DinoStackAI/qasper-rag huggingface dataset:
What is the seed lexicon?
What are the results?
How are relations used to propagate polarity?
How big is the Japanese data?
What are labels available in dataset for supervision?
How does their model learn using mostly raw data?
How big is seed lexicon used for training?
How large is raw corpus used for training?
Does the paper report macro F1?
How is the annotation experiment evaluated?
What are the aesthetic emotions formalized?
How do the various social phenomena examined manifest in different types of communities?
What patterns do they observe about how user engagement varies with the characteristics of a community?
How did the select the 300 Reddit communities for comparison?
How do the authors measure how temporally dynamic a community is?
How do the authors measure how distinctive a community is?
What data is the language model pretrained on?
What baselines is the proposed model compared against?
How is the clinical text structuring task defined?
What are the specific tasks being unified?
Is all text in this dataset a question, or are there unrelated sentences in between questions?
How many questions are in the dataset?
How they introduce domain-specific features into pre-trained language model?
How big is QA-CTS task dataset?
How big is dataset of pathology reports collected from Ruijing Hospital?
What are strong baseline models in specific tasks?
What aspects have been compared between various language models?
what classic language models are mentioned in the paper?
What is a commonly used evaluation metric for language models?
Which dataset do they use a starting point in generating fake reviews?
What kind of model do they use for detection?
Does their detection tool work better than human detection?
How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk?
Which baselines did they compare?
How many attention layers are there in their model?
Is the explanation from saliency map correct?
How is embedding quality assessed?
What are the three measures of bias which are reduced in experiments?
What turn out to be more important high volume or high quality data?
What two architectures are used?
What were the word embeddings trained on?
Which word embeddings are analysed?
How is quality of the citation measured?
How is the intensity of the PTSD established?
How is LIWC incorporated into this system?
How many twitter users are surveyed using the clinically validated survey?
Which clinically validated survey tools are used?
Did they experiment with the dataset?
what sentiment sources do they compare with?
Is the method described in this work a clustering-based method?
How are the different senses annotated/labeled?
Was any extrinsic evaluation carried out?
Is the performance compared against a baseline model?
What is the accuracy reported by state-of-the-art methods?
Which vision-based approaches does this approach outperform?
What baseline is used for the experimental setup?
Which languages are used in the multi-lingual caption model?
Did they experiment on all the tasks?
What models did they compare to?
What datasets are used in training?
Which GAN do they use?
Do they evaluate grammaticality of generated text?
Which corpora do they use?
Do they report results only on English datasets?
How do the authors define or exemplify 'incorrect words'?
Do they test their approach on a dataset without incomplete data?
Should their approach be applied only when dealing with incomplete data?
By how much do they outperform other models in the sentiment in intent classification tasks?
What is the sample size of people used to measure user satisfaction?
What are all the metrics to measure user engagement?
What the system designs introduced?
Do they specify the model they use for Gunrock?
Do they gather explicit user satisfaction data on Gunrock?
How do they correlate user backstory queries to user satisfaction?
What is the baseline for the experiments?
Which experiments are perfomed?
Is ROUGE their only baseline?
what language models do they use?
What misbehavior is identified?
Which attention mechanisms do they compare?
Which paired corpora did they use in the other experiment?
By how much does their system outperform the lexicon-based models?
Which lexicon-based models did they compare with?
How many comments were used?
How many articles did they have?
What news comment dataset was used?
By how much do they outperform standard BERT?
How do they combine text representations with the knowledge graph embeddings?
What is the algorithm used for the classification tasks?
Is the outcome of the LDA analysis evaluated in any way?
What is the corpus used in the study?
What are the traditional methods to identifying important attributes?
What do you use to calculate word/sub-word embeddings
Did they propose other metrics?
Which real-world datasets did they use?
What are the country-specific drivers of international development rhetoric?
Is the dataset multilingual?
How are the main international development topics that states raise identified?
What experiments do the authors present to validate their system?
What components is the QnAMaker composed of?
How they measure robustness in experiments?
What experiments with large-scale features are performed?
Which ASR system(s) is used in this work?
What are the series of simple models?
Over which datasets/corpora is this work evaluated?
Is the semantic hierarchy representation used for any task?
What are the corpora used for the task?
Is the model evaluated?
What new metrics are suggested to track progress?
What intrinsic evaluation metrics are used?
What experimental results suggest that using less than 50% of the available training examples might result in overfitting?
What multimodality is available in the dataset?
What are previously reported models?
How better is accuracy of new model compared to previously reported models?
How does the scoring model work?
How does the active learning model work?
Which neural network architectures are employed?
Did the annotators agreed and how much?
How many subjects have been used to create the annotations?
What datasets are used to evaluate this approach?
How is this approach used to detect incorrect facts?
Can this adversarial approach be used to directly improve model accuracy?
what are the advantages of the proposed model?
what are the state of the art approaches?
What are the benchmark models?
How was the corpus annotated?
What models other than standalone BERT is new model compared to?
How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?
What are three downstream task datasets?
What is dataset for word probing task?
What datasets used for evaluation?
what are the mentioned cues?
What approaches without reinforcement learning have been tried?
What classification approaches were experimented for this task?
Did classification models perform better than previous regression one?
What are the main sources of recall errors in the mapping?
Do they look for inconsistencies between different languages' annotations in UniMorph?
Do they look for inconsistencies between different UD treebanks?
Which languages do they validate on?
How is face and audio data analysis evaluated?
What is the baseline method for the task?
What are the emotion detection tools used for audio and face input?
what amounts of size were used on german-english?
what were their experimental results in the low-resource dataset?
what are the methods they compare with in the korean-english dataset?
what pitfalls are mentioned in the paper?
Does the paper report the results of previous models applied to the same tasks?
What are the causal mapping methods employed?
What is the previous work's model?
How is the dataset collected?
What models do previous work use?
Does the dataset contain content from various social media platforms?
How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment?
Are language-specific and language-neutral components disjunctive?
How they show that mBERT representations can be split into a language-specific component and a language-neutral component?
What challenges this work presents that must be solved to build better language-neutral representations?
What pretrained LM is used?
What approaches they propose?
What faithfulness criteria does they propose?
Which are three assumptions in current approaches for defining faithfulness?
Which are key points in guidelines for faithfulness evaluation?
Did they use the state-of-the-art model to analyze the attention?
How many layers are there in their model?
What MC abbreviate for?
how much of improvement the adaptation model can get?
what is the architecture of the baseline model?
What is the exact performance on SQUAD?
What simpler models do they look at?
What benchmark datasets are used for the link prediction task?
What are state-of-the art models for this task?
How better does HAKE model peform than state-of-the-art methods?
How are entities mapped onto polar coordinate system?
What additional techniques are incorporated?
What is the architecture of the system?
What additional techniques could be incorporated to further improve accuracy?
What programming language is target language?
What dataset is used to measure accuracy?
Is text-to-image synthesis trained is suppervized or unsuppervized manner?
What challenges remain unresolved?
What is the conclusion of comparison of proposed solution?
What is typical GAN architecture for each text-to-image synhesis group?
Where do they employ feature-wise sigmoid gating?
Which model architecture do they use to obtain representations?
Which downstream sentence-level tasks do they evaluate on?
Which similarity datasets do they use?
Are there datasets with relation tuples annotated, how big are datasets available?
Which one of two proposed approaches performed better in experiments?
What is previous work authors reffer to?
How higher are F1 scores compared to previous work?
what is the supervised model they developed?
what is the size of this built corpus?
what crowdsourcing platform is used?
Which deep learning model performed better?
What was their performance on the dataset?
Did the authors use crowdsourcing platforms?
What language do the agents talk in?
What evaluation metrics did the authors look at?
What data did they use?
How is the accuracy of the system measured?
How is an incoming claim used to retrieve similar factchecked claims?
What existing corpus is used for comparison in these experiments?
What dataset was used in the experiment?
Did they use any crowdsourcing platform?
How was the dataset annotated?
How many label options are there in the multi-label task?
Who are the experts?
Who is the crowd in these experiments?
How do you establish the ground truth of who won a debate?
How much better is performance of proposed method than state-of-the-art methods in experiments?
What further analysis is done?
What seven state-of-the-art methods are used for comparison?
What three datasets are used to measure performance?
How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
What are recent works on knowedge graph embeddings authors mention?
Do the authors mention any confounds to their study?
Is the data acquired under distant supervision verified by humans at any stage?
Do the authors equate drunk tweeting with drunk texting?
What corpus was the source of the OpenIE extractions?
Is an entity linking process used?
Are the OpenIE extractions all triples?
What method was used to generate the OpenIE extractions?
Can the method answer multi-hop questions?
What was the textual source to which OpenIE was applied?
What OpenIE method was used to generate the extractions?
Is their method capable of multi-hop reasoning?
Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?
What evaluation is conducted?
Which corpus of synsets are used?
What measure of semantic similarity is used?
Which retrieval system was used for baselines?
What word embeddings were used?
What type of errors were produced by the BLSTM-CNN-CRF system?
How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?
What supplemental tasks are used for multitask learning?
Did they experimnet in other languages?
Do they use multi-attention heads?
How big is their model?
How is their model different from BERT?
What datasets were used?
How did they do compared to other teams?
Which tested technique was the worst performer?
How many emotions do they look at?
What are the baseline benchmarks?
How many annotators were there?
Can SCRF be used to pretrain the model?
What conclusions are drawn from the syntactic analysis?
What type of syntactic analysis is performed?
How is it demonstrated that the correct gender and number information is injected using this system?
Which neural machine translation system is used?
What are the components of the black-box context injection system?
What normalization techniques are mentioned?
What features do they experiment with?
Which architecture is their best model?
What approach did previous models use for multi-span questions?
How they use sequence tagging to answer multi-span questions?
What is the performance of proposed model on entire DROP dataset?
What is the previous model that attempted to tackle multi-span questions as a part of its design?
How is the expectation regularization loss defined?
What were the non-neural baselines used for the task?
Which publicly available NLU dataset is used?
What metrics other than entity tagging are compared?
Do they provide decision sequences as supervision while training models?
What are the models evaluated on?
How do they train models in this setup?
What commands does their setup provide to models seeking information?
What models do they propose?
What is the results of multimodal compared to unimodal models?
What is author's opinion on why current multimodal models cannot outperform models analyzing only text?
What metrics are used to benchmark the results?
How is data collected, manual collection or Twitter api?
How many tweats does MMHS150k contains, 150000?
What unimodal detection models were used?
What different models for multimodal detection were proposed?
What annotations are available in the dataset - tweat used hate speach or not?
What were the evaluation metrics used?
Which popular clustering methods did they experiment with?
Does pre-training on general text corpus improve performance?
What neural configurations are explored?
Are the Transformers masked?
How is this problem evaluated?
What datasets do they use?
What evaluation metrics were used?
Where did the real production data come from?
What feedback labels are used?
What representations for textual documents do they use?
Which dataset(s) do they use?
How do they evaluate knowledge extraction performance?
What is CamemBERT trained on?
Which tasks does CamemBERT not improve on?
What is the state of the art?
How much better was results of CamemBERT than previous results on these tasks?
Was CamemBERT compared against multilingual BERT on these tasks?
What data is used for training CamemBERT?
What are the state of the art measures?
What controversial topics are experimented with?
What social media platform is observed?
How many languages do they experiment with?
What is the current SOTA for sentiment analysis on Twitter at the time of writing?
What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?
How many sentence transformations on average are available per unique sentence in dataset?
What annotations are available in the dataset?
How are possible sentence transformations represented in dataset, as new sentences?
What are all 15 types of modifications ilustrated in the dataset?
Is this dataset publicly available?
Are some baseline models trained on this dataset?
Do they do any analysis of of how the modifications changed the starting set of sentences?
How do they introduce language variation?
How better are state-of-the-art results than this model?
Are results reported only on English datasets?
Which three Twitter sentiment classification datasets are used for experiments?
What semantic rules are proposed?
Which knowledge graph completion tasks do they experiment with?
What is the dataset used to train the model?
What is the performance of the model?
Is the model evaluated against a CNN baseline?
Has STES been previously used in the literature to evaluate similar tasks?
What are the baseline models mentioned in the paper?
What kind of settings do the utterances come from?
On what datasets is the new model evaluated on?
Does the new objective perform better than the original objective bert is trained on?
Do the authors report performance of conditional bert on tasks without data augmentation?
Do they survey visual question generation work?
What learning paradigms do they cover in this survey?
What are all the input modalities considered in prior work in question generation?
What is their model?
Do they evaluate on NER data sets?
What previously proposed methods is this method compared against?
How is effective word score calculated?
Why is supporting fact supervision necessary for DMN?
What does supporting fact supervision mean?
What changes they did on input module?
What improvements they did for DMN?
How does the model circumvent the lack of supporting facts during training?
Does the DMN+ model establish state-of-the-art ?
Is this style generator compared to some baseline?
How they perform manual evaluation, what is criteria?
What metrics are used for automatic evaluation?
How they know what are content words?
How they model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions?
What insights into the relationship between demographics and mental health are provided?
What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter?
How do this framework facilitate demographic inference from social media?
What types of features are used from each data type?
How is the data annotated?
Where does the information on individual-level demographics come from?
What is the source of the user interaction data?
What is the source of the textual data?
What is the source of the visual data?
Do they perform manual evaluation?
Do they compare against Noraset et al. 2017?
What is a sememe?
What language tasks did they experiment on?
What result from experiments suggest that natural language based agents are more robust?
What experiments authors perform?
How is state to learn and complete tasks represented via natural language?
How does the model compare with the MMR baseline?
Does the paper discuss previous models which have been applied to the same task?
Which datasets are used in the paper?
How does the parameter-free model work?
How do they quantify moral relevance?
Which fine-grained moral dimension examples do they showcase?
How well did the system do?
How is the information extracted?
What are some guidelines in writing input vernacular so model can generate
How much is proposed model better in perplexity and BLEU score than typical UMT models?
What were the evaluation metrics?
What were the baseline systems?
Which dialog datasets did they experiment with?
Do they train a different training method except from scheduled sampling?
What classifier is used for emergency categorization?
Do the tweets come from any individual?
Do they release MED?
What NLI models do they analyze?
How do they define upward and downward reasoning?
What is monotonicity reasoning?
What other relations were found in the datasets?
How does the ensemble annotator extract the final label?
How were dialogue act labels defined?
How many models were used?
Do they annotate their own dataset or use an existing one?
Does their neural network predict a single offset in a recording?
What kind of neural network architecture do they use?
How are aspects identified in aspect extraction?
What web and user-generated NER datasets are used for the analysis?
Which unlabeled data do they pretrain with?
How many convolutional layers does their model have?
Do they explore how much traning data is needed for which magnitude of improvement for WER?
How are character representations from various languages joint?
On which dataset is the experiment conducted?
Do they train their own RE model?
How big are the datasets?
What languages do they experiment on?
How big is data provided by this research?
How they complete a user query prefix conditioned upon an image?
Did the collection process use a WoZ method?
By how much did their model outperform the baseline?
What baselines did they compare their model with?
Did the authors use a crowdsourcing platform?
How were the navigation instructions collected?
What language is the experiment done in?
What additional features are proposed for future work?
What datasets did the authors use?
How is morphology knowledge implemented in the method?
How does the word segmentation method work?
How many layers do they use in their best performing network?
Do they just sum up all the loses the calculate to end up with one single loss?
Are agglutinative languages used in the prediction of both prefixing and suffixing languages?
What is an example of a prefixing language?
How is the performance on the task evaluated?
What are the tree target languages studied in the paper?
Is the model evaluated against any baseline?
Does the paper report the accuracy of the model?
How is the performance of the model evaluated?
What are the different bilingual models employed?
How does the well-resourced language impact the quality of the output?
did they outperform previous methods?
How is order of binomials tracked across time?
What types of various community texts have been investigated for exploring global structure of binomials?
Are there any new finding in analasys of trinomials that was not present binomials?
What new model is proposed for binomial lists?
How was performance of previously proposed rules at very large scale?
What previously proposed rules for predicting binoial ordering are used?
What online text resources are used to test binomial lists?
How do they model a city description using embeddings?
How do they obtain human judgements?
Which clustering method do they use to cluster city description embeddings?
What are the performance metrics used?
Which datasets are used to evaluate performance?
How many samples did they generate for the artificial language?
Which of their training domains improves performance the most?
Do they fine-tune their model on the end task?
Why does not the approach from English work on other languages?
How do they measure grammaticality?
Which model do they use to convert between masculine-inflected and feminine-inflected sentences?
What is the size of the datasets employed?
Why does their model do better than prior models?
How large is the dataset they used?
Which coreference resolution systems are tested?
How big is improvement in performances of proposed model over state of the art?
What two large datasets are used for evaluation?
What context modelling methods are evaluated?
What are two datasets models are tested on?
How big is the improvement over the state-of-the-art results?
Is the model evaluated against other Aspect-Based models?
Is the baseline a non-heirarchical model like BERT?
Which inter-annotator metric do they use?
How high is the inter-annotator agreement?
How are resources adapted to properties of Chinese text?
How better are results compared to baseline models?
What models that rely only on claim-specific linguistic features are used as baselines?
How is pargmative and discourse context added to the dataset?
How big is dataset used for training/testing?
Is there any example where geometric property is visible for context similarity between words?
What geometric properties do embeddings display?
How accurate is model trained on text exclusively?
What was their result on Stance Sentiment Emotion Corpus?
What performance did they obtain on the SemEval dataset?
How is multi-tasking performed?
What are the datasets used for training?
What is the previous state-of-the-art model?