DinoStackAI/qasper-rag huggingface dataset:
We used the self-critical model of BIBREF13 proposed for image captioning Additionally, we have used enthttps://stackoverflow.com/questions/19053077/looping-over-data-and-creating-individual-figuresropy regularization. To the best of our knowledge, ours is the first GPU based implementation.
Abstractive and extractive models from Nallapati et al., 2016, Pointer generator models with and without coverage from See et al., 2017, and Reinforcement Learning models from Paulus et al., 2018, and Celikyilmaz et al., 2018.
ROUGE-1 41.69
ROUGE-2 19.47
ROUGE-L 37.92
Automatic evaluation metrics show  relative improvements of  11.11, 6.07, 19.29 for BLEU-4, ROUGE-L and SF Coverage respectively (over average baseline). 
Human evaluation  relative improvement for Difficulty, Naturalness and SF Coverage are 8.44,  32.64,  13.57 respectively.
over 113k Wikipedia-based question-answer pairs
stacked bilstms
English Penn Treebank spmrl datasets
3,492 documents
English portion of CoNLL 2012 data BIBREF15
French-English parent model
Freezing certain portions of the parent model and fine tuning others
first train a high-resource language pair, then use the resulting trained network (the parent model) to initialize and constrain training for our low-resource language pair (the child model). by fixing certain parameters of the parent model, letting the rest be fine-tuned by the child model. When we train the child model initialized with the parent model, we fix parameters likely to be useful across tasks so that they will not be changed during child-model training.
average F1-score accuracy
DBpedia (2016-04) DBpedia (2015-10)
by filtering errors in noisy entity linking by the empty query check or domain/range check in query structure ranking
(LC-QuAD) BIBREF8 (QALD-5) dataset BIBREF9
Only 0.2% accuracy gain in morpho-sintactic questions in original test set, and 12.7% accuracy gain on their test set
Answer with content missing: (baseline list) TextRank, KLSum, Lead-1, Lead-K and Random-K
446 sets of parallel text
18.08 percent points on F-score
5130 Mboshi speech utterances
Structured Variational AutoEncoder (SVAE) AUD Bayesian Hidden Markov Model (HMM)
Mboshi (Bantu C25)
V-measure BIBREF16
WED CDA
By solving the Euclidean-distance bipartite matching problem of names by frequency
and gender-specificity
word similarity sentiment classification understanding of gender using non-biased analogies
test the degree to which the spaces are successful at mitigating direct and indirect bias, as well as the degree to which they can still be used in two NLP tasks standardly performed with embeddings, word similarity and sentiment classification
a word or a word-piece
relative 4.5$\%$ gain built upon the code base of ESPnet, and achieves relative $10\%$ gains due to the better architecture and masking strategy
Chinese-English English-Japanese
NIST 2003 (MT03) NIST2004-2006 (MT04-06) NIST 2008 (MT08) KFTT
attention-based NMT system BIBREF23 , BIBREF24
In CH-EN translation, the average improvement is up to 2.23 BLEU points, in EN-JA translation, the improvement can reach 1.96 BLEU point.
DSSM Match Pyramid ARC-II our model with frozen, randomly initialized embeddings
a self-collected dataset of 11 months of search logs as query-product pairs
GL and non-GL studies consists of 670 publications (spanning the years 1938 through 2014) with results from 2,615 uterotrophic bioassays
Bernoulli Naïve Bayes classifier
670 publications
Study descriptor is a set of structured data elements extracted from a publication text that contains specific expert knowledge pertaining to domain topics.
The learning rate boundary of the CLR is selected by the range test (shown in Figure FIGREF7). The base and maximal learning rates adopted in this study are presented in Table TABREF13. Shrink strategy is applied when examining the effects of CLR in training NMT. The optimizers (Adam and SGD) are assigned with two options: 1) without shrink (as “nshrink"); 2) with shrink at a rate of 0.5 (“yshrink"), which means the maximal learning rate for each cycle is reduced at a decay rate of 0.5.
Applying CLR has positive impacts on NMT training for both Adam and SGD it can be observed that the effects of applying CLR to Adam are more significant than those of SGD we see that the trend of CLR with a larger batch size for NMT training does indeed lead to better performance. The benefit of a larger batch size afforded by CLR means that training time can be cut down considerably.
from the online platform Freesound BIBREF8
different annotators are instructed to correct any grammatical errors score each caption in terms of the accuracy of the description and fluency of English, using a scale from 1 to 4 top $N_{\text{cp}}$ captions are selected
we use a previously utilized method for audio captioning BIBREF3 which is based on an encoder-decoder scheme with attention
829 instances
Image Descriptions dataset, which is a subset of 8k-picture of Flickr Image Descriptions dataset which is a subset of the PASCAL VOC-2008 dataset BIBREF16
Precision, recall and F1 score.
BiGRU+CRF
the RoBERTa model achieves the highest F1 value of 94.17
ERNIE-tiny
Three datasets had total of 14.5k samples.
Stanford Natural Language Inference BIBREF7
dot-product attention module to dynamically combine all intermediates
BERT-Attention and BERT-LSTM perform better than vanilla BERT$_{\tiny \textsc {BASE}}$
Each labelled Data point was verified by at least one other person after initial labelling.
original and updated VADER (Valence Aware Dictionary and Sentiment Reasoner)
the original VADER English lexicon.
pre-trained word embeddings need to be tuned with local context during our experiments
word2vec GloVe BIBREF7 fasttext BIBREF8 ELMo
For the test set a BLEU score of 25.7 on Ja-En and 20.7 (2014 test set), 23.1 (2015 test set), and 26.1 (2016 test set) on En-De
a widely used, simple ensembling method (prediction averaging)
Japanese-English (Ja-En) ASPEC data set BIBREF26 WMT data set for English-German (En-De), using the news-test2014 as a development set, and keeping news-test2015 and news-test2016 as test sets.
English-German (En-De)
re-tweets do not bring any additional information to our study, thus we removed them removed any expressions considered to be semantically meaningless like URLs, emoticons, mentions of other users (denoted by the @ symbol) and hashtags downcased and stripped the punctuation
XGBoost, an ensemble of gradient-based decision trees algorithm
90,369,215 tweets written in French, posted by 1.3 Million users
XGBoost AdaBoost Random Forest
AdaBoost Random Forest
The SES score was assigned by architects based on the satellite and Street View images of users' homes.
In case of 2-votes they used 9,298 samples and in case of 3-votes they used 1,298 samples.
four languages
false positives improved by 90% and recall improved by 1%
a database containing historical time series data
candidate-generating parser
By matching users to locations using geolocated tweets data, then matching locations to socioeconomic status using INSEE sociodemographic data.
No, but the authors identified a correlation.
Standard usage of negation Standard usage of plural ending of written words lexical diversity
we combined our Twitter data with the socioeconomic maps of INSEE by assigning each geolocated Twitter user to a patch closest to their estimated home location
Answer with content missing: (Models sections) 100, 200 and 400
global structure in the learned embeddings is related to a linearity in the training objective
BPE level PBSMT models at morpheme and OS levels
multilingual Indian Language Corpora Initiative (ILCI) corpus BIBREF25 OpenSubtitles2016 section of the OPUS corpus collection BIBREF26
character morpheme word
from 1000 to 4000
semantic and linguistic differences between satire articles are more sophisticated, or less easy to read, than fake news articles
Empirical  evaluation  has done using 10 fold cross-validation considering semantic representation with BERT and  measuring   differences between fake news and satire  using coherence metric.
model using the Naive Bayes Multinomial algorithm
First person singular pronoun incidence
Sentence length, number of words, 
Estimates of hypernymy for nouns 
...
Agentless passive voice density,
Average word frequency for content words ,
Adverb incidence

...
consider the title of each sample as a pseudo label and conduct NER pre-training
NER (Named Entity Recognition) is the first task in the joint multi-head selection model relation classification task as a multi-head selection problem auxiliary sentence-level relation classification prediction task
To solve the problem that one entity belongs to multiple triplets, a multi-sigmoid layer is applied soft label embedding, which takes the logits as input to preserve probability of each entity type
in general exception of the Basque idiom
ROC curve (AUC) BIBREF32 and $F_{1}$ score BIBREF33
LR Here GBDT Here RNN Here ours-MTL
Duolingo SLA modeling shared datasets, which have three datasets and are collected from English students who can speak Spanish (en_es), Spanish students who can speak English (es_en), and French students who can speak English (fr_en) BIBREF29
They share words.
Essays collected from students from American Institutes for Research tests, Synthetic responses from Reddit and Teen Line
Logistic regression with TF-IDF with latent semantic analysis representations
Recurrent neural network
approximation of the probability that a held-out alert is flagged if a fixed percentage of a typical population were to be flagged as potential alerts
Severity is manually identified by a team of reviewers.
Urgency is manually identified by a team of reviewers.
MLE model Baseline$+(t)$
Conceptual Captions BIBREF0
3M iterations with the batch size of 4,096
validation and test splits containing approximately 130K, 7K and 7K
ANGE, ATEEMA, GENTREE, and NUMNORM
3,650,909 sentences 87,769,158 tokens
Rating scores given by users
Second on De-En and En-De (NMT) tasks, and third on En-De (SMT) task.
a gold standard for NER in microblogs
extracted features using a classical sparse vectorizer (Term frequency-Inverse document frequency - TF-ID
LOC (Building, Suburb, Street, City, Country, Mountain, Highway, Forest, Coast and Map), ORG (Company Logo), PER (Human Face ).
ELMo USE NBSVM FastText XLnet base cased model (XLnet) BERT base cased (BERT-Cased) BERT base uncased (BERT-Uncased) RoBERTa base model
We may identify three common FL expression forms namely, irony, sarcasm and metaphor. In this paper, figurative expressions, and especially ironic or sarcastic ones, are considered as a way of indirect denial.
dataset provided in “Semantic Evaluation Workshop Task 3” ironic tweets BIBREF95 Riloff’s high quality sarcastic unbalanced dataset BIBREF96 a large dataset containing political comments from Reddit BIBREF97 SA dataset that contains tweets with various FL forms from “SemEval-2015 Task 11” BIBREF66
They experiment on 3 types of classification tasks with different inputs:
2-way: True/False
3-way: True/False news with text true in real world/False news with false text
5-way: True/Parody/Missleading/Imposter/False Connection
Satire/Parody Misleading Content Imposter Content False Connection
On test set RNN that uses user embedding has AUC of 80.53 compared to base RNN 79.24.
embedding approach boosts the average precision of BM25 by 19% and 6% on TREC 2006 and 2007
documents are represented by normalized bag-of-words (BOW) vectors
They merge features of BM25 and semantic measures.
Our training data for this part was created by taking a random sample from Twitter and having it manually annotated on a 5-label basis to produce fully sentiment-labeled parse-trees
which we scored lower
very negative, negative, neutral, positive, very positive
Transformer-based encoder-decoder
M blocks, each consisting of self-attention module, context-attention module, and a two-layer feed-forward network.
M blocks, each consisting of self-attention module and a two-layer feed-forward network.
basic saliency model consists of $M$-layer Transformer encoder blocks ($\mathrm {Encoder}_\mathrm {sal}$) and a single-layer feed-forward network
attention mechanism bilinear pooling
shortcut connections batch normalization (BN) self-normalizing neural networks (SNNs)
Siri internal datasets (en_US and zh_CN)
F1 is improved from 80.15 to 80.50 and from 80.71 to 81.69 of Shortlister and Hipothesis Reranker models respectively.
The dataset was created by extracting utterances from the user log data from an intelligent conversational system.
demote the confidences of the negative ground-truths only when they are the highest so that the influence of using the negative ground-truths is not overwhelming
for a general AI that talks with human beings without making the users feel isolated making up the paraphrase corpus supporting the semantic web search
we increased the dataset size by obtaining various types of sentences from intent arguments, specifically via human-aided sentence rewriting
object level details, segmentation masks, and sentiment of the question
Stacked Attention Networks BIBREF11 Teney et al. Model BIBREF13 Neural-Symbolic VQA BIBREF23 Focal Visual Text Attention (FVTA) BIBREF24 Pythia v1.0 BIBREF27 Differential Networks BIBREF19:
R-CNN architecture
Can you park here?
Is something under the sink broken?
Does this man have children?
BERT BIBREF12
69 tasks
a modest 7% decrease in steps per second
HI-EN dataset has total size of of 18461 while BN-EN has total size of 5538.
randomly assigning any of the sentiment values to each of the test instances
The macro average precision, recall, and f-score
Bengali-English and Hindi-English
aims to make information available on the Web easier to process for machines and humans in RDF are expressed as so-called triples of the form (subject, predicate, object) OWL is equivalent to the description logic $\mathcal {SROIQ}(D)$
200 fine-grained categories
Patterns containing the preposition "of" make up the vast majority of prepositional phrases for both the fact and feel classes and  patterns with the preposition “for” are substantially more common in the feel class than the fact class.
They embed AutoSlog-TS in a bootstrapping framework to learn additional linguistic expressions from the unannotated texts -  they give the labeled training data to AutoSlog-TS, which generates patterns and associated statistics and then identifies high-precision patterns that can be used to label some of the unannotated texts as factual or feeling.
Pattrn based on the speaker's own beliefs or claims, perhaps assuming that they themselves are credible or they involve assessment or evaluations of the arguments of the other speaker.  They are typically also very creative and diverse.
Examples of extracted patters with high probability that correlate with factual argument are: RESULT OF, ORIGIN OF, THEORY OF, EVIDENCE OF, PARTS OF, EVOLUTION OF, PERCENT OF, THOUSANDS OF, EXAMPLE OF, LAW OF
binary Fact or Feel label to each response: texts with score INLINEFORM0 1 are assigned to the fact class and texts with score INLINEFORM1 -1 are assigned to the feeling class.
ConvS2S Transformer
English to Afrikaans, isiZulu, N. Sotho,
Setswana, and Xitsonga parallel corpora from the Autshumato project
We experimented with three owl ontologies: (1) the Wine Ontology, which provides information about wines, wine producers etc.; (2) the Consumer Electronics Ontology, intended to help exchange information about consumer electronics products; and (3) the Disease Ontology, which describes diseases, including their symptoms, causes etc.
Workers chatted with our baseline dialogue agent and assigned a rating 1-5 for the quality of each of the agent's responses. Contexts with rating 1 were mapped to the negative class (dissatisfied) and ratings INLINEFORM0 mapped to the positive class (satisfied).
the trained classifier significantly outperforms both the uncertainty-based methods and our original regular expression, by as much as 0.28 and 0.42 F1 points, respectively
Wall Street Journal(WSJ) portion of Penn Treebank (PTB) CoNLL 2000 chunking CoNLL 2003 English NER Amazon product reviews from different domains: Books, DVDs, Electronics and Kitchen IMDB The movie reviews with labels of subjective or objective MR The movie reviews with two classes
we take turns choosing 15 tasks to train our model with multi-task learning, then the learned Meta-LSTM are transferred to the remaining one task.
a function-level sharing scheme for multi-task learning, in which a shared meta-network is used to learn the meta-knowledge of semantic composition among the different tasks
HatEval YouToxic OffensiveTweets
logistic regression (LR) Support Vector Machines (SVM) LSTM network from the Keras library
Word2Vec and ELMo embeddings.
SST-1 BIBREF14 SST-2 IMDB BIBREF15 Multi-Domain Sentiment Dataset BIBREF16 RN BIBREF17 QC BIBREF18
different average lengths and class numbers Multi-Domain Product review datasets on different domains Multi-Objective Classification datasets with different objectives
LSTM with 4 types of recurrent neural layers.
LEAD-$k$ TextRank LexRank SumBasic C_SKIP
Conv2Conv Transformer TransformerLM
$265\,000$ professional reviews for around $72\,000$ games and $26\,000$ Wikipedia gameplay sections
macro precision, recall and F-1 average precision, recall and F-1
adding more layers to the encoder achieves larger improvements than adding more layers to the decoder the decoder converges faster than the encoder
by masking the previous tokens, and comparing autoregressive NMT with the non-autoregressive counterpart
random dropping random noising random swapping
They train a single model that integrates a BERT language model as a shared parameter layer on NER and RC tasks.
we define abuse as any expression that is meant to denigrate or offend a particular person or group.
weighted cosine similarity classification metric for sentences with one aspect
a word2vec BIBREF10 word embedding model on a set of 189,206 financial articles containing 161,877,425 tokens
By transducing initial hypotheses produced by the biased baseline system to create gender-inflected search spaces which can
be rescored by the adapted model
German Spanish Hebrew
$\mathbf {\Delta G}$ – difference in $F_1$ score between the set of sentences with masculine entities and the set with feminine entities $\mathbf {\Delta S}$ – difference in accuracy between the set of sentences with pro-stereotypical (`pro') entities and those with anti-stereotypical (`anti') entities
They select professions from the list collected by BIBREF4 from US labour statistics and manually translate masculine and feminine examples
Toronto Books Corpus STS 2014 dataset BIBREF37 SICK dataset BIBREF36
paraphrase identification (MSRP) movie review sentiment (MR) product reviews (CR) subjectivity classification (SUBJ) opinion polarity (MPQA) question type classification (TREC)
We constrain our comparison to methods that do not require labelled data
Sequential Denoising Autoencoders (SDAEs) and FastSent
Baseline_1, which applies the probability information Base-line_2, which is the parser using the Support Vector Maching as the train and predic-tion model with numeric type feature from the hashcode of the textual type feature
PDTB as training set, Section 22 as testing set
in-domain, out-of-domain and cross-dataset
NYTimes WashingtonPost FoxNews TheGuardian NYDailyNews WSJ USAToday CNN Time Mashable
dataset Newsroom BIBREF16
Model@!START@$^{I}_{Base}$@!END@ $Model^{I}_{Base}$ with BERT BIBREF28 Model@!START@$^{III}_{Tag}$@!END@ Model@!START@$^{IV}_{Meta}$@!END@
For POS,  improvements for cased BERT are 1.26 2.52  0.5 for TDT,  FTB and PUD datasets respectively.
For NER in-domain test set, improvement is  2.11 F1 and for NER out-of-domain test set, improvement is  5.32 F1.
For Dependency parsing, improvements are in range from 3.35 to 6.64 LAS for cased BERT.
che2018towards lim2018sex FiNER-tagger BIBREF32 gungor2018 HIT-SCIR BIBREF22 BIBREF33
Yle corpus STT corpus Suomi24 corpus (version 2017H2) luotolahti2015towards Common Crawl Finnish Wikipedia
Three tv series are considered.
Answer with content missing: (Table 2) Dataset contains 19062 reviews from 3 tv series.
Eight categories are: Plot of the TV series, Actor/actress actors, Role, Dialogue discussion, Analysis, Platform, Thumb up or down and Noise or others.
It performs better than other models predicting TED talk ratings.
By confining to transcripts only and normalizing ratings to remove the effects of speaker's reputations, popularity gained by publicity, contemporary hot topics, etc.
For particular Empathetic-Dialogues corpus released Raskin et al. is state of the art (as well as the baseline) approach. Two terms are used interchangeably in the paper.
Minimum description length (MDL) as the basic framework to reconcile the two contradicting objectives: generality and specificity.
INLINEFORM0 INLINEFORM1
Among those who wrote more than 50 tweets, 16% of the authors have average sentiment within [0.95, 1.00], while only 1.5% of the authors have average sentiment within [-1.00, -0.95]
The closer the day of the week to Friday and Saturday, the more positive the sentiment; tweets made between 10 a.m. 12 noon are most positive, while those made around 3 a.m. and 20 p.m. are least positive; tweets made in April and May are most positive, while those made in August and September are least positive.
ranking of US states based on their Twitter sentiment correlates with the ranking of US states based on the well-being index
O(2**N)
$$freq(*, word) = freq(word, *) = freq(word)$$ (Eq. 1)
estimating its semantic proximity (from the perspective of a NLP data scientist) with existing applications with known features system would recommend features for the new application in a ranked order
Examples of common features are: N-gram, POS, Context based Features, Morphological Features, Orthographic, Dependency and Lexical
Linguistic Features Semantic Similarity and Relatedness based Features Statistical Features
Analysis Unit (AU) (Corpus level, Document level, Para (paragraph) level,  Sentence level);
Syntactic Unit (SU) (Word, Phrase, N-gram, Regex, POS Regex,);
LOGICAL (AND, OR, AND NOT,  OR NOT);
Normalize Morphosyntactic Variants (yes or no).
N18-1126 UDPipe system of K17-3009 D15-1272 Morfette
PD : i-vectors had segment level F1 score 66.6 and for speaker level had 75.6 F1 score

OSA: For the same levels it had F1 scores of 65.5 and 75.0
Portuguese PD Corpus: for segment level i-vectors had better F1 score comparing to KB by 2.1% and for speaker level by 3.5%
In case of Spanish PD corpus, KB had higher F1 scores in terms of Segment level and Speaker level by 3.3% and 2.0%.
tasks whose domain does not match that of the training data
For Portuguese PD have  for patient 1.24h and for control 1.07 h.
For Portuguese OSA have  for patient 1.10h and for control 1.05 h.
For Spanish PD have for patient 0.49h  and for control 0.50h.
It will improve tagging and parsing performance, syntax based grammatical error correction.
version 2.2 of the Turbo tagger and Turbo parser BIBREF18
5,124 sentences (97,681 tokens)
with performance increases of 2.4%, 1.3%, and 1.6% on IMDB, Yelp 2013, and Yelp 2014, respectively
product category classification and review headline generation
Sentiment classification (datasets IMDB, Yelp 2013, Yelp 2014): 
embedding 56.4% accuracy, 1.161 RMSE, 67.8% accuracy, 0.646 RMSE, 69.2% accuracy, 0.629 RMSE;
encoder 55.9% accuracy, 1.234 RMSE, 67.0% accuracy, 0.659 RMSE, 68.4% accuracy, 0.631 RMSE;
attention 54.4% accuracy, 1.219 RMSE, 66.5% accuracy, 0.664 RMSE, 68.5% accuracy, 0.634 RMSE;
classifier 55.5% accuracy, 1.219 RMSE, 67.5% accuracy, 0.641 RMSE, 68.9% accuracy, 0.622 RMSE.

Product category classification and review headline generation:
embedding 62.26 ± 0.22% accuracy, 42.71 perplexity;
encoder 64.62 ± 0.34% accuracy, 42.65 perplexity;
attention 60.95 ± 0.15% accuracy, 42.78 perplexity;
classifier 61.83 ± 0.43% accuracy, 42.69 perplexity.
INLINEFORM0 bilingual English-Tamil and INLINEFORM1 English-Hindi titles on the Wikimedia
Phrase-Based SMT systems were trained using Moses, grow-diag-final-and heuristic were used for extracting phrases,  and lexicalised reordering and Batch MIRA for tuning.
OpenNMT BIBREF18 with attention-based transformer architecture BIBREF19