Named Entity Recognition for Vietnamese GitHub

Vietnamese NLP tasks

Dependency parsing

• Experiments employ the benchmark Vietnamese dependency treebank VnDT of 10K+ sentences, using 1,020 sentences for test, 200 sentences for development and the remaining sentences for training. LAS and UAS scores are computed on all tokens (i.e. including punctuation).

VnDT v1.1:

• Results on the VnDT v1.1 for Biaffine, jPTDP-v2 and VnCoreNLP are reported in the jointWPD paper "A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing."

VnDT v1.0:

• Results for the BIST graph/transition-based parsers, MSTparser and MaltParser are reported in "An empirical study for Vietnamese dependency parsing."

Intent detection and Slot filling

PhoATIS

• The first dataset for intent detection and slot filling for Vietnamese, based on the common ATIS benchmark in the flight booking domain. Data is localized (e.g. replacing slot values with Vietnamese-specific entities) to fit the context of flight booking in Vietnam.
• Training set: 4478 sentences
• Development set: 500 sentences
• Test set: 893 sentences

Machine translation

PhoMT Dataset

• A large-scale and high-quality dataset for Vietnamese-English Machine Translation with 3.02M sentence pairs, available at https://github.com/VinAIResearch/PhoMT.
  • Consists of 6 domains: TED Talks, WikiHow, MediaWiki, OpenSubtitles, News and Blog.
  • Training set: 2.9M sentence pairs
  • Validation set: 18719 sentence pairs
  • Test set: 19151 sentence pairs

IWSLT2015 Dataset

• Dataset is from The IWSLT 2015 Evaluation Campaign with 150K sentence pairs, also be obtained from https://github.com/tensorflow/nmt.

English-to-Vietnamese

tst2015 is used for test

tst2013 is used for test

• The ELMo score is reported in Semi-Supervised Sequence Modeling with Cross-View Training. The Transformer score is available at https://github.com/duyvuleo/Transformer-DyNet.

Vietnamese-to-English

tst2013 is used for test

Named entity recognition

PhoNER_COVID19

• A named entity recognition dataset for Vietnamese with 10 newly-defined entity types in the context of the COVID-19 pandemic. Data is extracted from news articles and manually annotated. In total, there are 34 984 entities over 10 027 sentences.
• Training set: 5027 sentences
• Development set: 2000 sentences
• Test set: 3000 sentences

VLSP

• 16,861 sentences for training and development from the VLSP 2016 NER shared task:
  • 14,861 sentences are used for training.
  • 2k sentences are used for development.
• Test data: 2,831 test sentences from the VLSP 2016 NER shared task.
• NOTE that in the VLSP 2016 NER data, each word representing a full personal name are separated into syllables that constitute the word. The VLSP 2016 NER data also consists of gold POS and chunking tags as reconfirmed by VLSP 2016 organizers. This scheme results in an unrealistic scenario for a pipeline evaluation:
  • The standard annotation for Vietnamese word segmentation and POS tagging forms each full name as a word token, thus all word segmenters have been trained to output a full name as a word and all POS taggers have been trained to assign a POS label to the entire full-name.
  • Gold POS and chunking tags are NOT available in a real-world application.
• For a realistic scenario, contiguous syllables constituting a full name are merged to form a word. POS/chunking tags--if used--have to be automatically predicted!

• [1] denotes that scores are reported in "ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task"
• [2] denotes that BiLSTM-CRF-based scores are reported in "VnCoreNLP: A Vietnamese Natural Language Processing Toolkit"

Part-of-speech tagging

• 27,870 sentences for training and development from the VLSP 2013 POS tagging shared task:
  • 27k sentences are used for training.
  • 870 sentences are used for development.
• Test data: 2120 test sentences from the VLSP 2013 POS tagging shared task.

• Result for jPTDP-v2 is reported in "A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing."
• Results for BiLSTM-CRF-based models and RDRPOSTagger are reported in "From Word Segmentation to POS Tagging for Vietnamese."

Semantic parsing

ViText2SQL

• The first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese, consisting of about 10K question and SQL query pairs.
• Training set: 6831 question and query pairs
• Development set: 954 question and query pairs
• Test set: 1906 question and query pairs

Word segmentation

• Training & development data: 75k manually word-segmented training sentences from the VLSP 2013 word segmentation shared task.
• Test data: 2120 test sentences from the VLSP 2013 POS tagging shared task.

• Results for VnTokenizer, JVnSegmenter and DongDu are reported in "A hybrid approach to Vietnamese word segmentation."