DinoStackAI/qasper-rag huggingface dataset:
The embeddings are learned several times using the training set, then the average is taken.
The data from collected travel surveys is used to model travel behavior.
The coefficients are projected back to the dummy variable space.
Proposed ORNN has 0.769, 1.238, 0.818, 0.772 compared to 0.778, 1.244, 0.813, 0.781 of best state of the art result on Mean Absolute Error (MAE), macro-averaged Mean Absolute Error (MAEM ), binary classification accuracy (Acc.) and weighted binary classification accuracy (Wt. Acc.)
re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones
The lack of background Non-cursing aggressions and insults the presence of controversial topic words shallow meaning representation directly ask the suspected troll if he/she is trolling or not a blurry line between “Frustrate” and “Neutralize” distinction between the classes “Troll” and “Engage”
inclusion of longer parts of the conversation
1000 conversations composed of 6833 sentences and 88047 tokens
between 0.81 and 0.88
The MCD splits achieve a significantly higher compound divergence at a similar atom divergence when compared to the other experiments
CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets
LSTM+attention Transformer Universal Transformer
239,357 English question-answer pairs
random Output length Input length Output pattern Input pattern
CSKS task
Honk DeepSpeech-finetune
English Hindi
Difference is around 1 BLEU score lower on average than state of the art methods.
NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM
WMT2014, WMT2016 and IWSLT-2014
visual attention is very sparse visual component of the attention hasn't learnt any variation over the source encodings
existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT
Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer
Reasoner model, also implemented with the MatchLSTM architecture Ranker model
Reasoner learns to extract the linking entity from chains selected by a well-trained Ranker, and it benefits the Ranker training by providing extra rewards
Answer with content missing: (formula) The accuracy is defined as the ratio # of correct chains predicted to # of evaluation samples
empirically test to what extent changes in the evaluation design affect the outcome of the human evaluation
English Chinese
Choose professional translators as raters Evaluate documents, not sentences Evaluate fluency in addition to adequacy Do not heavily edit reference translations for fluency Use original source texts
MT developers to which crowd workers were compared are usually not professional translators, evaluation of sentences in isolation prevents raters from detecting translation errors, used not originally written Chinese test set
1.08 points in ROUGE-L over our base pointer-generator model 0.6 points in ROUGE-1
Using model gradients with respect to input features they presented that the most important model inputs are verbs associated with entities which shows that the model attends to shallow context clues
In four entity-centric ways - entity-first, entity-last, document-level and sentence-level
With both test sets performances decrease, varying between 94-97%
6)Contributor first names 7)Contributor last names 8)Contributor types ("soprano", "violinist", etc.) 9)Classical work types ("symphony", "overture", etc.) 10)Musical instruments 11)Opus forms ("op", "opus") 12)Work number forms ("no", "number") 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp") 14)Work Modes ("major", "minor", "m")
standard linguistic features, such as Part-Of-Speech (POS) and chunk tag series of features representing tokens' left and right context
biLSTM-networks
two parallel convolutional networks, INLINEFORM0 , that share the same set of weights
Egyptian (EGY) Levantine (LEV) Gulf (GLF) North African (NOR)
Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march
in an ME game there are typically several interpretive biases at work: each player has her own bias, as does the Jury
11'248
does not just consider the question tokens, but also the relationship between those tokens and the properties
information retrieval system word-association method CCG-style rule-based semantic parser written specifically for friction questions state-of-the-art neural semantic parser
workers were given a seed qualitative relation asked to enter two objects, people, or situations to compare created a question, guided by a large number of examples LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions
jointly trained with slots
ACL Anthology Reference Corpus
F-score micro-F macro-F weighted-F
precision recall F-measure
eight GB
There are 6 simulated datasets collected which is initialised with a corpus of size 550 and simulated by generating new documents from Wikipedia extracts and replacing existing documents
WMD VSM PV-DTW PV-TED
$4th$
dialectal tweet data
Modern Standard Arabic (MSA) MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine
Author profiling and deception detection in Arabic LAMA+DINA Emotion detection Sentiment analysis in Arabic tweets
rating questions on a scale of 1-5 based on fluency of language used and relevance of the question to the context
reviews under distinct product categories are considered specific domain knowledge
SemEval 2018 Task 3 BIBREF20 BIBREF4 SARC 2.0 SARC 2.0 pol Sarcasm Corpus V1 (SC-V1) Sarcasm Corpus V2 (SC-V2)
Twitter Reddit Online Dialogues
A bi-LSTM with max-pooling on top of it
all caps quotation marks emoticons emojis hashtags
As the question has integrated previous utterances, the model needs to directly relate previously mentioned concept with the current question. This is helpful for concept carry-over and coreference resolution.
a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
Improved AECNN-T by 2.1 and AECNN-T-SM BY 0.9
Sociodemographics: gender, age, marital status, etc. Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc. Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.
features per admission were extracted as inputs to the readmission risk classifier
EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA
automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15
potentially indicating a shining through effect explicitation effect
allows the annotator to define each markable as a certain mention type (pronoun, NP, VP or clause) The mentions referring to the same discourse item are linked between each other. chain members are annotated for their correctness
first two systems are transformer models trained on different amounts of data The third system includes a modification to consider the information of full coreference chains
shining through explicitation
analogy query analogy browsing
Semantic similarity structure Semantic direction structure
three main components, namely data processing, task processing, and query processing
Task processing: converting data exploration tasks to algebraic operations on the embedding space Query processing: executing semantic query on the embedding space and return results
Syn Dep OpenIE SRL BiDAF QANet BERT NAQANet NAQANet+
neural network-based models can outperform feature-based models with wide margins contextualized representation learning can boost performance of NN models
CAEVO
BIBREF16
IEMOCAP
set of related tasks are learned (e.g., emotional activation) primary task (e.g., emotional valence)
low-frequency words
CMRC-2017 People's Daily (PD) Children Fairy Tales (CFT) Children's Book Test (CBT)
AS Reader, GA Reader, CAS Reader
They were able to create a language model from the dataset, but did not test.
extracted text from Sorani Kurdish books of primary school and randomly created sentences
2000 sentences
we observe a positive correlation between retweeting and co-voting strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union significantly negative coefficient, is the area Economic and monetary system
Greens-EFA, S&D, and EPP exhibit the highest cohesion non-aligned members NI have the lowest cohesion, followed by EFDD and ENL two methods disagree is the level of cohesion of GUE-NGL
By visualizing syntactic distance estimated by the parsing network
Penn Treebank Text8 WSJ10
BPC, Perplexity
they are used as additional features in a supervised classification task
A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18
Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question.
Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions.
They think it will help human TCM practitioners make prescriptions.
A pointer network decodes the answer from a bidirectional LSTM with attention flow layer and self-matching layer, whose inputs come from word and character embeddings of the query and input text fed through a highway layer.
Morphological analysis is the task of creating a morphosyntactic description for a given word inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form
DyNet
the Otomanguean language family
hand-curated collection of complete inflection tables for 198 lemmata
They achieved best result in the PAN 2017 shared task with accuracy for Variety prediction task 0.0013 more than the 2nd best baseline, accuracy for Gender prediction task 0.0029 more than 2nd best baseline and accuracy for Joint prediction task 0.0101 more than the 2nd best baseline
Gender prediction task
Variety prediction task
simply split the image into two parts. One for the text input, and the other for the tabular data
from the Database of Modern Icelandic Inflection (DMII) BIBREF1
a reference corpus of 21,093 tokens and their correct lemmas
Transformer generation model
The training dataset is augmented by swapping all gendered words by their other gender counterparts
Gendered characters in the dataset
RNNLM BIBREF11
Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained.
Informative are those that will not be suppressed by regularization performed.
softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary
MADL BIBREF0 extends the dual learning BIBREF1, BIBREF2 framework by introducing multiple primal and dual models.
German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh
1.34 and 1.12 BLEU score on top of the strong baselines in BIBREF3 perplexity scores are also better On the Google Production dataset, our model achieved 1.01 higher test BLEU score
Perpexity is improved from 34.7 to 28.0.
varied the number of experts between models
DISPLAYFORM0 DISPLAYFORM0 DISPLAYFORM1
Accuracy of best interpretible system was 0.3945 while accuracy of LSTM-ELMo net was 0.6818.
[error, correction] pairs
Levenshtein distance metric BIBREF8 diacritical swapping Levenshtein distance is used in a weighted sum to cosine distance between word vectors ELMo-augmented LSTM
spellchecking mammography reports and tweets BIBREF7 , BIBREF4
to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness
BLEU scores and the slot error rate (ERR)
$({1})$ SC-LSTM BIBREF3 $({2})$ GPT-2 BIBREF6 $({3})$ HDSA BIBREF7
WMT 2019 parallel dataset a restricted dataset containing the full TED corpus from MUST-C BIBREF10 sampled sentences from WMT 2019 dataset
BLEU and TER scores
32.25% of gang members in our dataset have chained together the police and the pistol emoji, compared to just 1.14% of non-gang members only 1.71% of non-gang members have used the hundred points emoji and pistol emoji together in tweets while 53% of gang members have used them gang members have a penchant for using just a small set of emoji symbols that convey their anger and violent behavior
76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre
user holds or points weapons, is seen in a group fashion which displays a gangster culture, or is showing off graffiti, hand signs, tattoos and bulk cash
Although cursing is frequent in tweets, they represent just 1.15% of all words used BIBREF21 . In contrast, we found 5.72% of all words posted by gang member accounts to be classified as a curse word gang members talk about material things with terms such as got, money, make, real, need whereas ordinary users tend to vocalize their feelings with terms such as new, like, love, know, want, look, make, us
Manual verification
Emotion Sensor Feature, Part of Speech, Punctuation, Sentiment Analysis, Empath, TF-IDF Emoticon features
Systems do not perform well both in Facebook and Twitter texts
Majority Class baseline (MC) Random selection baseline (RAN)
News Articles Twitter
$ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $
WN18, FB15k
The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents.
PV-DM
String length Words co-occurrences Stems co-occurrences MeSH similarity
window_size alpha sample dm hs vector_size
This approach considers related images
It eliminates non-termination in some models fixing for some models up to 6% of non-termination ratio.
There are is a strong conjecture that it might be the reason but it is not proven.
NDCG MRR recall@k mean rank
DL-61
ensemble model
133,287 images
verb/preposition-based relation, nominal attribute, descriptive phrase and hyponymy relation.
restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information
3,200 sentences
We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.
In the MULT method, two datasets are simultaneously trained, and the weights are tuned based on the inputs which come from both datasets. The hyper-parameter $\lambda \in (0,1)$ is calculated based on a brute-force search or using general global search. This hyper parameter is used to calculate the final cost function which is computed from the combination of the cost function of the source dataset and the target datasets. this paper proposes to use the most relevant samples from the source dataset to train on the target dataset. One way to find the most similar samples is finding the pair-wise distance between all samples of the development set of the target dataset and source dataset. we propose using a clustering algorithm on the development set. The clustering algorithm used ihere is a hierarchical clustering algorithm. The cosine similarity is used as a criteria to cluster each question and answer. Therefore, these clusters are representative of the development set of the target dataset and the corresponding center for each cluster is representative of all the samples on that cluster. In the next step, the distance of each center is used to calculate the cosine similarity. Finally, the samples in the source dataset which are far from these centers are ignored. In other words, the outliers do not take part in transfer learning.
The system extends BiDAF BIBREF4 with self-attention
document-level variants of the SQuAD dataset
Author's own DCG rules are defined from scratch.
cases of singular/plural, subject pronoun/object pronoun, etc.
disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2% this operation vary across tasks
single head disabling a whole layer, that is, all 12 heads in a given layer
nouns verbs pronouns subjects objects negation words special BERT tokens
MRPC STS-B SST-2 QQP RTE QNLI MNLI
2.7 accuracy points
SNAP (Stanford Network Analysis Project)
accuracy with standard deviation
Corpus 5KL Corpus 8KF
Datasets used are Celex (English, Dutch), Festival (Italian), OpenLexuque (French), IIT-Guwahati (Manipuri), E-Hitz (Basque)
Authors report their best models have following accuracy: English CELEX (98.5%), Dutch CELEX (99.47%), Festival (99.990%), OpenLexique (100%), IIT-Guwahat (95.4%), E-Hitz (99.83%)
CELEX (Dutch and English) - SVM-HMM
Festival, E-Hitz and OpenLexique - Liang hyphenation
IIT-Guwahat - Entropy CRF
Arabic, Czech and Turkish
entity recognition, semantic role labeling and co-reference resolution
1.6% lower phone error rate on average
First, the embedding matrix INLINEFORM4 for all corpora is initialized during the training phase, INLINEFORM9 can be used to bias the input feature Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective
Stanford Sentiment Treebank (SST) BIBREF15 and AG News BIBREF16
For relation prediction they test TransE and for relation extraction they test position aware neural sequence model
relation prediction relation extraction Open IE
Wikidata ReVerb FB15K TACRED
By assessing similarity of 360 pairs of relations from a subset of Wikidata using an integer similarity score from 0 to 4
monte-carlo sequential sampling
To classify a text as belonging to one of the ten possible classes.
A network, whose learned functions satisfy a certain equation. The  network contains RNN cells with either nested internal memories or dependencies that extend temporally beyond the immediately previous hidden state.
Switchboard-2000 contains 1700 more hours of speech data.
300-hour English conversational speech
the Common Voice website iPhone app
A maximum of three contributors will listen to any audio clip. If an $<$audio,transcript$>$ pair first receives two up-votes, then the clip is marked as valid. If instead the clip first receives two down-votes, then it is marked as invalid.
Answer with content missing: (Experimental setup not properly rendered) In our experiments we used seven target domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW),
“Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). Exceptionally, GEN does
not have a natural root category.
1. there may be situations where more than one action is reasonable, and also because writers tell a story playing with elements such as surprise or uncertainty.
2. Macro F1 = 14.6 (MLR, length 96 snippet)
Weighted F1 = 31.1 (LSTM, length 128 snippet)
using generative process
The selection model selects the best answer from the set $\lbrace a_i\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants.
Average claim length is 8.9 tokens.
idebate.com debatewise.org procon.org
one needs to develop mechanisms to recognize valid argumentative structures we ignore trustworthiness and credibility issues
Ethics, Gender, Human rights, Sports, Freedom of Speech, Society, Religion, Philosophy, Health, Culture, World, Politics, Environment, Education, Digital Freedom, Economy, Science and Law
Each class has different patterns in adjectives, adverbs and verbs for sarcastic and non-sarcastic classes
unigrams, bigrams, and trigrams, including sequences of punctuation Word2Vec word embeddings
adjective and adverb patterns verb, subject, and object arguments verbal patterns
Vagalume website
bossa-nova and jovem-guarda genres
Word2Vec, Wang2Vec, and FastText
Gospel, Sertanejo, MPB, Forró, Pagode, Rock, Samba, Pop, Axé, Funk-carioca, Infantil, Velha-guarda, Bossa-nova and Jovem-guarda
Through Amazon MTurk annotators to determine plausibility and content richness of the response
the hybrid model MinAvgOut + RL
BPRA APRA BLEU
most of the models have similar performance on BPRA: DSTC2 (+0.0015), Maluuba (+0.0729)
GDP achieves the best performance in APRA: DSTC2 (+0.2893), Maluuba (+0.2896)
GDP significantly outperforms the baselines on BLEU: DSTC2 (+0.0791), Maluuba (+0.0492)
E2ECM CDM
DSTC2 Maluuba
large-scale document classification datasets introduced by BIBREF14
The different attention distributions suggest that the proposed senti-emoji embedding is capable of recognizing words with strong sentiments that are closely related to the true sentiment even with the presence of words with conflicting sentiments
Attention-based LSTM with emojis
LSTM with text embedding LSTM with emoji embedding Attention-based LSTM with emojis
previous emoji embedding methods fail to handle the situation when the semantics or sentiments of the learned emoji embeddings contradict the information from the corresponding contexts BIBREF5 , or when the emojis convey multiple senses of semantics and sentiments
model has around 836M parameters
input byte embedding matrix has dimensionality 256
the Pasokh dataset BIBREF42
Ordinal position Length of sentence The Ratio of Nouns The Ratio of Numerical entities Cue Words Cosine position Relative Length TF-ISF POS features Document sentences Document words Topical category Ratio of Verbs, Ratio of Adjectives, and Ratio of Adverbs
Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21)
abuse, social, anxiety, PTSD, and financial
Feature selection Random forest XGBoost Hierarchical Model
A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers
By annotators on Amazon Mechanical Turk.
four different companies in the telecommunication, electronics, and insurance industries
overlapping dialogue acts