
Link Prediction
on WN18RR
From POS tagging to dependency parsing for biomedical event extraction
Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT.
Ranked #1 on
Dependency Parsing
on GENIA - LAS
jLDADMM: A Java package for the LDA and DMM topic models
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models.
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing.
Ranked #15 on
Dependency Parsing
on Penn Treebank
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model.
NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets".
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.
Ranked #60 on
Link Prediction
on WN18RR
From Word Segmentation to POS Tagging for Vietnamese
This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable.
A Fast and Accurate Vietnamese Word Segmenter
We propose a novel approach to Vietnamese word segmentation.
Sequence to Sequence Learning for Event Prediction
This paper presents an approach to the task of predicting an event description from a preceding sentence in a text.
A Mixture Model for Learning Multi-Sense Word Embeddings
Our model generalizes the previous works in that it allows to induce different weights of different senses of a word.
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly.
Ranked #5 on
Part-Of-Speech Tagging
on UD
A survey of embedding models of entities and relationships for knowledge graph completion
Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Search Personalization with Embeddings
Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user's topical interests.
An empirical study for Vietnamese dependency parsing
This paper presents an empirical comparison of different dependency parsers for Vietnamese, which has some unusual characteristics such as copula drop and verb serialization.
STransE: a novel embedding model of entities and relationships in knowledge bases
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Neighborhood Mixture Model for Knowledge Base Completion
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete.
Ripple Down Rules for Question Answering
Recent years have witnessed a new trend of building ontology-based question answering systems.
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging
In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task.
