DinoStackAI/qasper-rag huggingface dataset:
33,458 Twitter users are orginally used, but than random sample of tweets is extracted resulting in smaller number or users in final dataset.
24,802 labeled tweets
SST-2
For GLUE bencmark no, for dataset MRPC, SST-B, SST-2 and COLA yes.
by balancing or, smoothing the artifacts across different classes by assigning specific weights for every sample
SNLI BIBREF0 MultiNLI BIBREF1 JOCI BIBREF13 SICK BIBREF14
Entropy algorithm is the best way to build machine learning models. Vote entropy and KL divergence are helpful for the training of machine learning ensemble classifiers.
3,685,984 unique tweets
10K sentences and 250K tokens
feedforward bidirectional LSTM (biLSTM)
rule-based tagger designed by Rashel et al. BIBREF14
IDN Tagged Corpus
11,529,432 segmented words and 20,402 characters
model did not have a flexibility in OOV words One disadvantage that the previous model had is that for unknown words the model assigned a zero vector It was noticed that the model performed better when using the vectors from different FastText models
SpaCy is an open source, Natural Language Processing library that supports a variety of tasks, including POS Tagging, Named Entity Recognition, Dependency Parsing, etc. SpaCy uses sophisticated neural network-based models
Information about the dataset includes the tokens of a set of articles and their position in a sentence, the lemma and the part of speech of every token The label map supports 16 standard part of speech tags: Adjective, Adposition, Adverb, Coordinating Conjuction, Determiner, Interjection, Noun, Numeral, Particle, Pronoun, Proper Noun, Punctuation, Subordinating Conjuction, Symbol, Verb and Other. Each tag describes morphological features of the word, depending on the part of the speech to which it refers like the gender, the number, and the case
a sample of $10,417$ users from a database containing more than 400 million publicly available tweets posted by more than $100,000$ users who have announced their pregnancy on Twitter Two professional annotators manually categorized the $10,417$ users as "bot," "non-bot," or "unavailable," based on their publicly available Twitter sites Users were annotated broadly as "bot" if, in contrast to users annotated as "non-bot," they do not appear to be posting personal information Users were annotated as "unavailable" if their Twitter sites could not be viewed at the time of annotation
Using a machine learning algorithm on top of an existing bot detection system, and a set of simple derived features, we were able to significantly improve bot detection performance in health-related data.
Social networks encapsulate large volumes of data associated with diverse health topics, generated by active user bases in continuous growth. Social media data is thus being widely used for health-related research, for tasks such as adverse drug reaction detection BIBREF0, syndromic surveillance BIBREF1, subject recruitment for cancer trials BIBREF2, and characterizing drug abuse BIBREF3, to name a few.
conversations, which consist of at least one question and one free-form answer
pre-trained version of BERT without special emoji tokens
2000 tuples
2000 tuples collected by BIBREF24 that are sourced from Twitter
Light Gradient Boosting Machine (LGBM)
363,078 structured abstracts
The new dataset was collected from structured abstracts from PubMed and filtering abstract headings representative of the desired categories.
automatic labeling lemmatization of the abstract section labels in order to cluster similar categories manually looked at a small number of samples for each label to determine if text was representative
using a keyword-based approach would yield a sequence labeled population and methods with the label P, but such abstract sections were not purely about the population and contained information about the interventions and study design making them poor candidates for a P label. Moreover, in the dataset from BIBREF12 , a section labeled as P that contained more than one sentence would be split into multiple P sentences to be included in the dataset.
For some language yes, but not for another.
The subject and verb in (UNKREF3) agree for number, while the noun and determiner in (UNKREF4), the noun and attributive adjective in (UNKREF5) and the subject and predicated adjective in (UNKREF6) agree for both number and gender.
Amazon, Yelp, IMDB MR BIBREF16 MPQA BIBREF17 Subj BIBREF18 TREC BIBREF19
word-LSTM BIBREF20, word-CNN BIBREF21 and a fine-tuned BERT BIBREF12 base-uncased classifier
New best result is F1 score of 0.752 compared to 0.744 of the best previous work.
BIBREF12 buschmeier-cimiano-klinger:2014:W14-26
All Sarcasm case assumes that every instance is sarcastic Random case randomly assigns each instance as sarcastic or non-sarcastic
Data was taken from two domains: Twitter, and Amazon product reviews.
Twitter product reviews containing exactly one of the trailing hashtags “#sarcasm,” “#happiness,” “#sadness,” “#anger,” “#surprise,” “#fear,” and “#disgust”, and Amazon product reviews from the publicly available sarcasm corpus developed by Filatova FILATOVA12.661.
LSTMs and GRU with attention which achieved 0.89 F1 score
Two SVMs, one with linear kernel and the other with a radial basis function kernel (RBF) The third classifier trained was another linear classifier with Stochastic Gradient Descent (SGDC) learning The final classifier was two models based on the Bayes theorem: Multinomial Naïve Bayes, which works with occurrence counts, and Bernoulli Naïve Bayes, which is designed for binary features Pooled GRU Stacked LSTM with Attention LSTM and GRU with Attention 2D Convolution with Pooling GRU with Capsule LSTM with Capsule and Attention BERT
LSTM and GRU with Attention can be considered as the best model trained for OGTD
Three, plus 2 in case of disagreement below 66%.
Two SVMs, one with linear kernel and the other with a radial basis function kernel (RBF) linear classifier with Stochastic Gradient Descent (SGDC) learning The final classifier was two models based on the Bayes theorem: Multinomial Naïve Bayes, which works with occurrence counts, and Bernoulli Naïve Bayes, which is designed for binary features Pooled GRU Stacked LSTM with Attention LSTM and GRU with Attention 2D Convolution with Pooling GRU with Capsule LSTM with Capsule and Attention BERT
The final Catalan corpus contains 567 annotated reviews and the final Basque corpus 343.
Given a paragraph, our system produces a GF program (a pair of an abstract and a concrete syntax), which can be used for sentence generation. The system consists of two components, understanding sentences and generating GF grammar. The first component is divided into two sub-components, one for recognizing the sentence structure and one for recognizing the sentence components. The second component consists of a GF grammar encoder and a GF grammar exporter. The encoder is responsible for generating a GF grammar for each sentence, while the exporter aggregates the grammars generated from the encoder, and produces a comprehensive grammar for the whole paragraph.
The first application is to build a natural language description of the ontologies built in an evolutionary biology project called Phylotastic, so that biologists can understand the output, without knowledge of ontologies. The second aims to create an abstract or intermediate representation of the Wikipedia pages from the BlueSky session in 2018.
standard stacked character-based LSTM BIBREF4
By applying hierarchical clustering on language vectors found during training
Procedure INLINEFORM0 takes the narrative description, performs tokenization and stop-word removal and puts it into an array of words. Procedures INLINEFORM0 and INLINEFORM1 get LLTs and create a dictionary of words and of their stemmed versions, respectively By the functional notation INLINEFORM0 (resp., INLINEFORM1 ), we refer to the set of LLTs containing the word INLINEFORM2 (resp., the stem of INLINEFORM3 ). Function INLINEFORM0 returns the stemmed version of word INLINEFORM1 . Function INLINEFORM0 returns the position of word INLINEFORM1 in term INLINEFORM2 . INLINEFORM0 is a flag, initially set to 0, which holds 1 if at least a stemmed matching with the MedDRA term INLINEFORM1 is found. INLINEFORM0 , INLINEFORM1 , INLINEFORM2 are arrays and INLINEFORM3 appends INLINEFORM4 to array INLINEFORM5 , where INLINEFORM6 may be an element or a sequence of elements. INLINEFORM0 ( INLINEFORM1 ) are the weights related to the criteria defined in Section UID23 . Procedure INLINEFORM0 performs the multi-value sorting of the array INLINEFORM1 based on the values of the properties INLINEFORM2 of its elements. Procedure INLINEFORM0 , where INLINEFORM1 is a set of terms and INLINEFORM2 is a term, tests whether INLINEFORM3 (considered as a string) is prefix of a term in INLINEFORM4 . Dually, procedure INLINEFORM5 tests if in INLINEFORM6 there are one or more prefixes of INLINEFORM7 , and eventually remove them from INLINEFORM8 . Function INLINEFORM0 specifies whether a word INLINEFORM1 has been already covered (i.e., a term voted by INLINEFORM2 has been selected) in the (partial) solution during the term release: INLINEFORM3 holds 1 if INLINEFORM4 has been covered (with or without stemming) and it holds 0 otherwise. Procedures INLINEFORM0 and INLINEFORM1 , where INLINEFORM2 is a set of terms, implement ordered-phrases and maximal-set-of-voters criteria (defined in Section UID28 ), respectively. Function INLINEFORM0 , returns the first INLINEFORM1 elements of an ordered set INLINEFORM2 . If INLINEFORM3 , the function returns the complete list of ordered terms and INLINEFORM4 nil values.
The main idea of INLINEFORM0 is that a single linear scan of the free-text is sufficient, in order to recognize INLINEFORM1 terms. INLINEFORM0 scans the text word-by-word (remember that each word corresponds to a token) once and performs a “voting task”: at the INLINEFORM1 -th step, it marks (i.e., “votes”) with index INLINEFORM2 each LLT INLINEFORM3 containing the current ( INLINEFORM4 -th) word of the ADR description.
a recovery approach as leading principle for national or global health service strategies, should be informed by voices of individuals expect the proposed investigations of views on recovery by previously under-researched ethnic, language, and cultural groups to yield valuable insights on the appropriateness of the recovery approach for a wider population The datasets collected in this project can serve as useful resources for future research
language identification
For Twitter and Reddit users , implicit consent is assumed to use their public tweets. Blog users are  contacted to obtain consent for using their texts.
Twitter and Reddit users with a BD diagnosis will be identified automatically via self-reported diagnosis statements, such as `I was diagnosed with BD-I last week'.
Two major forms(Jueju and Lvshi) of SHI and 121 major forms of CI from Chinese Classical Poerty Corpus (CCPC1.0)
two primary categories, SHI and CI SHI and CI can be further divided into many different types
RNN and Transformer baseline systems utilize BPE BIBREF3 to transfer the original word sequence to subword sequence on both the source and the target sides
correlation between the word similarity scores by human judgment and the word distances in vector space select the semantically closest word, from the candidate answers
online news websites New York Times Japan Times
FirstPhrases baseline MultipartiteRank BIBREF17
CopySci was trained using scientific abstracts (KP20k) and CopyNews using newspaper articles (KPTimes)
news articles are annotated in a semi-automatic way first the editors revise a set of tags proposed by an algorithm provide additional tags which will be used by a taxonomy team to improve the algorithm
6 layers
DUC-2001 dataset BIBREF6 Inspec dataset NUS Keyphrase Corpus BIBREF10 ICSI Meeting Corpus
F1 score their system achieved is 27.53, 27.01, 4.30 and 9.10 on DUC, Inspec, ICSI and NUS dataset respectively.
SingleRank and Topical PageRank
we conduct an ablation study on IWSLT14 De-En task. As shown in Table TABREF18, the hints from word alignments provide an improvement of about 1.6 BLEU points, and the hints from hidden states improve the results by about 0.8 BLEU points. We also test these models on a subsampled set whose source sentence lengths are at least 40. Our model outperforms the baseline model by more than 3 BLEU points (20.63 v.s. 17.48).
While the ART models have achieved great success in terms of translation quality, the time consumption during inference is still far away from satisfactory
BLUE and the percentage of repetitive words
a document's terms' minimum, maximum, average (relative to all terms and to in-vocabulary terms), and standard deviation of weights; and proportion of terms that are in-vocabulary
three representations obtained comparative results and support the robustness of the low dimensionality representation
No, the answers can also be summaries or yes/no.
Compositional Neural Network Element-wise Multiplicative Composition Neural Tensor Network
we use the framework of SGNN, and only replace their input event embeddings with our intent and sentiment-enhanced event embeddings
ATOMIC hard similarity small and big dataset the transitive sentence similarity dataset the standard multiple choice narrative cloze (MCNC) dataset
They did not report results for English but expect that morphologically complex languages will perform better.
human annotators are asked to judge how similar two words are on a fixed scale. Model word vectors are evaluated based on ranking the word pairs according to their cosine similarity, and then measuring the correlation (using Spearman's $\rho $ ) between model judgments and human judgments
15.6 and 16.5 for accuracy and NDCG on MCTest-150, 7.3 and 4.6 on MCTest-500.
Addition Addition-proj Neural Reasoner Attentive Reader
one-stage RNN system containing 2-layer BLSTM
relative accuracy (ACC) of the two multi-stage systems increases by 10% on every task relative to the baseline accuracy is high in several dialects' recognition, such as Shanghai (98.8%) and Hefei (99.8%), but the systems have some trouble while recognizing Minnan and Kekka, Hebei and Shanxi
Ningxia, Hefei, Sichuan, Shanxi, Changsha, Hebei, Nanchang, Shanghai, Kekka and Fujian
SGNMT tool BIBREF13 , BIBREF14 with OpenFST backend BIBREF8
goal on the restricted track was to explore the potential of purely neural models for grammatical error correction
Overall, while modular does a better job at reducing bias, concurrent appears to better preserve the meaning and fluency of the original text.
Wiki community effort
algorithm is given an input sentence and must produce an output sentence whose meaning is as similar as possible to the input but with the subjective bias removed
We present a deep learning based approach for ASL recognition that leverages skeletal and video data. The proposed model captures the underlying temporal dynamics associated with sign language and also identifies specific hand shape patterns from video data to improve recognition performance.
Best performing model is Spatial AI-LSTM with accuracy 81% and Std. Deviation 6%
Recurrent Neural Networks (RNN) 3D Convolutional Neural Network Axis Independent LSTM Spatial AI-LSTM Max CNN-LSTM network
Chung et al. (2016)
Finnish, Turkish, Czech, English, German, Hindi, Portuguese, Russian, Spanish, Urdu, Arabic, and Hebrew
The phenomena where words that have the same form express different morphological cases
Seq2Seq BIBREF25, BIBREF24 Variational Auto-Encoder (VAE) BIBREF26 Reinforcement Learning (RL)
race, ethnicity, national origin, religious affiliation, sexual orientation, caste, sex, gender, gender identity, and serious disease or disability.
AllText F8W
semi-supervised ESC model
Akbik et al. (2019) -  89.3 on Ontonotes 5
Baevski et al. (2019) 93.5 on CoNLL-03
Neural CRF model with and without ELMo embeddings
process the official dumps into tuples of entity and type based only on the left and right part of the instance_of triplet Each entity is associated with a set of aliases, we keep only the aliases that are less than seven tokens long we use the sitelink count to keep the six most popular types To move from fine-grained to coarse-grained types, we use the Wikidata hierarchical structure
LibriSpeech BIBREF46
Acoustic factors such as speaker characteristics, microphone characteristics, background noise.
For the first task they have two baseline models, TF-IDF and LSI which both use cosine similarity. For the QA task, they baseline models were the original CNN and CNN with separate TF-IDF, LSI features.
Adding more features to the traditional sets such as TF-IDF, BM25 and PL2F as well as using voting in a ranking system help to improve accuracy on a legal question answering task
ast-20h: 20 hours,
zh-ai-small: 20 hours,
zh-ai-large: 150 hours,
zh-ai-hanzi: 150 hours,
hr-gp: 12 hours,
sv-gp: 18 hours,
pl-gp: 19 hours,
pt-gp: 23 hours,
fr-gp: 25 hours,
zh-gp: 26 hours,
cs-gp: 27 hours,
multilin6: 124 hours
two CNN layers three-layer bi-directional long short-term memory network (LSTM) followed by a three-layer LSTM
Spanish English Chinese Mandarin Chinese Croatian Czech French Polish Portuguese Swedish
Yes. They show results on the Wall Street Journal Corpus, which consists of recordings of real speech.
They use four classifiers: ZeroR, Naive Bayes, J48, and random forest.
316 sentences in Hypertension corpus, 877 sentences in Rhinosinusitis corpus
Conditions that affect erythrocyte turnover and hemoglobin variants must be considered, particularly when the A1C result does not correlate with the patient's clinical situation
ter bleu rouge nist lepor cider meteor Semantic Text Similarity Flesch Reading Ease characters per utterance (len) and per word (cpw) words per sentence syllables per sentence (sps) and per word (spw) polysyllabic words per utterance (pol) and per word (ppw) the number of misspellings (msp) and the parsing score as returned by the Stanford parser (prs)
utterance generation for spoken dialogue systems provide information about hotels and restaurants in San Francisco provides information about restaurants in Cambridge
CLASSY04, ICSI, Submodular, DPP and RegSum.
Rouge-1 recall, Rouge-2 recall, Rouge-4 recall
it represents sentences as bag-of-word (BOW) vectors with TF-IDF weighting and uses a centroid of these vectors to represent the whole document collection
All words in the vocabulary are ranked by their value in the centroid vector. Then the ranked list of sentences is de-queued in decreasing order.
The twitter dataset is English-only; no information for the reddit dataset is given
Mean of occurrence per utterance and the standard deviation is calculated for every marker type; the means between each pair of types is compared via independent t-tests
Support Vector Machines (SVM) classifier with linear kernel BIBREF16
collected using hashtags, such as #irony, #sarcasm, and #sarcastic
a sequence of pretrained embeddings for the surface forms of the sentence tokens annotations for a single predicate CoNLL 2009 dataset
semantic role labeling portion of the CoNLL-2009 shared task BIBREF0
For each of the shared task languages,  they produced GloVe vectors BIBREF7 from the news, web, and Wikipedia text of the Leipzig Corpora Collection and trained 300-dimensional vectors then reduced them to 100 dimensions with principal component analysis for efficiency.
Catalan, Chinese, Czech, English, German, Japanese and Spanish
ot all sentences in the current conversation session are equally important irrelevant to the current context, and should not be considered when the computer synthesizes the reply
real-world chatting corpus from DuMi unlabeled massive dataset of conversation utterances
For domain relevance, we take the seed question set as the in-domain data $D_{in}$ , the domain relevance of expanded question $q$ is defined as:

$$\textsc {Rel}(q) = \cos (v(q),v(D_{in}))$$ (Eq. 7)

where $v(\cdot )$ is the document embedding defined as the averaged word embedding within the document.
106 163
For fluency, we define the averaged language model score as:

$$\textsc {AvgLM}(q) = \frac{\textsc {Lm}(q)}{\textsc {Len}(q)}$$ (Eq. 8)

where $\textsc {Lm}(\cdot )$ is the general-domain language model score (log probability), and $\textsc {Len}(\cdot )$ is the word count.
Post-match interviews for tennis singles matches from ASAP Sport's website with match information from a dataset provided by Tennis-Data
XKCD color dataset Caltech–UCSD Birds dataset actions and messages generated by pairs of human Amazon Mechanical Turk workers playing the driving game
data was collected using crowdsourcing where speakers were recorded saying random ordered phrases for each intent twice
MAE: Mean Absolute Error Accuracy$\pm k$
Craigslist Bargaining dataset (CB)
Identify  POS tags for each sentence,  check whether one sentence from the corpus has the same sequence of POS tags. If the same POS sequence has been found, that points in a certain direction, if not found, the evaluation for that sentence is performed manually.
The quality of sentence transition was measured manually by checking how well do consecutive sentences connect
3857 speech segments from 53 US Congressional floor debates
The manual evaluation contains 4 criteria to check grammatical correctness, sentence transitions, speech structure, and speech content of the generated speech and assigning a score between 0 to 3 for each criterion
General Language Understanding question answering task (SQuAD v1.1 - BIBREF14) classification task (IMDb sentiment classification - BIBREF13)
on 8 16GB V100 GPUs for approximately 90 hours
LibriSpeech corpus BIBREF46 GlobalPhone corpus BIBREF47
They compare MAP performance of query-by-example STD using representations obtained from naive encoder and their method
English paired with any of the following: French, German, Czech, Spanish.
BiLSTM based framework and the linear classifier reaches a 97.97% accuracy SVM classifier reaches a remarkable 98.11% accuracy
They simply split document in chunks, get embedding for each chunk and train BiLSTM models with embeddings.
The character-aware neural language model BIBREF1 Hierarchical attention networks BIBREF10 FastText BIBREF11
En-De De-En Zh-En Englsh-Russian and Russian-English
Irish, Tibetian, Spanish, Hindi, Marathi, Gujarati, Telugu, Tamil, Hinglish, Bengali,Arabic, French, German, Odia
Answer with content missing: (Table1 merged with Figure 3) Joint Neural
Embedding (JNE) and AdaMine
Answer with content missing: (Table1 part of Figure 3):
Proposed vs Best baseline result
- Median Rank: 2.9 vs 3.0 (lower better)
- Rank 1 recall: 34.6 vs 33.1 (higher better)
synthetic emails generated by Dada engine
In terms of macro F1 score their model has 0.65 compared to 0.58 of best other model.
Changing the word order of the translation so it is in the right order of the target language.
combining a language model (LM) with cross-lingual word embedding Let $f$ be a source word in the current position and $e$ a possible target word. Given a history $h$ of target words before $e$ , the score of $e$ to be the translation of $f$ would be:

$ L(e;f,h) = \lambda _\text{emb}\log q(f,e) + \lambda _\text{LM}\log p(e|h) $

Here, $q(f,e)$ is a lexical score defined as:

$ q(f,e) = \frac{d(f,e) + 1}{2} $

where $d(f,e) \in [-1,1]$ is a cosine similarity between $f$ and $e$ . Accumulating the scores per position, we perform a beam search to allow only reasonable translation hypotheses.
PDTB 2.0
F-measures of ROUGE-1, ROUGE-2 and ROUGE-SU4
Topics were taken from category names in Google News, tags on Twitter, event archives on the Web. News articles were taken from news websites.
Each topic is assigned to 4 experts
The dataset contains 19k annotated aspect facets, 45 topics, 6 predefined categories, 450 news document, 180 model summaries. The length limit of the model summary is 100 words (slitted by space). 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