DinoStackAI/qasper-rag huggingface dataset:
How big is the ANTISCAM dataset?
How is intent annotated?
What are the baselines outperformed by this work?
What are the evaluation metrics and criteria used to evaluate the model performance?
What is the accuracy of this model compared to sota?
What previous methods do they compare against?
What is their evaluation metric?
Are their methods fully supervised?
Do they build a dataset of rumors?
What languages do they evaluate their methods on?
How do they define rumors?
What baselines did they compare with?
Which tasks are explored in this paper?
Which NER dataset do they use?
How do they incorporate direction and relative distance in attention?
Do they outperform current NER state-of-the-art models?
What was their accuracy score?
What are the state-of-the-art systems?
What dataset did they evaluate on?
What are the contributions of this paper?
What are the baselines this paper uses?
How do they decide what is the semantic concept label of particular cluster?
How do they discover coherent word clusters?
How big are two introduced datasets?
What are strong baselines authors used?
How do data-driven models usually respond to abuse?
How much data did they gather from crowdsourcing?
How many different strategies were evaluated?
Was the automatic annotation evaluated?
What morphological typologies are considered?
Does the model consider both derivational and inflectional morphology?
What type of morphological features are used?
What datasets are used in this paper?
What language are the captions in?
What ad-hoc approaches are explored?
What supervised baselines did they compare with?
Is the data specific to a domain?
Where do their figure and captions come from?
did they experiment with lexicons?
what was the baseline?
what was their result?
what dataset was used?
What is their definition of hate speech?
What languages does the new dataset contain?
What aspects are considered?
How big is their dataset?
What are the opportunities presented by the use of Semantic Web technologies in Machine Translation?
What are the challenges associated with the use of Semantic Web technologies in Machine Translation?
What are the other obstacles to automatic translations which are not mentioned in the abstract?
what eeg features were used?
Does LadaBERT ever outperform its knowledge destilation teacher in terms of accuracy on some problems?
Do they evaluate which compression method yields the most gains?
On which datasets does LadaBERT achieve state-of-the-art?
What domain of text are they working with?
What dataset do they use?
Do they compare to abstractive summarization methods?
What types of commonsense knowledge are they talking about?
What do they mean by intrinsic geometry of spaces of learned representations?
Did they pre-train on existing sentiment corpora?
What were the most salient features extracted by the models?
How many languages are in the dataset?
What are the parts of the "multimodal" resources?
Are annotators familiar with the science topics annotated?
How are the expert and crowd-sourced annotations compared to one another?
What platform do the crowd-sourced workers come from?
Who are considered trained experts?
Which model architecture do they opt for?
Which dataset do they use?
Which weak signal data do they use?
Do they compare their semantic feature approach to lexical approaches?
what dataset was used for training?
what is the size of the training data?
what features were derived from the videos?
Do any of the models use attention?
What translation models are explored?
What is symbolic rewriting?
How do they incorporate expert knowledge into their topic model?
On which corpora do they evaluate on?
Do they compare against popular topic models, such as LDA?
What is F-score obtained?
What is the state-of-the-art?
Which Chinese social media platform does the data come from?
What dataset did they use?
What are the five downstream tasks?
Is this more effective for low-resource than high-resource languages?
Is mBERT fine-tuned for each language?
How did they select the 50 languages they test?
What kind of evaluations do use to evaluate dialogue?
By how much do their cross-lingual models lag behind other models?
Which translation pipelines do they use to compare against?
Which languages does their newly created dataset contain?
did they collect their own contrastive test set?
what are the baselines?
what context aware models were experimented?
what languages did they experiment on?
How do they obtain the entity linking results in their model?
Which model architecture do they use?
Which datasets do they evaluate on?
How many domain experts were involved into creation of dataset?
What metrics are used for evaluation?
What is the performance of fine tuned model on this dataset?
Are constructed datasets open sourced?
How does labeling scheme look like?
What pretrained language model is used?
How big is constructed dataset?
What metric is considered?
What hand-crafted features are used?
What word embeddings are used?
Do they annotate their own dataset?
How are the sentence embeddings generated?
What is argumentative zoning?
How did they obtain the tweets?
What baseline do they compare to?
What language is explored in this paper?
What blackmarket services do they look at?
What languages do they use during pretraining?
What is the architecture of the decoder?
What is the architecture of the encoder?
What is their baseline?
What human evaluation metrics do they look at?
Which automated evaluation metrics are used?
What baselines do they compare against?
What model is used to generate the premise?
Are the stories in the dataset fictional stories?
Where are the stories collected from?
which pretrained embeddings were experimented with?
what datasets where used?
what are the state of the art methods they compare with?
What agreement measure is used?
Do they report the annotation agreement?
How many annotators participated?
What social-network features are used?
What are the five factors considered?
How is cyberbullying defined?
What evaluation was performed on the output?
Where did the joke data come from?
What type of quotes is this system trying to generate?
What size filters do they use in the convolution layer?
By how much do they outperform state-of-the-art models on knowledge graph completion?
did they test with other pretrained models besides bert?
what models did they compare with?
what datasets were used for testing?
What inter-annotator agreement did they obtain?
How did they annotate the corpus?
What is the size of the corpus?
Which datasets do they use?
What models are explored in this paper?
what were the evaluation metrics?
what model is used?
what future work is described?
How is the sentence alignment quality evaluated?
How is the speech alignment quality evaluated?
Is their gating mechanism specially designed to handle one sentence bags?
Do they show examples where only one sentence appears in a bag and their method works, as opposed to using selective attention?
By how much do they outperform previous state-of-the-art in terms of top-n precision?
By how much do they outperform existing methods?
Do they separately evaluate performance of their learned representations (before forwarding them to the CNN layer)?
What dataset was used in this challenge?
Which subsystem outperformed the others?
Do they reduce language variation of text by enhancing frequencies?
Which domains do they explore?
Which thesauri did they use?
What type of model do they train?
How many users does their dataset have?
How long is their dataset?
In what tasks does fine-tuning all layers hurt performance?
Do they test against the large version of RoBERTa?
How does the proposed training framework mitigate the bias pattern?
Which datasets do they use in the cross-dataset evaluation?
Which was the most helpful strategy?
How large is their tweets dataset?
what is the size of the idn tagged corpus?
what neural network models were explored?
what rule based models were evaluated?
what datasets have been used for this task?
How much data do they use to train the embeddings?
Do they evaluate their embeddings in any downstream task appart from word similarity and word analogy?
What are the issues identified for out-of-vocabulary words?
Is the morphology detection task evaluated?
How does the model proposed extend ENAMEX?
Which morphological features are extracted?
Do the authors report results on only English datasets?
What are the characteristics of the dataset of Twitter users?
How can an existing bot detection system by customized for health-related research?
What type of health-related research takes place in social media?
Do the QA tuples fall under a specific domain?
What is the baseline model?
How large is the corpus of QA tuples?
What corpus did they use?
what boosting techniques were used?
did they experiment with other text embeddings?
what is the size of this improved dataset?
how was the new dataset collected?
who annotated the new dataset?
what shortcomings of previous datasets are mentioned?
Do single-language BERT outperforms multilingual BERT?
What types of agreement relations do they explore?
what text classification datasets do they evaluate on?
which models is their approach compared to?
by how much did their approach outperform previous work?
what was the previous best results model?
what are the baseline models?
what domains are explored?
what training data was used?
What is the performance of the best model?
What are the models tested on the dataset?
Which method best performs on the offensive language identification task?
Did they use crowdsourcing for the annotations?
How many annotators did they have?
Is the dataset balanced?
What models do they experiment on?
Do any of their reviews contain translations for both Catalan and Basque?
What is the size of their published dataset?
How does sentence construction component works?
What are two use cases that demonstrate capability of created system?
Do they explore how their word representations vary across languages?
Which neural language model architecture do they use?
How do they show genetic relationships between languages?
Did they test the idea that the system reduces the time needed to encode ADR reports on real pharmacologists?
Do the authors offer a hypothesis as to why the system performs better on short descriptions than longer ones?
What are the steps in the MagiCoder algorithm?
How is the system constructed to be linear in the size of the narrative input and the terminology?
What conclusions do the authors draw about the aspects and mechanisms of personal recovery in bipolar disorder?
What computational linguistic methods were used for the analysis?
Was permission sought from the bipolar patients to use this data?
How are the individuals with bipolar disorder identified?
What is the source of the training/testing data?
What are the types of chinese poetry that are generated?
what is the previous work they are comparing to?
Do they use skip-gram word2vec?
How is quality of the word vectors measured?
Do they report results only on English data?
Where do the news texts come from?
What baseline is used for this task?
What type of nerual keyphrase generation models are trained?
How do the editors' annotations differ from those in existing datasets?
Do they use pretrained word embeddings?
How many layers does their model have?
What metrics do they use?
what dataset did they use?
what was their model's f1 score?
what are the state of the art models?
How do you know the word alignments are correct?
How slow is the unparallelizable ART model in the first place?
What metric is used to measure translation accuracy?
Were any datasets other than WMT used to test the model?
What dicrimating features are discovered?
What results are obtained on the alternate datasets?
Are answers in this dataset guaranteed to be substrings of the text? If not, what is the coverage of answers being substrings?
What is the machine learning method used to make the predictions?
How is the event prediction task evaluated?
What are the datasets used in the paper?
Is there a difference between the model's performance for morphologically impoverished and morphologically complex languages?
What languages do they apply the model to?
How are the embeddings evaluated in the human judgement comparison?
what was the margin their system outperformed previous ones?
what prior approaches did they compare to?
what results do they achieve?
what chinese dialects are explored?
Which neural machine translation model was used?
What are the restrictions of the restricted track?
Which works better according to human evaluation, the concurrent or the modular system?
Were the Wikipedia edits that removed framings, presuppositions and attitudes from biased sentences a Wiki community effort, or were annotators trained to do it?
How is subjective text automatically neutralized?
What is the sign language recognition task investigated?
What is the performance of the best model in the sign language recognition task?
What are the deep learning architectures used?
Who made the stated claim (that "this is because character-level models learn morphology")?
Which languages do they use?
Do the character-level models perform better than models with access to morphological analyses only?
What is case syncretism?
Do humans assess the quality of the generated responses?
What models are used to generate responses?
What types of hate speech are considered?
Which baselines to they compare to?
Which sentence compression technique works best?
Do they compare performance against state of the art systems?
What is the performance of large state-of-the-art models on these datasets?
What is used as a baseline model?
How do they build gazetter resources from Wikipedia knowlege base?
What is the dataset that is used to train the embeddings?
What speaker characteristics are used?
What language is used for the experiments?
Is the embedding model test in any downstream task?
what is the baseline model
What contribute to improve the accuracy on legal question answering task?
What sizes were their datasets?
What is their model's architecture?
What languages did they use?
Are there experiments with real data?
What supervised machine learning models do they use?
Does the supervised machine learning approach outperform previous work?
How large is the released data set?
What is an example of a condition-action pair?
Which metrics were considered?
What NLG tasks were considered?
what state of the art methods are compared to?
what are the performance metrics?
what is the original model they refer to?
how are sentences selected prior to making the summary?
Do they evaluate only on English datasets?
What type of frequency analysis was used?
What type of classifiers were used?
Who annotated the Twitter and Reddit data for irony?
what resources are combined to build the labeler?
what datasets were used?
what is the monolingual baseline?
what languages are explored in this paper?
What is the problem of session segmentation?
Was the filtering based on fluency and domain relevance done automatically?
How was domain relevance estimated?
How many hand-crafted templates did they have to make?
How was the fluency measured?
What data is used in this work?
What dataset is used?
How was the dataset collected?
what evaluation metrics were used?
What datasets are used?
how did they measure grammatical correctness?
how was quality of sentence transition measured?
what is the size of the dataset?
what manual evaluation is presented?
What downstream tasks are analyzed?
How much time takes the training of DistilBERT?
How do they compare representations performance obtained from a naive encoder versus ones learned from large amount of source language data?
Which pairs of languages do they consider similar enough to capture phonetic structure?
What are their results on this task?
How is the text segmented?
Which language pairs do they evaluate on?
Which languages do they explore?
What are two baseline methods?
How does model compare to the baselines?
Are the two paragraphs encoded independently?
Do they compare to previous work?
by how much did their model outperform the other models?
What is reordering in the context of the paper?
How does the paper use language model for context aware search?
what is the source of their dataset?
how many experts were there?
what is the size of the data collected?
did they use a crowdsourcing platform?
how was annotation conducted?
what does their dataset contain?
How do the authors measure the extent to which LGI has learned the task?
Which 8 tasks has LGI learned?
In what was does an LSTM mimic the prefrontal cortex?
In what way does an LSTM mimic the intra parietal sulcus?
How do the authors define imagination, or imagined scenarios?
Which classifiers did they experiment with?
Is the distribution of the edits uniform across all languages?
How did they identify what language the text was?
Which repositories did they collect from?
Which three features do they use?
Which languages are covered in the corpus?
Which BERT layers were combined to boost performance?
Which NLI data was used to improve the quality of the embeddings?
Which four QA datasets are examined?
Which two tasks from SentEval are the sentence embeddings evaluated against?
what classifiers did they train?
what combination of features helped improve the classification?
what linguistics features did they apply?
what is the state of the art in English?
Are results reported only on English data?
What type of model were the features used in?
What unsupervised approach was used to deduce the thematic information?
What profile features are used?
What textual features are used?
what other representations do they compare with?
what empirical evaluations performed?
which document understanding tasks did they evaluate on?
What private companies are members of consortium?
Does programme plans gathering and open sourcing some large dataset for Icelandic language?
What concrete software is planned to be developed by the end of the programme?
What other national language technology programs are described in the paper?
When did language technology start in Iceland?
what domain do the opinions fall under?
is this the first dataset with a grading scaling rather than binary?
what are the existing datasets for this task?
what is the size of the introduced dataset?
did they crowdsource annotations?
how was labeling done?
where does their dataset come from?
what tools did they use?
What is the performance of NJM?
How are the results evaluated?
How big is the self-collected corpus?
How is the funny score calculated?
Which of their proposed domain adaptation methods proves best overall?
Do they use evolutionary-based optimization algorithms as one of their domain adaptation approaches?
With how many languages do they experiment in the multilingual setup?
How do they extract target language bottleneck features?
Which intrisic measures do they use do evaluate obtained representations?
What results are obtained by their model?
What sources do the news come from?
What is the size of Multi-news dataset?
Which languages are explored?
What datasets are used in the paper?
What datasets do they look at?
Why is improvement on OntoNotes significantly smaller compared to improvement on WNUT 2017?
How is "complexity" and "confusability" of entity mentions defined in this work?
What are the baseline models?
What text classification tasks are considered?
Do they compare against other models?
What is episodic memory?
Are LSA-reduced n-gram features considered hand-crafted features?
What is the performance of the model on English, Spanish and Arabic?
How is this model different from a LSTM?
What does the cache consist of?
What languages is the model tested on?
What is a personalized language model?
Is the dataset used in other work?
What is the drawback to methods that rely on textual cues?
What community-based profiling features are used?
what user traits are taken into account?
does incorporating user traits help the task?
how many activities are in the dataset?
who annotated the datset?
how were the data instances chosen?
what social media platform was the data collected from?
Do they report results only for English data?
What conclusions do the authors draw from their experiments?
In what way does each classifier evaluate one of the syntactic or social properties which are salient for a tweet?
How is a per-word reward tuned with the perceptron algorithm?
What methods are used to correct the brevity problem?
Why does wider beam search hurt NMT?
What linguistic model does the conventional method use?
What is novel about the newly emerging CNN method, in comparison to well-established conventional method?
What lexical cues are used for humor recogition?
Do they evaluate only on English data?
How are the positive instances annotated? e.g. by annotators, or by laughter from the audience?
How you incorporate commonsense into an LSTM?
Which domain are the conversations in?
Which commonsense knowledge base are they using?
How did they obtain the dataset?
Are the recommendations specific to a region?
Did they experiment on this dataset?
What sized character n-grams do they use?
Which dataset do they evaluate on for headline generation?
What results do their embeddings obtain on machine translation?
How do they combine ordinary word embeddings and ones constructed from character n-grams?
By how much do they outperform previous state-of-the-art approaches?
What other scenarios can the bias mitigation methods be applied to?
Are the three bias mitigation methods combined in any model?
Which of the three bias mitigation methods is most effective?
What model architectures are used?
What pre-trained word embeddings are used?
What metrics are used to measure gender biases?
Do they ensure the that the architecture is differentiable everywhere after adding the Hungarian layer?
Which dataset(s) do they train on?
What is the model trained?
How large is the dataset used?
How exactly do they weigh between different statistical models?
Do they compare against state-of-the-art summarization approaches?
What showed to be the best performing combination of semantic and statistical model on the summarization task in terms of ROUGE score?
What QA system was used in this work?
Is the re-ranking approach described in this paper a transductive learning technique?
How big is the test set used for evaluating the proposed re-ranking approach?
What is the new metric?
What context is used when computing the embedding for an entity?
What are the limitations of the currently used quantitative metrics? e.g. why are they not 'good'?
What metrics are typically used to compare models?
Is there a benchmark to compare the different approaches?
What GAN and RL approaches are used?
What type of neural models are used?
What type of statistical models were used initially?
What was the proposed use of conversational agents in pioneering work?
What work pioneered the field of conversational agents?
How does this research compare to research going on in the US and USSR at this time?
What is the reason this research was not adopted in the 1960s?
What is included in the cybernetic methods mentioned?
What were the usual logical approaches of the time period?
what language was the data in?
which automatic metrics were used in evaluation?
how do humans judge the simplified sentences?
What previous approaches are presented for comparison?
What kind of data is used to train the model?
Does proposed approach use neural networks?
What machine learning techniques are used in the model architecture?
What language(s) is the model tested on?
By how much did their model outperform baselines?
Which baselines did they compare against?
What was their performance on this task?
What dataset did they use to evaluate?
How did they obtain part-of-speech tags?
what was their system's f1 score?
what were the baselines?
what emotion cause dataset was used?
what lexical features are extracted?
what word level sequences features are extracted?
what are the recent models they compare with?
what were their results on the hutter prize dataset?
what was their newly established state of the art results?
what regularisation methods did they look at?
what architectures were reevaluated?
what baseline models are trained?
what are the human evaluation metrics?
what automatic evaluation is performed?