DinoStackAI/qasper-rag huggingface dataset:
What subset of GLUE tasks is used?
Do they predict the sentiment of the review summary?
What is the performance difference of using a generated summary vs. a user-written one?
Which review dataset do they use?
What evaluation metrics did they look at?
What are the datasets used for the task?
What is the accuracy of the model for the six languages tested?
Which models achieve state-of-the-art performances?
Is the LSTM bidirectional?
What are the three languages studied in the paper?
Do they use pretrained models as part of their parser?
Which subtasks do they evaluate on?
Do they test their approach on large-resource tasks?
By how much do they, on average, outperform the baseline multilingual model on 16 low-resource tasks?
How do they compute corpus-level embeddings?
Which competitive relational classification models do they test?
Which tasks do they apply their method to?
Which knowledge bases do they use?
How do they gather human judgements for similarity between relations?
Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
What text classification task is considered?
What novel class of recurrent-like networks is proposed?
Is there a formal proof that the RNNs form a representation of the group?
How much bigger is Switchboard-2000 than Switchboard-300 database?
How big is Switchboard-300 database?
What crowdsourcing platform is used for data collection and data validation?
How is validation of the data performed?
Is audio data per language balanced in dataset?
What domains are detected in this paper?
Why do they think this task is hard?  What is the baseline performance?
Do they literally just treat this as "predict the next spell that appears in the text"?
Do they study frequent user responses to help automate modelling of those?
Do they use the same distance metric for both the SimCluster and K-means algorithm?
How do they generate the synthetic dataset?
how are multiple answers from multiple reformulated questions aggregated?
What is the average length of the claims?
What debate websites did they look at?
What crowdsourcing platform did they use?
Which machine baselines are used?
What challenges are highlighted?
What debate topics are included in the dataset?
What are the linguistic differences between each class?
What simple features are used?
What lexico-syntactic cues are used to retrieve sarcastic utterances?
what is the source of the song lyrics?
what genre was the most difficult to classify?
what word embedding techniques did they experiment with?
what genres do they songs fall under?
Is the filter based feature selection (FSE) a form of regularization?
How is human evaluation performed, what was the criteria?
Which one of the four proposed models performed best?
What metrics are used to measure performance of models?
How much is proposed model better than baselines in performed experiments?
What two benchmark datasets are used?
Do they compare to other models appart from HAN?
What are the datasets used
What evidence does visualizing the attention give to show that it helps to obtain a more robust understanding of semantics and sentiments?
Which SOTA models are outperformed?
What is the baseline for experiments?
What is the motivation for training bi-sense embeddings?
How many parameters does the model have?
How many characters are accepted as input of the language model?
What dataset is used for this task?
What features of the document are integrated into vectors of every sentence?
Is new approach tested against state of the art?
Is the dataset balanced across categories?
What supervised methods are used?
What categories does the dataset come from?
What are all machine learning approaches compared in this work?
Which patterns and rules are derived?
How are customer satisfaction, customer frustration and overall problem resolution data collected?
Which Twitter customer service industries are investigated?
Which dialogue acts are more suited to the twitter domain?
How many improvements on the French-German translation benchmark?
How do they align the synthetic data?
Where do they collect the synthetic data?
Do they propose a new model to better detect Arabic bots specifically?
How do they prevent the model complexity increasing with the increased number of slots?
What network architecture do they use for SIM?
How do they measure model size?
Does model uses pretrained Transformer encoders?
What was previous state of the art model?
How big is LibriSpeech dataset?
How do they evaluate their sentence representations?
Which model architecture do they for sentence encoding?
Which training objectives do they combine?
Has there been previous work on SNMT?
Which languages do they experiment on?
What corpora is used?
Do the authors report results only on English datasets?
How were breast cancer related posts compiled from the Twitter streaming API?
What machine learning and NLP methods were used to sift tweets relevant to breast cancer experiences?
Is this the first paper to propose a joint model for event and temporal relation extraction?
What datasets were used for this work?
What approach performs better in experiments global latent or sequence of fine-grained latent variables?
What baselines other than standard transformers are used in experiments?
What three conversational datasets are used for evaluation?
What previous approaches did this method outperform?
What data is used to build the embeddings?
How big is dataset used for fine-tuning model for detection of red flag medical symptoms in individual statements?
Is there any explanation why some choice of language pair is better than the other?
Does the additional training on supervised tasks hurt performance in some tasks?
Which translation system do they use to translate to English?
Which languages do they work with?
Which pre-trained English NER model do they use?
What are the best within-language data augmentation methods?
How much of the ASR grapheme set is shared between languages?
What is the performance of the model for the German sub-task A?
What are the languages used to test the model?
What is the Moral Choice Machine?
How is moral bias measured?
How do the authors define deontological ethical reasoning?
How does framework automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model?
What human judgement metrics are used?
What automatic evaluation metrics are used?
What state of the art models were used in experiments?
What five dialogue attributes were analyzed?
What three publicly available coropora are used?
How much training data is used?
How is the training data collected?
was bert used?
which existing metrics do they compare with?
How does their model differ from BERT?
Which metrics are they evaluating with?
What different properties of the posterior distribution are explored in the paper?
Why does proposed term help to avoid posterior collapse?
How does explicit constraint on the KL divergence term that authors propose looks like?
Did they experiment with the tool?
Is this software available to the public?
What shared task does this system achieve SOTA in?
How are labels propagated using this approach?
What information is contained in the social graph of tweet authors?
What were the five English subtasks?
How many CNNs and LSTMs were ensembled?
how many movie genres do they explore?
what evaluation metrics are discussed?
What is dataset used for news-driven stock movement prediction?
How much faster is training time for MGNC-CNN over the baselines?
By how much of MGNC-CNN out perform the baselines?
What dataset/corpus is this evaluated over?
What are the comparable alternative architectures?
Which state-of-the-art model is surpassed by 9.68% attraction score?
What is increase in percentage of humor contained in headlines generated with TitleStylist method (w.r.t. baselines)?
How is attraction score measured?
How is presence of three target styles detected?
How is fluency automatically evaluated?
What are the measures of "performance" used in this paper?
What are the languages they consider in this paper?
Did they experiment with tasks other than word problems in math?
What preprocessing techniques are used in the experiments?
What state of the art models are used in the experiments?
What tasks did they experiment with?
What multilingual parallel data is used for training proposed model?
How much better are results of proposed model compared to pivoting method?
What kind of Youtube video transcripts did they use?
Which SBD systems did they compare?
What makes it a more reliable metric?
How much in experiments is performance improved for models trained with generated adversarial examples?
How much dramatically results drop for models on generated adversarial examples?
What is discriminator in this generative adversarial setup?
What are benhmark datasets for paraphrase identification?
What representations are presented by this paper?
How is the delta-softmax calculated?
Are some models evaluated using this metric, what are the findings?
Where does proposed metric differ from juman judgement?
Where does proposed metric overlap with juman judgement?
Which baseline performs best?
Which baselines are explored?
Are any experiments performed to try this approach to word embeddings?
Which two datasets does the resource come from?
What model was used by the top team?
What is the size of the second dataset?
How large is the first dataset?
Who was the top-scoring team?
What supervised learning tasks are attempted with these representations?
Which techniques for word embeddings and topic models are used?
Why is big data not appropriate for this task?
What is an example of a computational social science NLP task?
What were the previous state of the art benchmarks?
How/where are the natural question generated?
What is the input to the differential network?
How do the authors define a differential network?
How do the authors define exemplars?
Is this a task other people have worked on?
Where did they get the data for this project?
Which major geographical regions are studied?
How strong is the correlation between the prevalence of the #MeToo movement and official reports [of sexual harassment]?
How are the topics embedded in the #MeToo tweets extracted?
How many tweets are explored in this paper?
Which geographical regions correlate to the trend?
How many followers did they analyze?
What two components are included in their proposed framework?
Which framework they propose in this paper?
Why MS-MARCO is different from SQuAD?
What were their results on the test set?
What was the baseline model?
What models are evaluated with QAGS?
Do they use crowdsourcing to collect human judgements?
Which modifications do they make to well-established Seq2seq architectures?
How do they measure the size of models?
Do they reduce the number of parameters in their architecture compared to other direct text-to-speech models?
Do they use pretrained models?
How was speed measured?
Where did they collect their dataset from?
How does the model work if no treebank is available?
How many languages have this parser been tried on?
Do they use attention?
What non-annotated datasets are considered?
Did they compare to Transformer based large language models?
Which baselines are they using?
What two types the Chinese reading comprehension dataset consists of?
For which languages most of the existing MRC datasets are created?
How did they induce the CFG?
By how much do they outperform basic greedy and cross-entropy beam decoding?
Do they provide a framework for building a sub-differentiable for any final loss metric?
Do they compare partially complete sequences (created during steps of beam search) to gold/target sequences?
Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
How are different domains weighted in WDIRL?
How is DIRL evaluated?
Which sentiment analysis tasks are addressed?
Which NLP area have the highest average citation for woman author?
What aspect of NLP research is examined?
How many papers are used in experiment?
What ensemble methods are used for best model?
What hyperparameters have been tuned?
How much F1 was improved after adding skip connections?
Where do they retrieve neighbor n-grams from in their approach?
To which systems do they compare their results against?
Does their combination of a non-parametric retrieval and neural network get trained end-to-end?
Which similarity measure do they use in their n-gram retrieval approach?
Where is MVCNN pertained?
What are the effects of extracting features of multigranular phrases?
What are the effects of diverse versions of pertained word embeddings?
How is MVCNN compared to CNN?
Are the models compared to some baseline models?
What are the in-house data employed?
What are the three datasets used in the paper?
What elements of natural language processing are proposed to analyze qualitative data?
How does the method measure the impact of the event on market prices?
How is sentiment polarity measured?
Which part of the joke is more important in humor?
What is improvement in accuracy for short Jokes in relation other types of jokes?
What kind of humor they have evaluated?
How they evaluate if joke is humorous or not?
Do the authors have a hypothesis as to why morphological agreement is hardly learned by any model?
Which models are best for learning long-distance movement?
Where does the data in CoLA come from?
How is the CoLA grammatically annotated?
What baseline did they compare Entity-GCN to?
Did they use a relation extraction method to construct the edges in the graph?
How did they get relations between mentions?
How did they detect entity mentions?
What classical machine learning algorithms are used?
What are the different methods used for different corpora?
In which domains is sarcasm conveyed in different ways?
What modalities are being used in different datasets?
What is the difference between Long-short Term Hybrid Memory and LSTMs?
What provisional explanation do the authors give for the impact of document context?
What document context was added?
What were the results of the first experiment?
How big is the evaluated dataset?
By how much does their model outperform existing methods?
What are the existing methods mentioned in the paper?
Does having constrained neural units imply word meanings are fixed across different context?
Do they perform a quantitative analysis of their model displaying knowledge distortions?
Do all the instances contain code-switching?
What embeddings do they use?
Do they perform some annotation?
Do they use dropout?
What are the other models they compare to?
What are the sources of the data?
What is the new labeling strategy?
Which future direction in NLG are discussed?
What experimental phenomena are presented?
How strategy-based methods handle obstacles in NLG?
How architecture-based method handle obstacles in NLG?
How are their changes evaluated?
What clustering algorithm is used on top of the VerbNet-specialized representations?
What are the six target languages?
what classifiers were used in this paper?
what are their evaluation metrics?
what types of features were used?
what lexical features did they experiment with?
How are seed dictionaries obtained by fully unsupervised methods?
How does BLI measure alignment quality?
What methods were used for unsupervised CLWE?
What is the size of the released dataset?
What is the most common error type?
Which OpenIE systems were used?
What is the role of crowd-sourcing?
How are meta vertices computed?
How are graphs derived from a given text?
how are the bidirectional lms obtained?
what metrics are used in evaluation?
what previous systems were compared to?
what are the evaluation datasets?
Are datasets publicly available?
Are this models usually semi/supervised or unsupervised?
Is there any concrete example in the paper that shows that this approach had huge impact on drug discovery?
How do they evaluate interpretability?
What types of word representations are they evaluating?
What is a word confusion network?
What type of simulations of real-time data feeds are used for validaton?
How are FHIR and RDF combined?
What are the differences between FHIR and RDF?
What do FHIR and RDF stand for?
What is the motivation behind the work? Why question generation is an important task?
Why did they choose WER as evaluation metric?
What evaluation metrics were used in the experiment?
What kind of instructional videos are in the dataset?
What baseline algorithms were presented?
What is the source of the triples?
How much better is performance of the proposed model compared to the state of the art in these various experiments?
What was state of the art on SemEval-2014 task4 Restaurant and Laptop dataset?
What was previous state-of-the-art on four Chinese reviews datasets?
In what four Chinese review datasets does LCF-ATEPC achieves state of the art?
did they use a crowdsourcing platform for annotations?
where does the data come from?
What is the criteria for a good metric?
What are the three limitations?
What is current state-of-the-art model?
Which language(s) are found in the WSD datasets?
What datasets are used for testing?
How does TP-N2F compare to LSTM-based Seq2Seq in terms of training and inference speed?
What is the performance proposed model achieved on AlgoList benchmark?
What is the performance proposed model achieved on MathQA?
How do previous methods perform on the Switchboard Dialogue Act and DailyDialog datasets?
What is dialogue act recognition?
Does the performance necessarily drop when more control is desired?
How is the model trained to do only content selection?
What is the baseline model used?
Is this auto translation tool based on neural networks?
What are results of public code repository study?
Where is the dataset from?
What data augmentation techniques are used?
How are the models evaluated?
In which languages did the approach outperform the reported results?
What eight language are reported on?
What are the components of the multilingual framework?
Is the proposed method compared to previous methods?
Which is the baseline model?
What is the main contribution of the paper?
What training settings did they try?
How do they get the formal languages?
Are the unobserved samples from the same distribution as the training data?
By how much does their model outperform the baseline in the cross-domain evaluation?
What are the performance results?
What is a confusion network or lattice?
How close do clusters match to ground truth tone categories?
What multilingual word representations are used?
Do the authors identify any cultural differences in irony use?
What neural architectures are used?
What text-based features are used?
What monolingual word representations are used?
What type of RNN is used?
What do they constrain using integer linear programming?
Do they build one model per topic or on all topics?
Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?
Do the authors report on English datasets only?
Which supervised learning algorithms are used in the experiments?
How in YouTube content translated into a vector format?
How is the ground truth of gang membership established in this dataset?
Do they single out a validation set from the fixed SRE training set?
How well does their system perform on the development set of SRE?
Which are the novel languages on which SRE placed emphasis on?
Does this approach perform better than context-based word embeddings?
Have the authors tried this approach on other languages?
What features did they train on?
What off-the-shelf QA model was used to answer sub-questions?
How large is the improvement over the baseline?
What is the strong baseline that this work outperforms?
Which model architecture do they use to build a model?
Which metrics do they use to evaluate simultaneous translation?
What models other than SAN-VOES are trained on new PlotQA dataset?
Do the authors report only on English language data?
What dataset of tweets is used?
What external sources of information are used?
What linguistic features are used?
What are the key issues around whether the gold standard data produced in such an annotation is reliable?
How were the machine learning papers from ArXiv sampled?
What are the core best practices of structured content analysis?
In what sense is data annotation similar to structured content analysis?
What additional information is found in the dataset?
Over what period of time were the tweets collected?
Do the authors suggest any future extensions to this work?
How are the keywords associated with events such as protests selected?
What classification models were used?
What is different in BERT-gen from standard BERT?
How are multimodal representations combined?
What is the problem with existing metrics that they are trying to address?
How do they evaluate their proposed metric?
How are discourse embeddings analyzed?
What was the previous state-of-the-art?
How are discourse features incorporated into the model?
What discourse features are used?
How are proof scores calculated?
What external sources are used?
What submodules does the model consist of?
How they add human prefference annotation to fine-tuning process?
What previous automated evalution approaches authors mention?
How much better peformance is achieved in human evaluation when model is trained considering proposed metric?
What is the training objective of their pair-to-sequence model?
How do they ensure the generated questions are unanswerable?
Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?
What conclusions are drawn from these experiments?
What experiments are presented?
What is specific about the specific embeddings?
What embedding algorithm is used to build the embeddings?
How was the KGR10 corpus created?
How big are improvements with multilingual ASR training vs single language training?
How much transcribed data is available for for Ainu language?
What is the difference between speaker-open and speaker-closed setting?
What baseline approaches do they compare against?
How do they train the retrieval modules?
How do they model the neural retrieval modules?
Retrieval at what level performs better, sentence level or paragraph level?
How much better performance of proposed model compared to answer-selection models?
How are some nodes initially connected based on text structure?
how many domains did they experiment with?
what pretrained models were used?
What domains are contained in the polarity classification dataset?
What machine learning algorithms are used?
What is a string kernel?
Which dataset do they use to learn embeddings?
How do they correlate NED with emotional bond levels?
Which languages are evaluated?
Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?
What will be in focus for future work?
What are the existing end-to-end ASR approaches for the French language?
How much is decoding speed increased by increasing encoder and decreasing decoder depth?
What language are the conversations in?
Which two schemes are used?
Does Grail accept Prolog inputs?
What formalism does Grail use?
Which components of QA and QG models are shared during training?
How much improvement does jointly learning QA and QG give, compared to only training QA?
Do they test their word embeddings on downstream tasks?
What dimensions of word embeddings do they produce using factorization?
On which dataset(s) do they compute their word embeddings?
Do they measure computation time of their factorizations compared to other word embeddings?
What datasets are experimented with?
What model do they train?
What are the eight features mentioned?
How do slot binary classifiers improve performance?
What baselines have been used in this work?
How successful are they at matching names of authors in Japanese and English?
what bottlenecks were identified?
Is the RNN model evaluated against any baseline?
Which languages are used in the paper?
What baselines are used?
Which model is used to capture the implicit structure?
How is the robustness of the model evaluated?
How is the effectiveness of the model evaluated?
Do they assume sentence-level supervision?