DinoStackAI/qasper-rag huggingface dataset:
By how much do they outperform BiLSTMs in Sentiment Analysis?
Does their model have more parameters than other models?
what models did they compare to?
which benchmark tasks did they experiment on?
Are recurrent neural networks trained on perturbed data?
How does their perturbation algorihm work?
Which language is divided into six dialects in the task mentioned in the paper?
What is one of the first writing systems in the world?
How do they obtain distant supervision rules for predicting relations?
Which structured prediction approach do they adopt for temporal entity extraction?
Which evaluation metric has been measured?
What is the WordNet counterpart for Persian?
What large corpus is used for experiments?
Are any of the utterances ungrammatical?
How is the proficiency score calculated?
What proficiency indicators are used to the score the utterances?
What accuracy is achieved by the speech recognition system?
How is the speech recognition system evaluated?
How many of the utterances are transcribed?
How many utterances are in the corpus?
By how much does their model outperform both the state-of-the-art systems?
What is the state-of-the art?
How do they identify abbreviations?
What kind of model do they build to expand abbreviations?
Do they use any knowledge base to expand abbreviations?
Which dataset do they use to build their model?
What is the domain of their collected corpus?
What is the source of the CAIS dataset?
What were the baselines models?
Are the document vectors that the authors introduce evaluated in any way other than the new way the authors propose?
According to the authors, why does the CNN model exhibit a higher level of explainability?
Does the LRP method work in settings that contextualize the words with respect to one another?
How do they incorporate lexicon into the neural network?
What is the source of their lexicon?
What was their performance?
did they use other pretrained language models besides bert?
how was the dataset built?
what is the size of BoolQ dataset?
what processing was done on the speeches before being parsed?
What programming language is the tool written in?
What is the performance change of the textual semantic similarity task when no error and maximum errors (noise) are present?
Which sentiment analysis data set has a larger performance drop when a 10% error is introduced?
What kind is noise is present in typical industrial data?
What is the reason behind the drop in performance using BERT for some popular task?
How they observe that fine-tuning BERT on a specific task does not improve its prunability?
How much is pre-training loss increased in Low/Medium/Hard level of pruning?
How do they gather human reviews?
Do they explain model predictions solely on attention weights?
Can their method of creating more informative visuals be applied to tasks other than turn taking in conversations?
What is the average length of the recordings?
How big was the dataset presented?
Does a neural scoring function take both the question and the logical form as inputs?
What is the source of the paraphrases of the questions?
Does the dataset they use differ from the one used by Pasupat and Liang, 2015?
Is the model compared against a linear regression baseline?
What is the dataset used in the paper?
How does the differential privacy mechanism work?
How does the SCAN dataset evaluate compositional generalization?
What standard large speaker verification corpora is used for evaluation?
What systems are tested?
How many examples are there in the source domain?
How many examples are there in the target domain?
Did they only experiment with captioning task?
What benchmark datasets they use?
what is the proposed Progressive Dynamic Hurdles method?
What is in the model search space?
How does Progressive Dynamic Hurdles work?
Do they beat current state-of-the-art on SICK?
How do they combine MonaLog with BERT?
How do they select monotonicity facts?
How does the model recognize entities and their relation to answers at inference time when answers are not accessible?
What other solutions do they compare to?
How does the gatint mechanism combine word and character information?
How is the PBMT system trained?
Which NMT architecture do they use?
Do they train the NMT model on PBMT outputs?
How is the corpus normalized?
what evaluation methods are discussed?
what are the off-the-shelf systems discussed in the paper?
How is "hirability" defined?
Have the candidates given their consent to have their videos used for the research?
Do they analyze if their system has any bias?
Is there any ethical consideration in the research?
What classification task was used to evaluate the cross-lingual adaptation method described in this work?
How many parameters does the presented model have?
How do they measure the quality of detection?
What previous approaches are considered?
How is the back-translation model trained?
Are the rules dataset specific?
How many rules had to be defined?
How much labeled data is available for these two languages?
What was performance of classifiers before/after using distant supervision?
What classifiers were used in experiments?
In which countries are Hausa and Yor\`ub\'a spoken?
What is the agreement score of their annotated dataset?
What is the size of the labelled dataset?
Which features do they use to model Twitter messages?
Do they allow for messages with vaccination-related key terms to be of neutral stance?
How big are the datasets used?
Is this a span-based (extractive) QA task?
What datasets are used for training/testing models?
What is specific to gCAS cell?
What dataset do they evaluate their model on?
What is the source of external knowledge?
Which of their proposed attention methods works better overall?
Which dataset of texts do they use?
Do they measure how well they perform on longer sequences specifically?
Which other embeddings do they compare against?
What were the sizes of the test sets?
What training data did they use?
What domains do they experiment with?
What games are used to test author's methods?
How is the domain knowledge transfer represented as knowledge graph?
Which datasets are used?
Which six languages are experimented with?
What shallow local features are extracted?
Do they compare results against state-of-the-art language models?
Which dataset do they train their models on?
It looks like learning to paraphrase questions, a neural scoring model and a answer selection model cannot be trained end-to-end. How are they trained?
What multimodal representations are used in the experiments?
How much better is inference that has addition of image representation compared to text-only representations?
How they compute similarity between the representations?
How big is vSTS training data?
Which evaluation metrics do they use for language modelling?
Do they do quantitative quality analysis of learned embeddings?
Do they evaluate on downstream tasks?
Which corpus do they use?
How much more accurate is the model than the baseline?
How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
What is new state-of-the-art performance on CoNLL-2009 dataset?
What different approaches of encoding syntactic information authors present?
What domain does the dataset fall into?
What ASR system do they use?
How big are datasets used in experiments?
What previously annotated databases are available?
Do they argue that all words can be derived from other (elementary) words?
Do they break down word meanings into elementary particles as in the standard model of quantum theory?
How big is the dataset used?
How they prove that multi-head self-attention is at least as powerful as convolution layer?
What numerical experiments they perform?
What dierse domains and languages are present in new datasets?
Are their corpus and software public?
How are EAC evaluated?
What are the currently available datasets for EAC?
What are the research questions posed in the paper regarding EAC studies?
What is triangulation?
Did they experiment with the dataset on some tasks?
Do they use skipgram version of word2vec?
What domains are considered that have such large vocabularies?
Do they perform any morphological tokenization?
What data do they train the language models on?
What architectures are explored to improve the seq2seq model?
Why is this work different from text-only UNMT?
What is baseline used?
Did they evaluate against baseline?
How they evaluate their approach?
Which document classifiers do they experiment with?
How is some information lost in the RNN-based generation models?
What is the model accuracy?
Did they use other evaluation metrics?
What was their perplexity score?
What languages are explored in this paper?
What parallel corpus did they use?
What datasets are used for experiments on three other tasks?
What accuracy do they approach with their proposed method?
What they formulate the question generation as?
What two main modules their approach consists of?
Are there syntax-agnostic SRL models before?
What is the biaffine scorer?
What languages are were included in the dataset of hateful content?
How was reliability measured?
How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?
What definition was one of the groups was shown?
Was the degree of offensiveness taken as how generally offensive the text was, or how personally offensive it was to the annotator?
How were potentially hateful messages identified?
Which embeddings do they detect biases in?
What does their system consist of?
What are the two PharmaCoNER subtasks?
What neural language models are explored?
How do they perform data augmentation?
What are the characteristics of the city dialect?
What are the characteristics of the rural dialect?
What are their baseline methods?
Which of the model yields the best performance?
What is the performance of the models on the tasks?
How is the data automatically generated?
Do they fine-tune the used word embeddings on their medical texts?
Which word embeddings do they use to represent medical visits?
Do they explore similarity of texts across different doctors?
Which clustering technique do they use on partients' visits texts?
What is proof that proposed functional form approximates well generalization error in practice?
What other non-neural baselines do the authors compare to?
What baseline function is used in REINFORCE algorithm?
Is Aristo just some modern NLP model (ex. BERT) finetuned od data specific for this task?
On what dataset is Aristo system trained?
Does they focus on any specific product/service domain?
How is fluency of generated text evaluated?
How is faithfulness of the resulting text evaluated?
How are typing hints suggested?
What is the effectiveness plan generation?
How is neural planning component trained?
How do they evaluate interpretability in this paper?
How much better performing is the proposed method over the baselines?
What baselines are the proposed method compared against?
What dataset/corpus is this work evaluated over?
What language technologies have been introduced in the past?
Does the dataset contain non-English reviews?
What do the correlation demonstrate?
On Twitter, do the demographic attributes and answers show more correlations than on Yahoo! Answers?
How many demographic attributes they try to predict?
What evaluation metrics do they use?
How do they define local variance?
How do they quantify alignment between the embeddings of a document and its translation?
Does adversarial learning have stronger performance gains for text classification, or for NER?
Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
what experiments are conducted?
what opportunities are highlighted?
how do they measure discussion quality?
do they use a crowdsourcing platform?
Which soft-selection approaches are evaluated?
Is the model evaluated against the baseline also on single-aspect sentences?
What metric was used in the evaluation step?
What did they pretrain the model on?
What does the data cleaning and filtering process consist of?
What unlabeled corpus did they use?
What performance did proposed method achieve, how much better is than previous state-of-the-art?
What was previous state-of-the-art approach?
How is Relation network used to infer causality at segment level?
What is the TREC-CAR dataset?
How does their model utilize contextual information for each work in the given sentence in a multi-task setting? setting?
What metris are used for evaluation?
How better is proposed model compared to baselines?
How big is slot filing dataset?
Which machine learning models do they use to correct run-on sentences?
What are least important components identified in the the training of VQA models?
What type of experiments are performed?
What components are identified as core components for training VQA models?
what approaches are compared?
What model do they use a baseline to estimate satisfaction?
what semantically conditioned models did they compare with?
Do they differentiate insights where they are dealing with learned or engineered representations?
Do they show an example of usage for INFODENS?
Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?
Which tweets are used to output the daily sentiment signal?
What is the baseline machine learning prediction approach?
What are the weaknesses of their proposed interpretability quantification method?
What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?
How do they gather data for the query explanation problem?
Which query explanation method was preffered by the users in terms of correctness?
Do they conduct a user study where they show an NL interface with and without their explanation?
What was the task given to workers?
How was lexical diversity measured?
How many responses did they obtain?
What crowdsourcing platform was used?
Are results reported only for English data?
What human evaluation method is proposed?
How is human evaluation performed, what were the criteria?
What automatic metrics are used?
What other kinds of generation models are used in experiments?
How does discrete latent variable has an explicit semantic meaning to improve the CVAE on short-text conversation?
What news dataset was used?
How do they determine similarity between predicted word and topics?
What is the language model pre-trained on?
What languages are represented in the dataset?
Which existing language ID systems are tested?
Which language family does Mboshi belong to?
Does the paper report any alignment-only baseline?
How is the word segmentation task evaluated?
What are performance compared to former models?
How faster is training and decoding compared to former models?
What datasets was the method evaluated on?
How many people are employed for the subjective evaluation?
What other embedding models are tested?
How is performance measured?
How are rare words defined?
What other datasets are used?
What is the source of the dataset?
How do they show that acquiring names of places helps self-localization?
How do they evaluate how their model acquired words?
Which method do they use for word segmentation?
Does their model start with any prior knowledge of words?
Do the images have multilingual annotations or monolingual ones?
Could you learn such embedding simply from the image annotations and without using visual information?
How is the generative model evaluated?
How do they evaluate their method?
Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
What is the challenge for other language except English
How many categories of offensive language were there?
How large was the dataset of Danish comments?
Who were the annotators?
Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?
Which matching features do they employ?
How often are the newspaper websites crawled daily?
Which countries and languages do the political speeches and manifestos come from?
Do changes in policies of the political actors account for all of the mistakes the model made?
What model are the text features used in to provide predictions?
Does each attention head in the decoder calculate the same output?
Which distributional methods did they consider?
Which benchmark datasets are used?
What hypernymy tasks do they study?
Do they repot results only on English data?
What were the variables in the ablation study?
What is barycentric Newton diagram?
Do they propose any solution to debias the embeddings?
Is human evaluation performed?
What are the three datasets used?
Did they experiment with the corpus?
How were the feature representations evaluated?
What linguistic features were probed for?
Does the paper describe experiments with real humans?
What are bottleneck features?
How do they obtain language identities?
What other multimodal knowledge base embedding methods are there?
What is the data selection paper in machine translation
Do they visualize the difference between AM-Softmax and regular softmax?
what metrics were used for evaluation?
what are the state of the art methods?
What datasets do they use for the tasks?
Which two datasets is the system tested on?
How they compare varioius mechanisms in terms of naturalness?
Is any data-to-text generation model trained on this new corpus, what are the results?
How the authors made sure that corpus is clean despite being crowdsourced?
How do they obtain human generated policies?
How many agents do they ensemble over?
What is the task of slot filling?
How are the potentially relevant text fragments identified?
What algorithm and embedding dimensions are used to build the task-specific embeddings?
What data is used to build the task-specific embeddings?
What knowledge bases do they use?
What pre-trained models did they compare to?
How does the fusion method work?
What benchmarks did they experiment on?
What multi-domain dataset is used?
Which domains did they explored?
Which is the best performing method?
What size are the corpora?
What is a self-compiled corpus?
What are the 12 AV approaches which are examined?
how was annotation done?
what is the source of the new dataset?
Which word frequencies reflect on the psychology of the twitter users, according to the authors?
Do they collect only English data?
How do they measure correlation between the prediction and explanation quality?
Does the Agent ask for a value of a variable using natural language generated text?
What models does this overview cover?
How do they incorporate human advice?
What do they learn jointly?
Is this an English-language dataset?
What affective-based features are used?
What conversation-based features are used?
What datasets or tasks do they conduct experiments on?
How big is performance improvement proposed methods are used?
How authors create adversarial test set to measure model robustness?
Do they compare with the MAML algorithm?
What baseline is used?
What topic clusters are identified by LDA?
What are the near-offensive language categories?
How much do they outperform previous state-of-the-art?
How do they generate the auxiliary sentence?
How big are this dataset and catalogue?
What is open website for cataloguing abusive language data?
how many speeches are in the dataset?
How big is the provided treebank?
is the dataset balanced across the four languages?
What is the size of ACL-ARC datasets?
How is quality of annotation measured?
On what data is the model evaluated?
What is their baseline model?
Which are the sequence model architectures this method can be transferred across?
What percentage of improvement in inference speed is obtained by the proposed method over the newest state-of-the-art methods?
What is the metric that is measures in this paper?
Do they only test on one dataset?
What baseline decoder do they use?
Was evaluation metrics and criteria were used to evaluate the output of the cascaded multimodal speech translation?
What dataset was used in this work?
How do they evaluate the sentence representations?
What are the two decoding functions?
How is language modelling evaluated?
How did they gather the data?
What are the domains covered in the dataset?
How are the two different models trained?
How big are negative effects of proposed techniques on high-resource tasks?
Are this techniques used in training multilingual models, on what languages?
What baselines non-adaptive baselines are used?
What text sequences are associated with each vertex?
Which other unsupervised models are used for comparison?
What metric is used to measure performance?
How do the n-gram features incorporate compositionality?
How do Zipf and Herdan-Heap's laws differ?
What was the best performing baseline?
Which approaches did they use?
Did they use a crowdsourcing platform for the summaries?
How are the synthetic examples generated?
By how much does the new parser outperform the current state-of-the-art?
What experimental evaluation is used?
How is the architecture fault-tolerant?
Which elements of the platform are modular?
What is the source of memes?
Is the dataset multimodal?
How is each instance of the dataset annotated?
Which dataset do they use for text modelling?
Do they compare against state of the art text generation?
How do they evaluate generated text quality?
Was the system only evaluated over the second shared task?
Could you tell me more about the metrics used for performance evaluation?
What are the tasks that this method has shown improvements?
Why does the model improve in monolingual spaces as well?
Do the authors test their annotation projection techniques on tasks other than AMR?
How is annotation projection done when languages have different word order?
What is the reasoning method that is used?
What KB is used in this work?
How did they measure effectiveness?
Which of the two ensembles yields the best performance?
What are the two ways of ensembling BERT and E-BERT?
How is it determined that a fact is easy-to-guess?
How is dependency parsing empirically verified?
How are different network components evaluated?
What are the performances obtained for PTB and CTB?
What are the models used to perform constituency and dependency parsing?
What is the new initialization method proposed in this paper?
How was a quality control performed so that the text is noisy but the annotations are accurate?
Is it a neural model? How is it trained?
What are the features of used to customize target user interaction?
How they evaluate quality of generated output?
What automated metrics authors investigate?
Who annotated the data?
How do they obtain parsed source sentences?
What kind of encoders are used for the parsed source sentence?
Whas is the performance drop of their model when there is no parsed input?
How were their results compared to state-of-the-art?
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?
How is CNN injected into recurent units?
Are there some results better than state of the art on these tasks?
Do experiment results show consistent significant improvement of new approach over traditional CNN and RNN models?
What datasets are used for testing sentiment classification and reading comprehension?
How are sentence embeddings incorporated into the speech recognition system?
How different is the dataset size of source and target?
How do you find the entity descriptions?
How is OpenBookQA different from other natural language QA?
At what text unit/level were documents processed?
What evaluation metric were used for presenting results?
Was the structure of regulatory filings exploited when training the model?
What type of documents are supported by the annotation platform?
What are the state-of-the-art models for the task?
What are the strong baselines you have?
What are causal attribution networks?
how did they ask if a tweet was racist?
What other cross-lingual approaches is the model compared to?
What languages are explored?
How does the model compute the likelihood of executing to the correction semantic denotation?
Which conventional alignment models do they use as guidance?
What are state of the art methods authors compare their work with?