DinoStackAI/qasper-rag huggingface dataset:
a rule-based unigram matching baseline, which takes the entry with highest unigram overlap with the query string to be the top match
Trained using SpaCy and fine-tuned with their data of hotel and location entities
Information  from users and information  from database of approximately 100,000 cities and 300,000 hotels, populated using data from their partners.
The most common intents are thanks, cancel, stop, search, and unknown
US Petition dataset CNN/DailyMail corpus 385 documents from the Wall Street Journal
Observing that our model generally has better recall (Table TABREF17) and its summaries tend to be more verbose (e.g. second example in Figure FIGREF20) We are able to reproduce the performance of the baseline model (“CNN w/ GloVe”), and find that once again, adding the shallow discourse features improves results.
semantic relatedness (SICK, BIBREF17 ), paraphrase detection (MSRP, BIBREF19 ), question-type classification (TREC, BIBREF20 ), and five benchmark sentiment and subjective datasets, which include movie review sentiment (MR, BIBREF21 , SST, BIBREF22 ), customer product reviews (CR, BIBREF23 ), subjectivity/objectivity classification (SUBJ, BIBREF24 ), opinion polarity (MPQA, BIBREF25 ), semantic textual similarity (STS14, BIBREF18 ), and SNLI BIBREF13
Amazon Review dataset BIBREF26 with 142 million sentences, about twice as large as BookCorpus
interviewing individuals involved in government ($n=5$)
20194 cleaned, unique tweets identified as either hateful and not hateful from previous research BIBREF22
Volunteers submitted many of these positivitweets through an online form
The text analysis models classify a tweet by using, as features, the outputs from Perspective API from Jigsaw BIBREF10, HateSonar BIBREF11, and VADER sentiment models BIBREF12
English French
The cache language models are based on the assumption that the word in recent history are more likely to appear again conditional probability of a word is calculated by interpolating the output of standard language model and the probability evaluated by caching
FNNLM RNNLM BiRNN LSTM
gate recurrent unit (GRU) RNNLM, dropout strategy for addressing overfitting, character level neural network language model and ect.
1).But   2).Eng  3). A-But-B
BLEU perplexity self-BLEU percentage of $n$ -grams that are unique
Face tracking is performed in an automatic tracklet module, face identification is performed by creating a face embedding from the output of a CNN, the embedding is then compared to a gallery of each person's face using a discriminative classifier (SVM) and localization is modelled with a complex angular central Gaussian model. All are merged in a statistical model.
The baseline system was a conventional speech recognition approach using single-output beamforming.
LSTM-Att BIBREF7 , a LSTM model with spatial attention MemAUG BIBREF33 : a memory-augmented model for VQA MCB+Att BIBREF6 : a model combining multi-modal features by Multimodal Compact Bilinear pooling MLAN BIBREF11 : an advanced multi-level attention model
Visual7W a collection of open-domain visual question-answer pairs
Word embeddings from knowledge triples (subject, rel, object) from ConceptNet are fed to an RNN
an open multilingual knowledge graph containing common-sense relationships between daily words
They combine content- and graph-based methods in new ways.
Early fusion, late fusion, hybrid fusion.
Top graph based features are: Coreness Score, PageRank Centrality, Strength Centrality, Vertex Count, Closeness Centrality, Closeness Centrality, Authority Score, Hub Score, Reciprocity and Closeness Centrality.
Overton learns the accuracy of these sources using ideas from the Snorkel project
Code-free Deep Learning Multitask Learning Weak Supervision
Applications are customized by providing supervision in a data file that conforms to the schema
modified version of standard precision, recall, and F INLINEFORM0 , as proposed by reschke2014 mean reciprocal rank (MRR)
Reschke CRF Reschke Noisy-OR Reschke Best
80 plane crash events, each paired with a set of related news articles
Stanford Plane Crash Dataset BIBREF15
The performances of our final model and other baseline models are illustrated in Table TABREF34.
Special Token Replacement Quotes Fixing Recaser Patch-up
background corpus of 400 qualitative knowledge sentences
BERT (IR) BERT (IR upper bound) BERT-PFT (no knowledge) BERT-PFT (IR)
qualitative relations were manually extracted by the authors from a large corpus asked to annotate the two properties being compared asked to author a situated, 2-way multiple-choice (MC) question that tested this relationship asked to validate its answer and quality asked to generate a new question by “flipping” the original so the answer switched
the Span-Attribute Tagging (SAT) model
A2C and REINFORCE-joint for joint policy optimization achieve the improvement over SL baseline of 29.4% and  25.7 susses rate,  1.21  AND 1.28 AvgRevard and  0.25 and  -1.34 AvgSucccess Turn Size, respectively.
Similar to the design of the dialog agent, state of the user simulator is maintained in the state of an LSTM. State of the dialog agent is maintained in the LSTM BIBREF35
Supervised methods are used to identify the dish and ingredients in the image, and an unsupervised method (KNN) is used to create the food profile.
The study features a radar chart describing inclinations toward particular cuisines, but they do not perform any experiments
a encoder-decoder architecture with attention incorporating LSTMs and transformers
219,777 114,243 89,413 over 5M
German-English, English-French, Czech-English, Basque-English pairs
ULMFiT BIBREF21
RandomForestRegressor LinearSVR KNeighborsRegressor Support Vector Machine Regression
Answer with content missing: (Table 2): Krippednorff's alpha coefficient for dataset is: Stability -0.26, Extraversion 0.07, Openness 0.36, Agreeableness 0.51, Conscientiousness 0.31
seq(9:1, 20, equi) - 9:1 training:test ratio, 20 equidistant samples, seq(9:1, 10, equi) - 9:1 training:test ratio, 10 equidistant samples, seq(2:1, 10, semi-equi) - 2:1 training:test ratio, 10 samples randomly selected out of 20 equidistant points.
xval(9:1, strat, block) - 10-fold, stratified, blocked; xval(9:1, no-strat, block) - 10-fold, not stratified, blocked; xval(9:1, strat, rand) - 10-fold, stratified, random selection of examples.
Albanian, Bulgarian, English, German, Hungarian, Polish, Portughese, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish
negative neutral positive