Search Results for author:
Found 6 papers, 5 papers with code
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.
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)
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.
