DinoStackAI/qasper-rag huggingface dataset:
what are the existing online systems?
What are their baselines?
How long is the test dataset for Dutch?
How long is the training dataset for English?
What features are used?
What is the source of the data?
What languages feature in the dataset?
What textual, psychological and behavioural patterns are observed in radical users?
Where is the propaganda material sourced from?
Which behavioural features are used?
Which psychological features are used?
Which textual features are used?
what is the cold-start problem?
how was the experiment evaluated?
what other applications did they experiment in?
Was the entire annotation process done manually?
What were the results of their experiment?
How big is the dataset?
What are all the domains the corpus came from?
How big is benefit in experiments of this editing approach compared to generating entire SQL from scratch?
What are state-of-the-art baselines?
Which dialogue data do they use to evaluate on?
How much improvement is there in the BLEU score?
What is the established approach used for comparison?
What are the five domains?
Which pre-trained language models are used?
What are the hyperparameter setting of the MTL model?
What architecture does the rest of the multi-task learning setup use?
How is the selected sharing layer trained?
what were the length constraints they set?
what is the test set size?
what are the evaluation metrics used?
what other training procedures were explored?
What baseline did they use?
How was the masking done?
How large is the FEVER dataset?
How do they obtain structured data?
Which prior approaches for style transfer do they test with?
Which competing objectives for their unsupevised method do they use?
how many sentiment labels do they explore?
Which dataset do they use for text altering attributes matching to image parts?
Is it possible for the DCM module to correct text-relevant content?
What statistical test is performed?
Which downstream tasks are used for evaluation in this paper?
Which datasets are used for evaluation?
What does the human-in-the-loop do to help their system?
Which dataset do they use to train their model?
Is there a metric that also rewards good stylistic response?
What are existing baseline models on these benchmark datasets?
On what two languages is experimented on?
What three benchmark datasets are used?
What IS versification?
How confident is the conclusion about Shakespeare vs Flectcher?
Is Henry VIII reflective of Shakespeare in general?
Is vocabulary or versification more important for the analysis?
What are the modifications by Thomas Merriam?
What sources of less sensitive data are available?
Other than privacy, what are the other major ethical challenges in clinical data?
Is the performance improvement (with and without affect attributes) statistically significant?
How to extract affect attributes from the sentence?
Which BERT-based baselines do they compare to?
What are the propaganda types?
Do they look at various languages?
What datasets did they use in their experiment?
What size ngram models performed best? e.g. bigram, trigram, etc.
How were the ngram models used to generate predictions on the data?
What package was used to build the ngram language models?
What were subtasks A and B?
Do the authors report only on English
How does counterfactual data augmentation affect gender bias in predictions and performance?
How does hard debiasing affect gender bias in prediction and performance?
How does name anonymization affect gender bias in predictions and performance?
How are the sentences in WikiGenderBias curated?
what crowdsourcing platform did they use?
where does their data come from?
which existing corpora do they compare with?
what is the size of their corpus?
which architectures did they experiment with?
what domains are present in the corpus?
what was the inter-annotator agreement?
Which metrics are used for quantitative analysis?
What metric did they use for qualitative evaluation?
What metric did they use for quantitative evaluation?
Which similarity metrics are used for quantitative analysis?
what languages did they evaluate on?
were these categories human evaluated?
do language share categories?
How long is the vocabulary of subwords?
what rnn classifiers were used?
what results did their system obtain?
what are the existing approaches?
How do they use extracted intent to rescore?
How is the model evaluated against the original recursive training algorithm?
What is the improvement in performance compared to the linguistic gold standard?
Which metrics do they use to evaluate results?
Does the performance increase with the number of used languages?
Which baselines did they compare to?
What dialog tasks was it experimented on?
How was annotation done?
Which news outlets did they focus on?
Do the interviews fall under a specific news category?
Which dialog tasks did they experiment on?
Which news sources do the transcripts come from?
Which real world datasets do they experiment on?
Which other models that incorporate meta information do they compare against?
How do they measure topic quality?
Which data augmentation techniques do they use?
Is this an English language corpus?
The authors point out a relevant constraint on the previous corpora of workplace, do they authors mention any relevant constrains on this corpus?
What type of annotation is performed?
How are the tweets selected?
by how much did their model improve over current alternatives?
did they experiment with other languages besides portuguese?
How large is the dataset?
How are labels for trolls obtained?
Do they only look at tweets?
Which datasets did they use to train the model?
What is the performance of their model?
What baseline do they compare against?
What datasets is the model evaluated on?
Are the orders of case assignment biases motivated by frequency considerations?
Does the paper list other heuristic biases in the LSTMs?
What are the performances of LSTMs and humans on the task?
Do they authors offer a hypothesis for why Twitter data makes better predictions about the inventory of languages used in each country?
What social media platforms are represented?
Which websites were used in the web crawl?
Do they encode sentences separately or together?
How do they use BERT to encode the whole text?
Which is the baseline method?
What loss function is used?
Do they use the unstructured text on the webpage that was the source of the table?
Does their method rely on the column headings of the table?
Are all the tables in the dataset from the same website?
Does the query-bag matching model use a neural network?
What datasets are used for experiments?
Is model compared to some baseline?
What datasets are used in experiments?
How many lexical features are considered?
What is the performance for the three languages tested?
Do they evaluate any non-zero-shot parsers on the three languages?
How big is the Parallel Meaning Bank?
What is the source of the crosslingual word embeddings?
Do they compare against manually-created lexicons?
Do they compare to non-lexicon methods?
What language pairs are considered?
How many abstractive summarizations exist for each dialogue?
How is human evaluators' judgement measured, what was the criteria?
What models have been evaluated?
Do authors propose some better metric than ROUGE for measurement of abstractive dialogue summarization?
How big is SAMSum Corpus?
Do they manually check all adversarial examples that fooled some model for potential valid examples?
Are all generated examples semantics-preserving perturbations to the original text?
What is success rate of fooling tested models in experiments?
What models are able to be fooled for AG's news corpus news categorization task by this approach?
What models are able to be fooled for IMDB sentiment classification task by this approach?
Do they use already trained model on some task in their reinforcement learning approach?
How does proposed reinforcement learning based approach generate adversarial examples in black-box settings?
Which languages with different script do they look at?
What languages do they experiment with?
What language pairs are affected?
What evaluation metrics are used?
What datasets did they use?
what are the other methods they compare to?
what preprocessing method is introduced?
How well does their model perform on the recommendation task?
Which knowledge base do they use to retrieve facts?
Which neural network architecture do they use?
Are reddit and twitter datasets, which are fairly prevalent, not effective in addressing these problems?
by how much did their system outperform previous tasks?
what are the previous state of the art for sentiment categorization?
what are the previous state of the art for tweet semantic similarity?
By how much do they outperform baselines?
Which baselines do they use?
What deep learning methods do they look at?
Which architectures do they experiment with?
Are pretrained embeddings used?
Did they represent tie strength only as number of social ties in a networks?
What sociolinguistic variables (phonetic spellings) did they analyze?
How many domains do they create ontologies for?
Do they separately extract topic relations and topic hierarchies in their model?
How do they measure the usefulness of obtained ontologies compared to domain expert ones?
How do they obtain syntax from raw documents in hrLDA?
What datasets are available for CDSA task?
What two novel metrics proposed?
What similarity metrics have been tried?
What 20 domains are available for selection of source domain?
why do they think sentiment features do not result in improvement?
what was the size of the datasets?
what were their results on both tasks?
what domain-specific features did they train on?
what are the sentiment features used?
what surface-form features were used?
How does their BERT-based model work?
How do they use Wikipedia to automatically collect a query-focused summarization dataset?
How is GPU-based self-critical Reinforcement Learing model designed?
What are previoius similar models authors are referring to?
What was previous state of the art on factored dataset?
How much did the model outperform
What language is in the dataset?
How big is the HotPotQA dataset?
What parts of their multitask model are shared?
How long is the training dataset?
What high-resource language pair is the parent model trained on?
Did they use any regularization method to constrain the training?
How did they constrain training using the parameters?
What are their evaluation metrics?
Are their formal queries tree-structured?
What knowledge base do they rely on?
How do they recover from noisy entity linking?
What datasets do they evaluate on?
Did they use the same dataset as Skip-gram to train?
How much were the gains they obtained?
What is the extractive technique used for summarization?
By how much they outperform the baseline?
How long are the datasets?
What bayesian model is trained?
What low resource languages are considered?
How is cluster purity measured?
What was the previous state of the art for bias mitigation?
How are names paired in the Names Intervention?
Which tasks quantify embedding quality?
What empirical comparison methods are used?
How do they define their tokens (words, word-piece)?
By how much do they outperform existing state-of-the-art model on end-to-end Speech recognition?s
Did the authors collect new data for evaluation?
what language pairs are explored?
what datasets did they use?
which attention based nmt method did they compare with?
by how much did their system improve?
What were the baseline methods?
What dataset is used for training?
What is the source of their data?
What is their binary classifier?
What is a study descriptor?
How are experiments designed to measure impact on performance by different choices?
What impact on performance is shown for different choices of optimizers and learning rate policies?
What domain do the audio samples fall under?
How did they evaluate the quality of annotations?
What is their baseline method?
Does each image have one caption?
What is the size of the dataset?
What is the source of the images and textual captions?
what evaluation metrics did they use?
what were roberta's results?
which was the worst performing model?
How long is their sentiment analysis dataset?
What NLI dataset was used?
What layer gave the better results?
How many annotators were used for sentiment labeling?
How is data collected?
What full English language based sentiment analysis models are tried?
Do they treat differerent turns of conversation differently when modeling features?
How do they bootstrap with contextual information?
Which word embeddings do they utilize for the EmoContext task?
What were the performance results of their network?
What were the baselines?
Do they explore other language pairs?
How do they preprocess Tweets?
What kind of inference model do they build to estimate socioeconomic status?
How much data do they gather in total?
Do they analyze features which help indicate socioeconomic status?
What inference models are used?
What baseline model is used?
How is the remotely sensed data annotated?
Where are the professional profiles crawled from?
How much additional data do they manage to generate from translations?
Do they train discourse relation models with augmented data?
How many languages do they at most attempt to use to generate discourse relation labelled data?
by how much did the system improve?
what existing databases were used?
what existing parser is used?
How do they combine the socioeconomic maps with Twitter data?
Does the fact that people are active during the day time define their SEC?
How did they define standard language?
How do they operationalize socioeconomic status from twitter user data?
What dimensions do the considered embeddings have?
How are global structures considered?
Which translation model do they employ?
Which datasets do they experiment on?
Which other units of text do they experiment with (apart from BPE and ortographic syllables)?
How many steps of BPE do they experiment with?
What nuances between fake news and satire were discovered?
What empirical evaluation was used?
Which linguistic features are used?
What contextual language model is used?
What is the weak supervision signal used in Baidu Baike corpus?
How is BERT optimized for this task?
What is a soft label?
Do the authors examine the real-world distribution of female workers in the country/countries where the gender neutral languages are spoken?
Which of the 12 languages showed the strongest tendency towards male defaults?
What are the evaluation metrics used?
What are the baselines?
Which language learning datasets are used?
What does it mean for sentences to be "lexically overlapping"?
How many tables are in the tablestore?
what dataset is used?
What type of neural network models are used?
How is validity identified and what metric is used to quantify it?
How is severity identified and what metric is used to quantify it?
How is urgency identified and what metric is used to quantify it?
Are all datapoints from the same website?
Do they consider semi-structured webpages?
What image caption datasets were used in this work?
How long does it take to train the model on the mentioned dataset?
How big is the human ratings dataset?
What existing techniques do the authors compare against?
Is the dataset completely automatically generated?
Does the SESAME dataset include discontiguous entities?
How big is the resulting SESAME dataset?
What measures of quality do they use for a Q&A platform?
How many layers of recurrent neural networks do they use for encoding the global context?
How did their model rank in three CMU WMT2018 tracks it didn't rank first?
What is the Ritter dataset?
Does this model perform better than the state of the art?
What features are extracted from text?
What features are extracted from images?
How are the three different forms defined in this work?
What datasets are used for training and testing?
Does approach handle overlapping forms (e.g., metaphor and irony)?
Does this work differentiate metaphor(technique) from irony and sarcasm (purpose)?
What classification tasks do they experiment on?
What categories of fake news are in the dataset?
How much gain in performance was obtained with user embeddings?
By how much does their similarity measure outperform BM25?
How do they represent documents when using their proposed similarity measure?
How do they propose to combine BM25 and word embedding similarity?
Do they use pretrained word embeddings to calculate Word Mover's distance?
Which Twitter sentiment treebank is used?
Where did the system place in the other sub-tasks?
What were the five labels to be predicted in sub-task C?
What is the previous state-of-the-art?
What is the architecture of the saliency model?
What are special architectures this review focuses on that are related to multimodal fusion?
What other model inference optimization schemes authors explore?
On what dataset is model trained/tested?
By how much do they improve on domain classification?
Which dataset do they evaluate on?
How do they decide by how much to decrease confidences of incorrectly predicted domains?
Is some baseline method trained on new dataset?
What potential applications are demonstrated?
What method is proposed to mitigate class imbalance in final dataset?
What are remaining challenges in VQA?
What are the new deep learning models discussed in the paper?
What was the architecture of the 2017 Challenge Winner model?
What is an example of a common sense question?
What pretrained language representations are used?
What tasks are explored?
How is the training time compared to the original position encoding?
Does the new relative position encoder require more parameters?
Can the new position representation be generalized to other tasks?
which social media platforms was the data collected from?
how many data pairs were there for each dataset?
how many systems were there?
what metrics did they use for evaluation?
How many tags are included in the ENE tag set?
What are the most discriminating patterns which are analyzed?
What bootstrapping methodology was used to find new patterns?
What patterns were extracted which were correlated with emotional arguments?
What patterns were extracted which were correlated with factual arguments?
How were the factual and feeling forum posts annotated?
What evaluation metrics did they use?
What NMT techniques did they explore?
What was their best performing model?
Which ontologies did they use?
how is user satisfaction estimated?
by how much did performance improve?
What datasets do they use in the experiment?
What new tasks do they use to show the transferring ability of the shared meta-knowledge?
What kind of meta learning algorithm do they use?
what dataset were used?
what text embedding methods were used in their approach?
Do they compare against state-of-the-art?
What are the benchmark datasets?
What tasks are the models trained on?
What recurrent neural networks are explored?
What extractive models were trained on this dataset?
What abstractive models were trained?
Do the reviews focus on a specific video game domain?
What is the size of this dataset?
What language(s) does the system answer questions in?
Is the proposed system compared to existing systems?
How do they determine that a decoder handles an easier task than the encoder?
How do they measure conditional information strength?
How do they generate input noise for the encoder and decoder?
How do they perform the joint training?
Did the survey provide insight into features commonly found to be predictive of abusive content on online platforms?
Is deep learning the state-of-the-art method in automated abuse detection
How is abuse defined for the purposes of this research?
Do they use external financial knowledge in their approach?
Which evaluation metrics do they use?
Which finance specific word embedding model do they use?
How does lattice rescoring improve inference?
What three languages are used in the translation experiments?
What metrics are used to measure bias reduction?
How is the set of trusted, gender-balanced examples selected?
Which data sources do they use?
Which tasks do they evaluate supervised systems on?
How do they evaluate domain portability?
Which unsupervised representation-learning objectives do they introduce?
Do they manage to consistenly outperform the best performing methods?
Do they try to use other models aside from Maximum Entropy?
What methods to they compare to?
Which dataset to they train and evaluate on?
Do they attempt to jointly learn connectives, arguments, senses and non-explicit identiifers end-to-end?
What settings did they experiment with?
what domains are explored in this paper?
what multi-domain dataset is repurposed?
what four learning strategies are investigated?
By how much did the new model outperform multilingual BERT?
What previous proposed methods did they explore?
What was the new Finnish model trained on?
How many TV series are considered?
How long is the dataset?
Is manual annotation performed?
What are the eight predefined categories?
What was the motivation for using a dependency tree based recursive architecture?
How was a causal diagram used to carefully remove this bias?
What is the state-of-the-art approach?
what do they mean by description length?
do they focus on english verbs?
what evaluation metrics are used?
Do the authors mention any possible confounds in this study?
Are there any other standard linguistic features used, other than ngrams?
What is the relationship between author and emotional valence?
What is the relationship between time and emotional valence?
What is the relationship between location and emotional valence?
What is the computational complexity of old method
Could you tell me more about the old method?
How this system recommend features for the new application?
What is the similarity of manually selected features across related applications in different domains?
What type of features are extracted with this language?
What are meta elements of language for specifying NLP features?
what previous work do they also look at?
What are state of the art results on OSA and PD corpora used for testing?
How better does x-vectors perform than knowlege-based features in same-language corpora?
What is meant by domain missmatch occuring?
How big are OSA and PD corporas used for testing?
How do they think this treebank will support research on second language acquisition?
What are their baseline models?
Did they use crowdsourcing to annotate the dataset?
How significant are the improvements over previous approaches?
Which other tasks are evaluated?
What are the performances associated to different attribute placing?
What architecture is used in the encoder?
Do they evaluate their parallel sentence generation?
How much data do they manage to gather online?
Which models do they use for phrase-based SMT?
Which models do they use for NMT?
What are the BLEU performance improvements they achieve?
What is the architecture of the model?
Do they use multitask learning?
Is Chinese a pro-drop language?
Is English a pro-drop language?
Which movie subtitles dataset did they use?
What are the other two Vietnamese datasets?
Which English dataset do they evaluate on?
What neural network models do they use in their evaluation?
Do they use crowdsourcing for the captions?
What methods are used to build two other Viatnamese datsets?
What deep neural network models are used in evaluation?
How authors evaluate datasets using models trained on different datasets?
Do they evaluate their model on datasets other than RACE?
What deep learning models do they plan to use?
How are the language models used to make predictions on humorous statements?
What type of language models are used? e.g. trigrams, bigrams?
How do attention, recurrent and convolutional networks differ on the language classes they accept?
What type of languages do they test LSTMs on?
What is possible future improvement for proposed method/s?
What is percentage change in performance for better model when compared to baseline?
Which of two design architectures have better performance?
By how much does their model outperform the baseline?
Which models did they compare with?
What is the source of their datasets?
What new advances are included in this dataset?
What language is this dataset in?