DinoStackAI/qasper-rag huggingface dataset:
summary variable - analytically thinking, clout, tone, authentic, Big five variable - openness, conscientiousness, extraversion, agreeableness, neuroticism, Emotional variables - positive emotions in the text, negative emotions in the text, personal drives - power, reward, risk, achievement, affiliation, personal pronouns -  counts the number of 1st, 2nd, and 3rd personal pronouns used, Minkowski distance between each profile and average values of these features created from the ISIS magazines
ratio of violent words in the tweet, ratio of curse words in the tweet, frequency of words with all capital letters, 200 dimension sized vector for the tweet calculated using word embedding, tf-idf scores for top scoring uni-grams, bi-grams and tri-grams
hotels/items appear infrequently or never in historical data Recommender systems typically have difficulty handling these items effectively due to the lack of relevant training data
Hits@k for hotel context prediction Comparison using cosine similarity Average intra/inter market embedding similarities Visualization of embeddings Most similar hotels Algebraic operations on hotel embeddings
dataset contains more than 40M user click sessions, which includes more than 1.1 million unique hotels
.41, .31, and .31 Proportional $\text{F}_1$ on Holders, Targets, and Polarity Expressions, respectively (.41, .36, .56 Binary $\text{F}_1$)
7451 sentences 6949 polar expressions 5289 targets 635 holders
professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, video games, music, products, movies, TV-series, stage performance, restaurants, etc
our model delivers an improvement of 7% question match accuracy and 11% interaction match accuracy over the previous state-of-the-art
For SParC, context-dependent seq2seq and syntaxSQL-con. For Spider, a recursive decoding procedure, graph neural networks, and intermediate representation models.
Datasets from PersonaChat and Wizard of Wikipedia tasks.
On average the three selection methods had better BLEU scores than Random and Oracle methods. 
The proposed method Domain-Finetune-Top-500k had better BLEU score than random by 4.34, better than Moore-Lewis by 0.38, better than Oracle by 0.92, and better than All method by 1.4
established method for data selection was proposed by BIBREF4
subtitles, medical text (PDF documents from the European Medicines Agency), legal text (legislative text of the European Union), translations of the Koran, and IT-related text (manuals and localization files of open-source software)
BERT DistilBERT RoBERTa GPT-2 XLNet
The sizes of word embeddings and position embeddings are set to 200 and 100, attention heads and blocks are set to 6 and 2 respectively, and the dropout of multi-head attention is set to 0.7,  the minibatch size is 64, the initial learning rate is set to 0.001, the dropout rate to 0.3, and $\lambda $ to 0.6 for fake news detection.
shared features in the shared layer are equally sent to their respective tasks without filtering
The selected sharing layer is trained jointly on the tasks of stance detection and fake news detection
search to translations longer than 0.25 times the source sentence length search to either the length of the best Beam-10 hypothesis or the reference length
2,169 sentences
BLEU-4 slot error rate informativeness naturalness
Scratch-NLG MTL-NLG Zero-NLG Supervised-NLG
we compare the label accuracy of “SUPPORTS” label against a supervised approach – HexaF
0.76 suggests that at least 3 out of the 4 questions have to be answered correctly 0.67 suggests that at least 2 out of the 3 questions has to be answered correctly
The named entities are then used to generate the questions by masking the entities for the subsequent stage.
around 185k claims from the corpus of 5.4M Wikipedia articles
split each game report into individual sentences, and, for each sentence, find its corresponding data in the box-score table as the content record we found some simple rules are sufficient to obtain high-quality results
AttnCopy-S2S Rule-based Method Multi-Attribute Style Transfer (MAST) BIBREF11 Adversarial Style Transfer (AdvST) BIBREF12
Reconstructing the auxiliary sentence and reconstructing the reference sentence.
macro-average recall
CUB bird COCO
Various sequence tagging tasks: Argument detection, ACE entity and event detection, part-of-speech tagging, CoNLL chunking, CoNLL named entity recognition, GENIA bio-entity recognition, WNUT named entity recognition. They also evaluate on Stanford Sentiment Treebank, Penn TreeBank constituency parsing, and Stanford Natural Language Inference.
For the first experiment, the datasets used were: argument component detection persuasive essays, ACE 2005 dataset of entities/essays, POS tags from Universal Dependencies, CoNLL 2000 shared task on chunking, CoNLL 2003
shared task on named entity recognition, the Bio-Entity Recognition Task dataset, WNUT 16 dataset on NER over tweets. For the second experiment, they used the CoNLL 2003 NER
dataset, the Stanford Sentiment Treebank (SST5) dataset, the constituency parsing model for the
Penn TreeBank as dataset, and the Stanford Natural Language Inference Corpus (SNLI) dataset.
identifying which biases to focus on and how to paraphrase or modify the sentence to de-bias it
A dataset they created that contains occupation and names data.
Generative Approaches ::: Seq2seq Generative Approaches ::: GPT2-FT: Generative Approaches ::: Speaker: Generative Approaches ::: ECM: Retrieval-Based Approaches ::: Skeleton-to-Response (SR) Retrieval-Based Approaches ::: Retrieval + Style Transfer (RST) Retrieval-Based Approaches ::: Retrieval + Style Transfer (RST) Retrieval-Based Approaches ::: Retrieval + Reranking (RRe)
Chinese and English
gender-specific (Chinese) dataset emotion-specific (Chinese) dataset sentiment-specific (English) dataset
the ratios of iambic lines ending with a stressed syllable (“The view of earthly glory: men might say”) to lines ending with an extra unstressed one (“Till this time pomp was single, but now married”), pointing out that the distribution of values across scenes is strongly bimodal.
high reliability
measuring the confidence intervals and principal component analysis of frequencies of selected function words in Shakespeare’s and Fletcher’s plays BIBREF25, controversial CUSUM technique concerning the occurrences of another set of selected function words and lines ending with an extra unstressed syllable BIBREF26 or principal component analysis of 64 most frequent words BIBREF27.
personal health information of deceased persons surrogate data derived data. Data that can not be used to reconstruct the original text veterinary texts
Texts produced in the clinical settings do not always tell a complete or accurate patient story (e.g. due to time constraints or due to patient treatment in different hospitals), yet important decisions can be based on them. As language is situated, a lot of information may be implicit, such as the circumstances in which treatment decisions are made discrimination can occur when individuals or groups receive unfair treatment as a result of automated processing, which might be a result of biases in the data that were used to train models. Clinical texts may include bias coming from both patient's and clinician's reporting. prejudices held by healthcare practitioners which may impact patients' perceptions communication difficulties in the case of ethnic differences Observational bias Although variance in health outcome is affected by social, environmental and behavioral factors, these are rarely noted in clinical reports Dual use
EM INLINEFORM0 EM INLINEFORM0 Rouge INLINEFORM0
either (1) inferred from the context using LIWC (this occurs when we provide sentence beginnings which are emotionally colored themselves), or (2) set to an input emotion descriptor $\mathbf {e}$
BERT. We add a linear layer on top of BERT and we fine-tune it BERT-Joint. We use the layers for both tasks in the BERT baseline, $L_{g_1}$ and $L_{g_2}$, and we train for both FLC and SLC jointly (cf. Figure FIGREF7-b). BERT-Granularity. We modify BERT-Joint to transfer information from SLC directly to FLC
Although not all of the 18 types are listed, they include using loaded language or appeal to authority and slogans, using logical fallacies such as strawmen, hidden ad-hominen fallacies ad red herrings.
retrieved 451 news articles from 48 news outlets, both propagandistic and non-propagandistic according to Media Bias/Fact Check, which professionals annotators annotated according to eighteen persuasion techniques
the trigram language model performed better on Subtask B the bigram language model performed better on Subtask A
The system sorts all the tweets for each hashtag and orders them based on their log probability score, where the funniest tweet should be listed first
KenLM Toolkit
For Subtask A, the system goes through the sorted list of tweets in a hashtag file and compares each pair of tweets. For Subtask B, the system outputs all the tweet_ids for a hashtag file starting from the funniest.
mitigates these contextual biases
Hard-Debiased Word Embeddings was also extremely effective at mitigating the difference in F1 scores for all relations
Name Anonymization surprisingly substantially increases F1 score gap for the hypernym relation, but slightly decreases F1 score gap for all other relations.
for a given relation between two entities, assume that any sentence from an article written about one of those entities that mentions the other entity expresses the relation
Snopes fact-checking website
PolitiFact14 Emergent16 PolitiFact17 RumourEval17 Snopes17 CLEF-2018 FEVER18
Corpus has 6422 claims, 16509 ETSs, 8291 FGE sets and 14296 ODCs.
For stance detection they used MLP, for evidence extraction they used Tf-idf and BiLSTM, for claim validation they used MLP, BiLSTM and SVM
corpus covers multiple domains, including discussion blogs, news, and social media
For stance annotation the inter-annotator agreement was 0.7, for FGE annotation inter-annotator agreement was 0.55
a measure that calculates the cross entropy between the word distribution of the model output and that of the target data
Workshop on Statistical Machine Translation (WMT) data script of the drama, “Friends," English bible data
length and style of sample output
Cross entropy between word distribution of model output and word distribution of target data.
Arabic, Chinese, French, English, and German
experiment with combining various LSTM models enhanced with a number of novel features (O No additional features,  NS Neutral & Sexism,  NR Neutral & Racism, RS Racism & Sexism,  NRS Neutral, Racism & Sexism) in an ensemble.
The best performing single classifier produces F1 0.9265. The best ensemble classifier  (O+NS+RS+NR+NRS) produce  F1 0.9320.
BIBREF12 , in which character n-grams and gender information were used as features BIBREF5 investigated the impact of the experience of the annotator in the performance two step classification BIBREF16 BIBREF13 , which focuses on various classes of Sexism CNN in, BIBREF16
dataset of 500 rescored intent annotations found in the lattices in cancellations and refunds domain
the rescoring was judged by two annotators, who labeled 250 examples each
The ability of the training algorithm to find parameters minimizing the Morfessor cost is evaluated by using the trained model to segment the training data, and loading the resulting segmentation as if it was a Morfessor Baseline model. We observe both unweighted prior and likelihood, and their $\alpha $-weighted sum.

The closeness to linguistic segmentation is evaluated by comparison with annotated morph boundaries using boundary precision, boundary recall, and boundary $F_{1}$-score BIBREF21.
Proposed approach is best in:
- Recall English: +3.47 (70.84 compared to next best 67.37)
- Precision Finnish: +6.16 (68.18 compared to 62.02)
- Recall NorthSami: +1.44 (62.84 compared to 61.40)
BLEU BIBREF35 , METEOR BIBREF36 and chrF BIBREF37 metrics precision, recall and F-measure
two models (GPT2 and DialoGPT) on two datasets (DailyDialog and CALLHOME)
1) generating appropriate responses for a specific role given a conversation history (speaker role modeling) 2) predicting whether a new speaker will interject on the next sentence of a conversation
annotations for each turn
natural dialog
We also finetune (FT) DialoGPT and GPT2 on Interview without speaker information
7 programs on National Public Radio (NPR) over 20 years
Reuters 20NG, 20 Newsgroup NYT, New York Times WS, Web Snippet TMN, Tag My News AN, ABC News
LDA BIBREF0 LLDA, Labelled LDA BIBREF11 and PLLDA, Partially Labelled LDA BIBREF9 DMR, LDA with Dirichlet Multinomial Regression BIBREF8 WF-LDA, Word Feature LDA BIBREF16 LF-LDA, Latent Feature LDA BIBREF5 GPU-DMM, Generalized Pólya Urn DMM BIBREF7 PTM, Pseudo document based Topic Model BIBREF18
Normalised Pointwise Mutual Information
augmented with a set of Beta random variables auxiliary variable
human annotation and automatic predictions over multiple rounds to label automatically data as job-related or not job-related
multiple iterations of human annotations and automatic machine learning predictions, followed by some linguistic heuristics, to extract job-related tweets from personal and business accounts
CoNLL_X bosque data, News data by Lusa agency, Sports news data
On average, it had better Recall by 0.481 in case of news dataset and by 0.372 in case of sports news dataset. 
On average, it had better Precision by 0.086 in case of news dataset and by 0.37 in case of sports news dataset. 
On average, it had better F1 by 0.381 in case of news dataset and by 0.616 in case of sports news dataset.
BIBREF2, who predict the roles of the Russian trolls on Twitter by leveraging social theory and Actor-Network Theory approaches
2973371 tweets by 2848 Twitter users
the ground truth labels for the troll users are available we leverage more persistent entities and their labels: news media. We assume a learning scenario with distant supervision where labels for news media are available. By combining these labels with a citation graph from the troll accounts to news media, we can infer the final labeling on the accounts themselves
CNN Daily Mail Children's Book Test
The different AS Reader models had average test accuracy of 71,35% and AS Reader (avg ensemble) had the highest test accuracy between all tested models with 75.4%

In case of Daily Mail average was  75.55% and greedy assemble had the highest value with 77.7%
CBT NE average was 69.65% and greedy ensemble had the highest value of 71% 

CBT CN had average of 65.5% and avg assemble had the highest value of 68.9%
Attentive and Impatient Readers Chen et al. 2016 MenNN Dynamic Entity Representation LSTM
LSTM obtains an overall score of 0.56 while humans' score is 0.58
Twitter data has fewer missing languages than what census-based data contains because it matches populations better when they are weighting by GDP
81.5 billion web pages covered in Common Crawl dataset
insert a [CLS] token before each sentence and a [SEP] token after each sentence use interval segment embeddings to distinguish multiple sentences within a document
non-pretrained Transformer baseline
negative log-likelihood
No, they come from the top ranked web pages relevant to a query and from Wikipedia
the AliMe and Quora dataset
3: In addition to word embedding, there is a POS tag embedding and a dependcy relation embedding.
Best authors achieved (different models) in terms of F1 score is:
German - 0.6446
Italian - 0.6999
Dutch - 0.6057
4405 English sentences, 1173 German sentences, 633 Italian sentences and 583 Dutch sentences
MUSE BIBREF17
French, Italian, Spanish and German Existing English sentiment lexicons are translated to the target languages
Each dialogue contains only one reference summary.
We asked two linguists to mark the quality of every summary on the scale of $-1$, 0, 1, where $-1$ means that a summarization is poor, extracts irrelevant information or does not make sense at all, 1 – it is understandable and gives a brief overview of the text, and 0 stands for a summarization that extracts only a part of relevant information, or makes some mistakes in the produced summary.
MIDDLE-n, LONGEST-n, LONGER-THAN-n and MOST-ACTIVE-PERSON are the baselines, and experiments also carried out on Pointer generator networks, Transformers, Fast Abs RL, Fast Abs RL Enhanced, LightConv and DynamicConv
contains over 16k chat dialogues with manually annotated summaries
Only 100 successfully adversarial examples were manually checked, not all of them.
While the models aim to generate examples which preserve the semantics of the text with minimal perturbations, the Random model randomly replaces a character, which may not preserve the semantics.
Our complete AEG model outperforms all the other models with significant drop in accuracy. For the CNN-Word, DeepWordBug decreases the accuracy from 89.95% to 28.13% while AEG model further reduces it to 18.5%.
A word-based convolutional model (CNN-Word) and a character-based convolutional model (CNN-Char)
A word-based convolutional neural network (CNN-Word)
Training ::: Training with Reinforcement learning
We fine-tune our model to fool a target classifier by learning a policy that maximizes a specific discrete metric formulated based on the constraints required to generate adversarial examples. In our work, we use the self-critical approach of Rennie et al. BIBREF36 as our policy gradient training algorithm.

Training ::: Training with Reinforcement learning ::: Self-critical sequence training (SCST)
In SCST approach, the model learns to gather more rewards from its sampled sequences that bring higher rewards than its best greedy counterparts. First, we compute two sequences: (a) $y^{\prime }$ sampled from the model's distribution $p(y^{\prime }_j|y^{\prime }_{
Urdu Hindi English Japanese Bulgarian
Answer with content missing: (subscripts 2 and 3)
NER task: Arabic, Bengali, Czech, German, English, Spanish, French, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Turkish, and Chinese.
POS task: Arabic, Bulgarian, Catalan, Czech, Danish, German, Greek, English, Spanish, Estonian, Basque, Persian, Finnish, French, Galician, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese, Korean, Latvian, Marathi, Dutch, Norwegian (Bokmaal and Nynorsk), Polish, Portuguese (European and Brazilian), Romanian, Russian, Slovak, Slovenian, Swedish, Tamil, Telugu, Turkish, Urdu, and Chinese.
Language pairs that are typologically different
ner F1 score pos zero-shot accuracy
CoNLL-2002 and -2003 sets an in-house dataset with 16 languages Universal Dependencies (UD) BIBREF7
Tweets without candidate names are removed, URLs and pictures are removed from the tweets that remain.
Proposed model achieves HITS@100 of 30.0 compared to best baseline model result of 29.2 on recommendation task.
bAbI Movie Dialog dataset
bidirectional recurrent neural network encoder with Gated Recurrent Units (GRU) additional recurrent neural network with GRU units
On paraphrase and semantic similarity proposed model has F1 score of 0.677 compared to best previous model result of 0.674, while on sentiment classification it has 0.656 compared to 0.648 of best previous result.
INESC-ID,  lsislif, unitn and  Webis.
nnfeats, ikr, linearsvm and svckernel.
On average our method significantly out-performed all the baselines, with the average improvement in accuracy across OMICS tasks between SEM-HMM and each baseline being statistically significant at a .01 level across all pairs and on sizes of INLINEFORM0 and INLINEFORM1 using one-sided paired t-tests.
The "frequency" baseline, the "conditional" baseline, the "BMM" baseline and the "BMM+EM" baseline
The Open Minds Indoor Common Sense (OMICS) corpus
FastText Convolutional Neural Networks (CNNs) Long Short-Term Memory Networks (LSTMs)
char n-grams TF-IDF vectors Bag of Words vectors (BoWV)
GloVe
Yes, a normalized mutual friends metric
variation and change in the use of words characteristic from eight US cities that have non-standard spellings
four domains
We use KB-LDA, phrase_hLDA, and LDA+GSHL as our baseline methods, and compare ontologies extracted from hrLDA, KB-LDA, phrase_hLDA, and LDA+GSHL with DBpedia ontologies. We use precision, recall and F-measure for this ontology evaluation. A true positive case is an ontology rule that can be found in an extracted ontology and the associated ontology of DBpedia. A false positive case is an incorrectly identified ontology rule. A false negative case is a missed ontology rule.
syntax and document structures such as paragraph indentations and item lists, assigns multiple topic paths for every document, and allows topic trees to grow vertically and horizontally. . By contrast, a complex sentence can be subdivided into multiple atomic sentences. Given that the syntactic verb in a relation triplet is determined by the subject and the object, a document INLINEFORM4 in a corpus INLINEFORM5 can be ultimately reduced to INLINEFORM6 subject phrases (we convert objects to subjects using passive voice) associated with INLINEFORM7 relation triplets INLINEFORM8 The idea is to find all syntactically related noun phrases and their connections using a language parser such as the Stanford NLP parser BIBREF24 and Ollie BIBREF25 .
DRANZIERA benchmark dataset
LM3 (Chameleon Words Similarity) and LM4 (Entropy Change)
LM1: Significant Words Overlap LM2: Symmetric KL-Divergence (SKLD) LM3: Chameleon Words Similarity LM4: Entropy Change ULM1: Word2Vec ULM2: Doc2Vec ULM3: GloVe ULM4 and ULM5: FastText ULM6: ELMo ULM7: Universal Sentence Encoder
Amazon Instant Video,  Automotive, Baby,  Beauty, Books,  Clothing Accessories,  Electronics,  Health, Home, Kitchen, Movies, Music,  Office Products, Patio, Pet Supplies, Shoes,  Software,  Sports Outdoors,  Tools Home Improvement, Toys Games, Video Games.
Because sentiment features extracted the same information as other features.
training set containing 10,822 tweets and a development set containing 4,845 tweets test set of 9,961 tweets was provided without labels training set containing 8,000 tweets and a development set containing 2,260 tweets test set of 7,513 tweets
F-score for class 1 (ADR) micro-averaged F-score of the class 1 (intake) and class 2 (possible intake)
0.435 on Task1 and 0.673 on Task2.
INLINEFORM0 -grams generalized over domain terms Pronoun Lexicon features domain word embeddings domain word clusters
The following set of features were calculated separately for each tweet and each lexicon:

the number of tokens with INLINEFORM0 ;

the total score = INLINEFORM0 ;

the maximal score = INLINEFORM0 ;

the score of the last token in the tweet.
INLINEFORM0 -grams General-domain word embeddings General-domain word clusters Negation: presence of simple negators the number of tokens with all characters in upper case, the number of hashtags, presence of positive and negative emoticons, whether the last token is a positive or negative emoticon, the number of elongated words presence of exclamation and question marks, whether the last token contains an exclamation or question mark
The model takes the concatenation of the query and the document as input. The query-sentence and sentence-sentence relationships are jointly modeled by the self-attention mechanism BIBREF12. The model is fine-tuned to utilize the general language representations of BERT BIBREF13.
To automatically construct query-focused summarization examples using Wikipedia, the statements' citations in Wikipedia articles as pivots to align the queries and documents. Figure FIGREF1 shows an example that is constructed by the proposed method. We first take the highlighted statement as the summary. Its supporting citation is expected to provide an adequate context to derive the statement, thus can serve as the source document. On the other hand, the section titles give a hint about which aspect of the document is the summary's focus. Therefore, we use the article title and the section titles of the statement to form the query.