DinoStackAI/qasper-rag huggingface dataset:
How do they prove that RNNs with arbitrary precision are as powerful as a pushdown automata?
Does the paper report F1-scores with and without post-processing for the second task?
What does post-processing do to the output?
Do they test any neural architecture?
What supports the claim that enhancement in training is advisable as long as enhancement in test is at least as strong as in training?
How does this single-system compares to system combination ones?
What was previous single-system state of the art result on the CHiME-5 data?
How much is error rate reduced by cleaning up training data?
Which baselines were they used for evaluation?
What is the difference in size compare to the previous model?
What languages are used as input?
What are the components of the classifier?
Which uncertain outcomes are forecast using the wisdom of crowds?
What set topics are looked at?
Which widely used dataset did the authors use?
How do they perform semi-supervised learning?
What are the five evaluated tasks?
What downstream tasks are explored?
What factors contribute to the stability of the word embeddings?
How is unstability defined?
What embedding algorithms are explored?
Which data-selection algorithms do they use?
How are the artificial sentences generated?
What domain is their test set?
What morphological features are considered?
What type of attention do they use in the decoder?
What set of semantic tags did they use?
How better are results of new model compared to competitive methods?
What is the metrics used for benchmarking methods?
What are other competitive methods?
What is the size of built dataset?
which had better results, the svm or the random forest model?
which network community detection dataset was used?
did they collect the human labeled data?
how many classes are they classifying?
Do the authors evaluate only on English datasets?
What metrics of gender bias amplification are used to demonstrate the effectiveness of this approach?
How is representation learning decoupled from memory management in this architecture?
What method did the highest scoring team use?
What descriptive statistics are provided about the data?
What was the level of inter-annotator agreement?
What questions were asked in the annotation process?
Why is NER for tweets more challenging as the number of entities increases?
What data preparation steps were used to construct the dataset?
What is the training objective in the method introduced in this paper?
Does regularization of the fine-tuning process hurt performance in the target domain?
What kind of baseline model do they compare against?
Do they analyze which types of sentences/reviews are useful or not?
Which set of datasets do they use?
How are adversarial examples generated?
Is BAT smaller (in number of parameters) than post-trained BERT?
What are the modifications made to post-trained BERT?
Were human evaluations conducted?
Where can I access the dataset?
Did they release their dataset?
Did they use Amazon Mechanical Turk to collect data?
Did they use The Onion as their dataset?
How much lower is the computational cost of the proposed model?
What is the state-of-the-art model?
What is a pseudo language model?
How significant is the performance compared to LSTM model?
How does the introduced model combine the both factors?
How much improvement do the introduced model achieve compared to the previous models?
do they compare their system with other systems?
what is the architecture of their model?
what dataset did they use for this tool?
Did they build a dataset?
what is the average number of speakers in the dataset?
by how much is accuracy improved?
what are the previous state of the art systems?
What are the three SOTA models evaluated?
What is the morphological constraint added?
How do they interpret the model?
What are the two applications of neuro-symbolism?
what elements of each profile did they use?
Does this paper discuss the potential these techniques have for invading user privacy?
How is the gold standard defined?
What is the timeframe of the current events?
What model was used for sentiment analysis?
What language are the tweets in?
Is this analysis performed only on English data?
Do they authors offer any hypothesis for why the parameters of Zipf's law and Heaps' law differ on Twitter?
What explanation do the authors offer for the super or sublinear urban scaling?
Do the authors give examples of the core vocabulary which follows the scaling relationship of the bulk text?
What syntactic and semantic features are proposed?
Which six speech acts are included in the taxonomy?
what classifier had better performance?
how many tweets were labeled?
how many annotators were there?
who labelled the tweets?
what are the proposed semantic features?
what syntactic features are proposed?
What was the baseline?
How many songs were collected?
how does end of utterance and token tags affect the performance
what kind of conversations are in the douban conversation corpus?
what pretrained word embeddings are used?
What other evaluation metrics are reported?
What out of domain scenarios did they evaluate on?
What was their state of the art accuracy score?
Which datasets did they use?
What are the neural baselines mentioned?
What regularization methods are used?
What metrics are used?
What simplification of the architecture is performed that resulted in same performance?
How much better is performance of SEPT compared to previous state-of-the-art?
How many actions are present in the dataset?
How many videos did they use?
What unimodal algorithms do they compare with?
What platform was used for crowdsourcing?
How long are the videos?
How were missing hypotheses discovered?
Which aspects of response generation do they evaluate on?
What model architecture do they use for the decoder?
Do they ensure the edited response is grammatical?
What do they use as the pre-defined index of prototype responses?
what are all the datasets they experiment with?
what was the baseline model?
What do they mean by explicit selection of most relevant segments?
What datasets they used for evaluation?
How big is this dataset?
How are biases identified in the dataset?
How do they deal with imbalanced datasets?
What models do they compare to?
What text preprocessing tasks do they focus on?
Do the tweets fall under a specific domain?
How many tweets are in the dataset?
What categories do they look at?
Which knowledge destilation methods do they introduce?
What type of weight pruning do they use?
Which dataset do they train on?
Do they reason why greedy decoding works better then beam search?
Are experiments conducted on multiple datasets?
What baselines is the neural relation extractor compared to?
What additional evidence they use?
What hand-crafted features do other approaches use?
What is the strong baseline model used?
What crowdsourcing platform did they obtain the data from?
How large is the test set?
What does SARI measure?
Is dataset balanced in terms of available data per language?
How do they split the dataset when training and evaluating their models?
Do they demonstrate the relationship between veracity and stance over time in the Twitter dataset?
What of the two strategies works best?
What downstream tasks are tested?
Is this model trained in unsuperized manner?
How much is BELU score difference between proposed approach and insertion-only method?
are the protocols manually annotated?
what ML approaches did they experiment with?
What type of attention is used in the recognition system?
What are the solutions proposed for the seq2seq shortcomings?
How much is training speeded up?
What experiments do they perform?
Do they compare against a system that does not use streaming text, but has the entire text at disposal?
Does larger granularity lead to better translation quality?
What is the baseline method?
What aspects are used to judge question quality?
What did the human annotations consist of?
What characterizes the 303 domains? e.g. is this different subject tags?
What is the best performing system?
What tokenization methods are used?
What baselines do they propose?
What models are trained?
Does the baseline use any contextual information?
What is the strong rivaling system?
Where are the debates from?
Do they have an elementary unit of text?
By how much do they outpeform existing text denoising models?
In their nonsymbolic representation can they represent two same string differently depending on the context?
On which datasets do they evaluate their models?
How do they determine demographics on an image?
Do they assume binary gender?
What is the most underrepresented person group in ILSVRC?
Do they compare to state-of-the-art models?
how many sentences did they annotate?
what dataset was used in their experiment?
what are the existing annotation tools?
what ontologies did they use?
How much improvement is given on RACE by their introduced approach?
what pruning did they perform?
Do they evaluate binary paragraph vectors on a downstream task?
How do they show that binary paragraph vectors capture semantics?
Which training dataset do they use?
Do they analyze the produced binary codes?
Do they use machine learning?
What are the ICD-10 codes?
What media sources do they use?
What evidence is presented that humans perceive the sentiment classes as ordered?
What size of dataset is sufficiently large for the model performance to approach the inter-annotator agreement?
Which measures of inter-annotator agreement are used?
What statistical test(s) is used to compare the top classification models?
How is their NER model trained?
Do they use pretrained word embeddings such as BERT?
Where does their information come from?
What intents do they have?
Is proposed approach compared to some baselines?
What datasets are used for this tasks?
How big are improvements on these tasks?
Which downstream tasks are considered?
How long are the two unlabelled corpora?
Do the authors report only on English data?
How is the impact of ParityBOT analyzed?
What public online harassment datasets was the system validated on?
Where do the supportive tweets about women come from? Are they automatically or manually generated?
How are the hateful tweets aimed at women detected/classified?
Do they use all the hidden layer representations?
What languages are used for the experiments?
What is the caching mechanism?
What language model architectures are examined?
What directions are suggested to improve language models?
What logic rules can be learned using ELMo?
Does Elmo learn all possible logic rules?
Which metrics are used for evaluating the quality?
Are face tracking, identification, localization etc multimodal inputs in some ML model or system is programmed by hand?
What are baselines used?
What are the baselines for this paper?
What VQA datasets are used for evaluating this task?
How do they model external knowledge?
What type of external knowledge has been used for this paper?
What is the proposed algorithm or model architecture?
Do they attain state-of-the-art performance?
What fusion methods are applied?
What graph-based features are considered?
How does Overton handles contradictory or incomplete supervision data?
What are high level declarative abstractions Overton provides?
How are applications presented in Overton?
Does Overton support customizing deep learning models without writing any code?
what metrics are used to evaluate the models?
How is the quality of the translation evaluated?
What are the post-processing approaches applied to the output?
Is the MUSE alignment independently evaluated?
How many general qualitative statements are in dataset?
What are state-of-the-art models on this dataset?
How are properties being compared annotated?
What state-of-the-art tagging model did they use?
By how much do they improve upon supervised traning methods?
Do they jointly optimize both agents?
Which neural network architecture do they use for the dialog agent and user simulator?
Do they create the basic dialog agent and basic user simulator separately?
Is this done in form of unsupervised (clustering) or suppervised learning?
Does this study perform experiments to prove their claim that indeed personalized profiles will have inclination towards particular cuisines?
What baselines do they compare to?
What training set sizes do they use?
What language model is trained?
What machine learning models are considered?
What is the agreement of the dataset?
Do the authors offer any potential reasons why cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it?
Which three variants of sequential validation are examined?
Which three variants of cross-validation are examined?
Which European languages are targeted?
In what way are sentiment classes ordered?