| 2-dimensional |
classifier or regression on two variables |
|
| 5gram |
5gram |
|
| adam |
Adam optimizer |
|
| algo |
non-trivial algorithm implemented |
|
| alpha |
alpha phase |
orange |
| analysis |
some extra analysis done, not just giving the test results |
|
| attention |
attention used |
|
| backoff |
|
|
| bagging |
bagging/bootstraping used |
|
| base |
base model |
|
| baseline |
baseline solution |
|
| bernoulli |
Bernoulli (naive Bayes) model used |
|
| bert |
bert |
|
| beta |
beta phase |
green |
| better-than-no-model-baseline |
significantly better than stupid, no-model baseline (e.g. returning the majority class) |
|
| bidirectional |
bidirectional |
|
| bigram |
bigrams considered |
|
| bilstm |
BiLSTM model used |
|
| bow |
bag of words |
|
| bpe |
Text segmented into BPE subword units |
|
| c++ |
written (partially or fully) in C++ |
|
| casemarker |
Special handling of case |
|
| challenge-preparation |
prepare other challenge |
|
| challenging-america |
Challenging America challenge |
|
| char-n-grams |
character n-grams |
|
| character-level |
Character-level |
|
| chi-square |
chi-square test used |
|
| classification |
classification challenge |
|
| clm |
Causal Language Modeling |
|
| cnn |
Convolutional Neural Network |
|
| complement |
Complement variant |
|
| computer-vision |
computer vision |
|
| crf |
|
|
| crm-114 |
CRM-114 used |
|
| data-exploration |
data exploration/visualization |
|
| deathmatch |
deathmatch |
|
| deberta |
deberta |
|
| decision-tree |
decision tree used |
|
| diachronic |
Diachronic/temporal challenge |
|
| document-understanding |
challenge related to Document Understanding |
|
| donut |
Donut model |
|
| dumz20-challenge |
UMZ 2019/2020 (stacjonarne) - konkurs |
|
| eng |
data in English |
|
| ensemble |
|
|
| existing |
some existing solution added |
|
| fairseq |
Fairseq used |
|
| fast-align |
Fast Align |
|
| faster-r-cnn |
Faster R-CNN |
|
| fasttext |
fasttext used |
|
| feature-engineering |
used more advanced pre-processing, feature engineering etc. |
|
| fine-tuned |
fine-tuned |
|
| frage |
FRAGE used |
|
| geval |
geval |
|
| glove |
GloVe used |
|
| golden-query |
"Golden" query used |
|
| goodturing |
|
|
| gpt2 |
GPT-2 used |
|
| gpt2-large |
GPT-2 large used |
|
| gpt2-xlarge |
GPT-2 xlarge used |
|
| gradient-descent |
gradient-descent |
|
| graph |
extra graph |
|
| gru |
Use GRU network |
|
| hashing-trick |
Hashing trick used |
|
| haskell |
written (partially or fully) in Haskell |
|
| huggingface-transformers |
Huggingface Transformers |
|
| hyperparam |
some hyperparameter modifed |
|
| improvement |
existing solution modified and improved as measured by the main metric |
|
| interpolation |
interpolation |
|
| inverted |
inverted |
|
| irstlm |
irstlm |
|
| java |
written (partially or fully) in Java |
|
| just-inference |
Just test a model without training/fine-tuning |
|
| k-means |
k-means or its variant used |
|
| kenlm |
KenLM used |
|
| kneser-ney |
Use Kneser-Ney |
|
| knn |
k nearest neighbors |
|
| knowledge-based |
some external source of knowledge used |
|
| language-tool |
LanguageTool used |
|
| large |
large model |
|
| left-to-right |
only left to right |
|
| lemmatization |
lemmatization used |
|
| linear-regression |
linear regression used |
|
| lisp |
written (partially or fully) in Lisp |
|
| lm |
a language model used |
|
| lm-loss |
Loss of language model used for prediction answers |
|
| locally-weighted |
Locally weighted variant |
|
| logistic-regression |
logistic regression used |
|
| lstm |
LSTM network |
|
| m2m-100 |
M2M-100 facebook model |
|
| mMiniLMv2 |
mMiniLMv2 |
|
| marian |
Marian NMT used |
|
| mbart |
MBart |
|
| mbart-large-50 |
MBart-50 large |
|
| medicine |
medicine-related challenge |
|
| mert |
MERT (or equivalent) for Moses |
|
| mlm |
Masked Language Modeling |
|
| modernization |
diachronic modernization |
|
| moses |
Moses MT |
|
| ms-read-api |
Microsoft Read API |
|
| ms-read-api-2021-04-12 |
Microsoft Read API model ver. 2021-04-12 |
|
| ms-read-api-2021-09-30-preview |
Microsoft Read API model ver. 2021-09-30-preview |
|
| multidimensional |
classifier or regression on many variables |
|
| multinomial |
multinomial (naive Bayes) model used |
|
| multiple-outs |
generated multiple outputs for geval |
|
| n-grams |
n-grams used |
|
| naive-bayes |
Naive Bayes Classifier used |
|
| neural-network |
neural network used |
|
| new-leader |
significantly better than the current top result |
|
| no-fine-tuning |
no fine-tuning |
|
| no-model-baseline |
significantly better than stupid, no-model baseline (e.g. returning the majority class) |
|
| no-pretrained |
Trained from scratch |
|
| no-temporal |
No temporal information |
|
| non-zero |
non zero value for the metric |
|
| null-model |
null model baseline |
|
| ocr |
OCR task |
|
| oddballness |
oddballness used |
|
| perl |
written (partially or fully) in Perl |
|
| plusaplha |
|
|
| pol |
Polish |
|
| postprocessing |
simple postprocessing |
|
| pretrained |
pre-trained embeddings |
|
| probabilities |
return probabilities not just classes |
|
| probability |
probability rather oddballness |
|
| proto |
proto version (e.g. for Donut) |
|
| python |
written (partially or fully) in Python 2/3 |
|
| pytorch-nn |
Pytorch NN |
|
| question-query |
Full question used as query |
|
| r |
written (partially or fully) in R |
|
| randlm |
RandLM |
|
| random-forest |
Random Forest used |
|
| ready-made |
Machine Learning framework/library/toolkit used, algorithm was not implemented by the submitter |
|
| ready-made-model |
Ready-made ML model |
|
| regexp |
handcrafted regular expressions used |
|
| regularization |
some regularization used |
|
| right-to-left |
model working from right to left |
|
| rnn |
Recurrent Neural Network |
|
| roberta |
RoBERTa model |
|
| roberta-base |
RoBERTa Base |
|
| roberta-challam |
RoBERTa trained on Chronicling America |
|
| roberta-pl |
Polish Roberta |
|
| roberta-xlm |
Multilingual Roberta |
|
| ruby |
written (partially or fully) in Ruby |
|
| rule-based |
rule-based solution |
|
| scala |
written (partially or fully) in Scala |
|
| scikit-learn |
sci-kit learn used |
|
| self-made |
algorithm implemented by the submitter, no framework used |
|
| sentence-piece |
sentence pieces used (unigram) |
|
| seq2seq |
Sequence To Sequence Modeling |
|
| simple |
simple solution |
|
| small |
small model |
|
| solr |
Solr used |
|
| standards-preparations |
How to standards |
|
| stemming |
stemming used |
|
| stop-words |
stop words handled in a special manner |
|
| stupid |
simple, stupid rule-based solution |
|
| subword-regularization |
Use subword-regularization |
|
| supervised |
supervised |
|
| svm |
Support Vector Machines |
|
| t5 |
T5 models |
|
| temporal |
temporal information taken into account |
|
| tesseract |
Tesseract OCR |
|
| tetragram |
tetragrams |
|
| tf |
term frequency |
|
| tf-idf |
tf-idf used |
|
| timestamp |
timestamp considered |
|
| tokenization |
special tokenization used |
|
| torch |
(py)torch used |
|
| train |
train yourself an ML model |
|
| transformer |
Transformer model used |
|
| transformer-decoder |
Transformer-decoder architecture used |
|
| transformer-encoder |
Transformer-encoder architecture used |
|
| transformer-encoder-decoder |
Transformer-encoder-decoder architecture used |
|
| trigram |
trigrams considered |
|
| truecasing |
truecasing used |
|
| tuning |
tuning an existing model |
|
| uedin |
Uedin corrector |
|
| umz-2019-challenge |
see https://eduwiki.wmi.amu.edu.pl/pms/19umz#Dodatkowe_punkty_za_wygranie_wyzwa.2BAUQ- |
|
| unigram |
only unigrams considered |
|
| unks |
special handling of unknown words |
|
| video |
video involved |
|
| vowpal-wabbit |
Vowpal Wabbit used |
|
| wikisource |
WikiSource used |
|
| word-level |
Word-level |
|
| word2vec |
Word2Vec |
|
| wordnet |
some wordnet used |
|
| xgboost |
xgboost used |
|
| zumz-2019-challenge |
zumz competition |
|