DinoStackAI/qasper-rag huggingface dataset:
Each topic is assigned to 4 experts to conduct the summary writing in two phases: facet identification, and summary generation. For the aspect facet identification, the experts read and digested all the news documents and reader comments under the topic. Then for each aspect, the experts extracted the related facets from the news document. The summaries were generated based on the annotated aspect facets.
45 topics from those 6 predefined categories Each topic contains 10 news documents and 4 model summaries On average, each topic contains 215 pieces of comments and 940 comment sentences Each news document contains an average of 27 sentences, and each sentence contains an average of 25 words. dataset contains 19k annotated aspect facets
classify figures in various morphology with correct identity (accuracy = 72.7%) demonstrates that LGI can understand the verbs and nouns
move left move right this is … the size is big/small’ the size is not small/big give me a … enlarge/shrink rotate …’
the human IPS to extract the quantity information from language text and proposed a biologically plausible textizer to produce text symbols output
mimic the human IPS to extract the quantity information from language text and proposed a biologically plausible textizer to produce text symbols output
transmitting the output image from the decoder to the encoder, an imagination loop is formed, which enables the continual operation of a human-like thinking process involving both language and image
logistic regression classifier trainable embedding layer, three layers of a stacked recurrent neural network, and a softmax classifier
used NanigoNet, a language detector based on GCNNs (Gated Convolutional Neural Networks) BIBREF17 that supports human languages as well as programming languages
Has at least one pull request or pull request review comment event between November 2017 and September 2019,

Has 50 or more starts,

Has a size between 1MB and 1GB, and

Has a permissive license.
Ratio of the target perplexity over the source calculated by a language model Normalized edit distance between the source and the target Binary variable indicating whether the edit purely consists of changes in numbers
the top 10 languages are English, simplified Chinese, Japanese, Russian, French, German, Portuguese, Spanish, Korean and Hindi
the top and bottom layer of the BERT fine-tuned on SNLI dataset
Two natural language inference datasets, MNLI BIBREF11 and SNLI
(1) WikiPassageQA BIBREF13 (2) InsuranceQA (version 1.0) BIBREF14 (3) Quasar-t BIBREF15 (4) SearchQA BIBREF16
Downstream tasks include text classification, natural language inference, paraphrase detection, and semantic similarity probe sentence-level linguistic phenomena
(1) a multinomial logistic regression model with ridge estimator (2) a multilayer perceptron (3) a support vector machine learner, Sequential Minimal Optimization (SMO) (4) a decision tree (J48)
a subset of COCTAILL, a corpus of course books covering five CEFR levels (A1-C1)
length-based, lexical, morphological, syntactic and semantic features
Sentence length
Modal verbs to verbs
Average token length
Particle IncSc
Extra-long words
SG pronoun IncSc
Number of characters
Punctuation IncSc
LIX
Subjunction IncSc
S-verb IncSc
A1 lemma IncSc
S-verbs to verbs
A2 lemma IncSc
Adjective IncSc
B1 lemma IncSc
Adjective variation
B2 lemma IncSc
Adverb IncSc
C1 lemma IncSc
Adverb variation
C2 lemma IncSc
Noun IncSc
Difficult word IncSc
Noun variation
Difficult noun and verb IncSc
Verb IncSc
Out-of-Kelly IncSc
Verb variation
Missing lemma form IncSc
Nominal ratio
Avg. Kelly log frequency
Nouns to verbs
Function word IncSc
Average dependency length
Lexical words to non-lexical words
Dependency arcs longer than
Lexical words to all tokens
Longest dependency from root node
Neuter gender noun IncSc
Ratio of right dependency arcs
Con- and subjunction IncSc
Ratio of left dependency arcs
Past participles to verbs
Modifier variation
Present participles to verbs
Pre-modifier IncSc
Past verbs to verbs
Post-modifier IncSc
Present verbs to verbs
Subordinate IncSc
Supine verbs to verbs
Relative clause IncSc
Relative structure IncSc
Prepositional complement IncSc
Bilog type-token ratio
Square root type-token ratio
Avg. nr. of senses per token
Pronouns to nouns
Noun senses per noun
Pronouns to prepositions
BIBREF9 , BIBREF12
Random Selection Majority Class bag-of-words Tweet2vec BIBREF32
Latent Dirichlet Allocation (LDA) topic modeling algorithm BIBREF8
bag of stopwords, Part-of-speech tags (POS), and syntactic dependency relations (DEPREL) count of special characters, consecutive characters and letters, URLs, hashtags, users' mentions uppercase ratio and the tweet length
eight Plutchik's emotions positive and negative list of bad and sexual words from BIBREF10 belief, denial, doubt, fake, knowledge, negation, question, and report assertives verbs BIBREF14, bias BIBREF15, factive verbs BIBREF16, implicative verbs BIBREF17, hedges BIBREF18, report verbs BIBREF15 pronoun, anx, cogmech, insight, cause, discrep, tentat, certain, inhib, incl care, harm, fairness, cheating, loyalty, betrayal, authority, subversion, sanctity, and degradation
Word2vec averaging (public release 300d), word2vec averaging (academic corpus), Paragraph Vector
we evaluate the KeyVec model on two text understanding tasks that take continuous distributed vectors as the representations for documents: document retrieval and document clustering.
document retrieval and document clustering
For the document retrieval task - the dataset of the document pool contained 669 academic papers published by IEEE. Fro the document clustering task -  the dataset of 850 academic papers, and 186 associated venues which are used as ground-truth for evaluation.
The Árni Magnússon Instit. for Icelandic Studies, Reykjavik University (RU),  University of Iceland (UI),  RÚV, Creditinfo, The Association of the Visually Impaired, Grammatek, Miðeind. Tiro
IceNLP Greynir Nefnir ABLTagger a flexible lexicon acquisition tool A punctuation system for Icelandic open source correction system a statistical phrase-based MT system a bidirectional LSTM model using the neural translation system OpenNMT a system based on an attention-based neural network An API and a web user interface
STEVIN programme in the Netherlands Plan for the Advancement of Language Technology in Spain Estonian LT programmes
in the year 2000 couple of LT resources and products were developed in the years leading up to that
computers, wireless routers, and speakers
SNAP Amazon Dataset BIBREF19 Bing Liu's dataset BIBREF20
Dataset I consists of 480 concepts previously analyzed in BIBREF1, BIBREF4. 240 are positive examples, titles from the Wikipedia list of controversial issues, and 240 are negative examples chosen at random and exclusive of the positives Dataset II is based on a more recent version of the Wikipedia list of controversial issues (May 2017). Dataset III is extracted from 3561 concepts whose Wikipedia pages are under edit protection, assuming that many of them are likely to be controversial.
Dataset I  -  480 concepts, 240  controversial examples, and  240 not-controversial examples.
Dataset II -  608 controversial concepts
Dataset III -  3561 controversial concepts
As positive examples we take, from this list, all concepts which appear more than 50 times in Wikipedia. For negative examples, we follow BIBREF1, BIBREF4 and select a like number of concepts at random The annotation instructions were: “Given a topic and its description on Wikipedia, mark if this is a topic that people are likely to argue about.”. Average pairwise kappa agreement on this task was 0.532. Annotations were normalized to controversiality scores on an integer scale of 0 - 10.
The topics from Wikipedia list of controversial issues that appear more than 50 times in Wikipedia, topics with their Wikipedia pages under edit protection.
Nearest neighbors (NN) Estimator Naive Bayes (NB) Estimator Recurrent neural network (RNN)
nearest-neighbor estimator Naive Bayes model bidirectional RNN
NJM vas selected as the funniest caption among the three options 22.59% of the times, and NJM captions posted to Bokete averaged 3.23 stars
With a questionnaire asking subjects to rank methods according to its "funniness". Also, by posting the captions to Bokete to evaluate them by received stars
999,571 funny captions for 70,981 images
Based on the number of stars users assign funny captions, an LSTM calculates the loss value L as an average of each mini-batch and returns  L when the number of stars is less than 100, otherwise L-1.0
U.S. Census Bureau conducted Annual Retail Trade Survey of U.S. Retail and Food Services Firms for the period of 1992 to 2013
Machine learning approach
ten languages
Extracting cae features requires three steps, as illustrated in Figure FIGREF6 . First, an utd system is applied to the target language to extract pairs of speech segments that are likely to be instances of the same word or phrase
GlobalPhone
Croatian
Hausa
Mandarin
Spanish
Swedish
Turkish
ZRSC
Buckeye
Xitsonga
Precision and recall at a given threshold
Our model outperforms PG-MMR when trained and tested on the Multi-News dataset Transformer performs best in terms of R-1 while Hi-MAP outperforms it on R-2 and R-SU
From a diverse set of news sources on site newser.com
56,216
German (De) and English (En) languages
Europarl v9 parallel data set NewsTest2013 and NewsTest2014 datasets from the WMT 2014 news translation track.
Europarl v9 parallel data set NewsTest2013 and NewsTest2014 datasets from the WMT 2014 news translation track
The WNUT 2017 dataset had entities already seen in the training set filtered out while the OntoNotes dataset did not. Cross-context patterns thus provided more significant information for NER in WNUT 2017 because the possibility of memorizing entity forms was removed.
Complexity is defined by examples of a singular named entity (e.g. work-of-art and creative-work entities) being represented by multiple surface forms. Mapping all of these forms to a single NE requires a complex understanding of the variations, some of which are genre-specific. Confusability is defined by examples when it becomes more difficult to disambiguate named entities that share the same surface form, such as the "language" versus "NORP" distinction represented by the surface forms Dutch and English.
BiLSTM-CNN proposed by BIBREF1
news classification sentiment analysis Wikipedia article classification questions and answers categorization
module that stores previously seen examples throughout its lifetime used for sparse experience replay and local adaptation to prevent catastrophic forgetting and encourage positive transfer
on PAN 2018 dataset, the accuracy is 82.31% for English, 80.22% for Spanish and 80.50% for Arabic
It's a recurrent neural network with n-gram model
static public cache stores the most frequent states lifetime of a private cache actually can last for the entire dialog section for a specific user subsequent utterances faster as more states are composed and stored
A model that contains the expected user-specific entities.
Yes, in Waseem and Hovy (2016)
tweets that are part of a larger hateful discourse or contain links to hateful content while not explicitly having textual cues
The features are the output of running node2vec on a community graph where the nodes are users, and they are connected if one of them follows the other on Twitter.
The hierarchical personal values lexicon with 50  sets of words and phrases that represent the user's value.
only in the 806-class task predicting <= 25 clusters
29,494
1000 people
By querying Twitter Search API for the tweets containing a first-person and a past-tense verb that describes a common activity.
among supervised models, CNN, LSTM and BLSTM encapsulates most of the syntactic and social properties with a great accuracy, while BOW, DSSM, STV and T2V does that among the unsupervised models Tweet length affects the task prediction accuracies LDA is insensitive to input word order, CNN, LSTM and BLSTM are extremely sensitive
if we cannot train a classifier to predict a property based on its tweet representation, then this property is not encoded in this representation
Optimal per-word reward is found using SGD, which in this case is the same as the perceptron algorithm
Length normalization; Google’s NMT correction; constant word reward
if a translation ending at time INLINEFORM2 scores higher than a longer translation, as long as it does not fall within the top INLINEFORM3 when compared with partial translations of length INLINEFORM4 (or complete translations of length at most INLINEFORM5 ), it will be pruned and unable to block the longer translation. But if we widen the beam ( INLINEFORM6 ), then translation accuracy will suffer. We call this problem (which is BIBREF0 's sixth challenge) the beam problem.
Random Forest to perform humor recognition by using the following two groups of features:  latent semantic structural features and semantic distance features.
one layer of convolution on top of word embedding vectors, achieves excellent results on multiple tasks
Incongruity Ambiguity Interpersonal Effect Phonetic Style
By laughter from the audience
using another LSTM for encoding all assertions $a$ in $A_x$ , as illustrated in Figure 3 . Each $a$ , originally in the form of $$ , is transformed into a sequence of tokens by chunking $c_1$ , $c_2$ , concepts which are potentially multi-word phrases, into $[c_{11},c_{12},c_{13}...]$ and $[c_{21},c_{22},c_{23}...]$ . Thus, $a=[c_{11},c_{12},c_{13}...,r,c_{21},c_{22},c_{23}...]$ .
open-domain Twitter dialogues
ConceptNet
The authors crawled all areas listed an TripAdvisor's SiteIndex and gathered all links related to hotels. Using Selenium, they put a time gap between opening each page, to mimic human behaviour and avoid having their scraper being detected. They discarded pages without a review and for pages with a review, they collected the review's profile, the overall rating, the summary, the written text and subratings, where given.
cahr3-MS-vec char4-MS-vec char2-MS-vec
the annotated English Gigaword corpus
BLEU score of 35.48 on En-Fr, 23.27 on En-De, 34.43 on Fr-En, 28.86 on De-En
They use a sum of charn-MS-vec and the standard word embedding as an input of an RNN
A crowdsourced twitter dataset containing 19358 tweets
Proposed model had 0.63 F1 score and 83.49% accuracy compared to the 0.61 F1 and 83.28% accuracy of best compared method.
developing bias mitigation methods that maintain (or even increase) the classification performance and reduce the bias at the same time
Debiased Word Embeddings Gender Swap Bias fine-tuning
most effective method was applying both debiased embedding and gender swap
Convolutional Neural Network (CNN) Gated Recurrent Unit (GRU) Bidirectional GRU with self-attention ( INLINEFORM0 -GRU)
word2vec train on Google News corpus; FastText train on Wikipedia corpus; randomly initialized embeddings
AUC scores on the original test set AUC scores on the unbiased generated test set the false positive/negative equality differences proposed in BIBREF1 which aggregates the difference between the overall false positive/negative rate and gender-specific false positive/negative rate
the public benchmark dataset of “Quora Question Pairs”
BiLSTM autoencoder as the Condense model simple LSTM decoder enhanced with attention BIBREF14 and copy mechanisms BIBREF32
3,731 movies; for each movie we are given a large set of reviews (99.8 on average)
They define cWeight as weight obtained for each sentence using all the models where the sentences is in the summary of predicted by each model.
Best result was obtained by using combination of: Jaccard/Cosine Similarity Matrix, TextRank and InferSent Based Model
We implement our question answering system using state-of-the-art open source components.
3084 real user requests  assigned to suitable answers from the training corpus.
They propose two new metrics. One, which they call the Neighbour Similarity Test, calculates how many shared characteristics there are between entities whose representations are neighbors in the embedding space. The second, which they call the Type and Category Test, is the same as the  Neighbour Similarity Test, except it uses entity types and categories in the place of individual entity characteristics.
a subject, a predicate, and an object in a knowledge base
As there can be multiple correct output utterances for a given input utterance there is no quantitative way to evaluate how well a model is performing. The current evaluation methods like perplexity and BLEU score are not good enough and correlate very weakly with human judgments. According to them, the metrics (like Kiros et al, 2015 BIBREF32 ) that are based on distributed sentence representations hold the most promise for the future. It is because word-overlap metrics like BLEU simply require too many ground-truth responses to find a significant match for a reasonable response due to the high diversity of dialogue responses.
perplexity and BLEU score
the authors proposed using adversarial training for open-domain dialogue generation such that the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. The task is considered as a reinforcement learning problem where two systems get jointly trained: a generative model to produce response sequences, and a discriminator (similar to the human evaluator in the Turing test) that distinguishes between the human-generated dialogues and the machine-generated ones The generative model defines the policy that generates a response given the dialog history and the discriminative model is a binary classifier that takes a sequence of dialog utterances as inputs and outputs whether the input is generated by the humans or machines.
Sequence to Sequence approaches for dialogue modelling Language Model based approaches for dialogue modelling
The speech recognition was done using n-gram statistical model The grammar rules used to identify bus stops were generated automatically from the schedule database they trained a statistical language model on the artificial corpus
allowing spontaneous conversational inquiries over the telephone for the train timetable and flight enquiries conversational assistant called Persona which was one of the first true personal assistant similar to what we have in recent times (like Siri, etc) Let’s Go project (Raux et al, 2003 BIBREF8 ) that was designed to provide Pittsburgh area bus information
ESPRIT SUNDIAL project (Peckham et al, 1993 BIBREF3 )
It is worthwhile to note that both the USA and the USSR had access to state-of-the-art computers, and the political support for the production of such systems meant that computers were made available to researchers in machine translation. However, the results were poor in the late 1950s, and a working system was yet to be shown. All work was therefore theoretical work implemented on a computer, which proved to be sub-optimal.
poorly funded Croatian research was lagging only a couple of years behind the research of the superpowers
Separation of the dictionary from the MT algorithm Separation of the understanding and generation modules of the MT algorithms All words need to be lemmatized The word lemma should be the key of the dictionary, Use context to determine the meaning of polysemous words.
They evaluated the majority of algorithms known at the time algorithms over meticulously prepared datasets, whose main strength was data cleaning, and by 1959 they have built a German-Russian machine translation prototype. Their approach was mainly logical, and they extended the theoretical ideas of Bar-Hillel BIBREF2 to build three algorithms: French-Russian, English-Russian and Hungarian-Russian. Their efforts resulted in the formation of the Mathematical Linguistics Seminar at the Faculty of Philology in Moscow in 1956 and in Leningrad in 1957. Their approach was mainly information-theoretic (but they also tried logic-based approaches BIBREF7), which was considered cybernetic at that time. This was the main role model for the Croatian efforts from 1957 onwards. Here, the algorithms for Indonesian-Russian, Arabic-Russian, Hindu-Russian, Japanese-Russian, Burmese-Russian, Norwegian-Russian, English-Russian, Spanish-Russian and Turkish-Russian were being built. The main approach of Andreev's group was to use an intermediary language, which would capture the meanings BIBREF7.
Dress Dress-Ls Pbmt-R Hybrid Sbmt-Sari
BLEU SARI
We then asked three volunteers to rate simplifications with respect to Fluency (the extent to which the output is grammatical English), Adequacy (the extent to which the output has the same meaning as the input sentence), and Simplicity (the extent to which the output is simpler than the input sentence) using a five point Likert scale.
Newsela BIBREF22 WikiSmall BIBREF10 WikiLarge BIBREF15
Baseline system corresponds to considering the original document as the modernized version. They used two approaches SMT and NMT and compared to the baseline, SMT showed best results.
Modern and historical versions of literature like the Bible and a Spanish novel.
Classical IMT approaches Prefix-based IMT Neural Machine Translation Prefix-based Interactive Neural Machine Translation
Dutch and Spanish
Answer with content missing: (Table 3) Best proposed result has F1 score of 0.844, 0.813, 0.870, 0.842, 0.844 compared to 0.855, 0.789, 0.852, 0.792, 0.833 on span, modality, degree, polarity and type respectively.
memorization, median report, max report
Their average F1 score was 0.874 on span detection; 08115 on contextual modality detection; 0.8695 on degree detection; 0.839 on polarity detection; 0.844 on type detection
Clinical TempEval corpus
Answer with content missing: (We then use ”PerceptronTagger” as our part-ofspeech tagger due to its fast tagging speed) PerceptronTagger.
0.6955
RB (Rule based method) CB (Common-sense based method) RB+CB+ML (Machine learning method trained from rule-based features and facts from a common-sense knowledge base) SVM classifier using the unigram, bigram and trigram features SVM classifier using word representations learned by Word2vec multi-kernel method BIBREF31 convolutional neural network for sentence classification BIBREF5
a simplified Chinese emotion cause corpus BIBREF31
the distance between a clause and an emotion words
Concatenation of three prediction output vectors
Recurrent Highway Networks NAS BIBREF5
slightly off the state of the art
At 24M, all depths obtain very similar results, reaching exp(4.065) [fixed,zerofill,precision=1] at depth 4 our best result, exp(4.188)
dropout variational dropout recurrent dropout
Answer with content missing: (Architecture section missing) The Long Short-Term Memory, Recurrent Highway Network and NAS
Transformer Big BIBREF26
WMT 2014 UGC (User Generated Content)
The outputs are ranked by human evaluators, the wins, ties and losses are counted, then the Wilcoxon signed-rank test is applied.
BLEU BIBREF28 indirect evaluation on an Aspect-Based Sentiment Analysis (ABSA) task task-related evaluation based on polysemous words
Existing online systems compared in this work are Google Translate (Feb 2019) and DeepL (Feb 2019).
an unoptimised linear-kernel SVM a keyword-based system that marks posts as positive for cyberbullying if they contain a word from existing vocabulary lists composed by aggressive language and profanity terms
Random 10 percent out of 78381 posts.
Random 90 percent out of 113698 posts.
Word INLINEFORM0 -gram bag-of-words Character INLINEFORM0 -gram bag-of-words Term lists Subjectivity lexicon features Topic model features
social networking site ASKfm
They use a lot of "us" and "them" in their vocabulary. They use a lot of mentions, and they tend to be "central" in their network. They use a lot of violent words.
online English magazine called Dabiq
frequency of tweets posted followers/following ratio users' interactions with others through using hashtags engagement in discussions using mention action