DinoStackAI/qasper-rag huggingface dataset:
How can the classifier facilitate the annotation task for human annotators?
What recommendations are made to improve the performance in future?
What type of errors do the classifiers use?
What neural classifiers are used?
What languages are included in the dataset?
What dataset is used for this study?
What proxies for data annotation were used in previous datasets?
What are the supported natural commands?
What is the size of their collected dataset?
Did they compare against other systems?
What intents does the paper explore?
What kind of features are used by the HMM models, and how interpretable are those?
What kind of information do the HMMs learn that the LSTMs don't?
Which methods do the authors use to reach the conclusion that LSTMs and HMMs learn complementary information?
Which publicly available datasets are used?
What embedding algorithm and dimension size are used?
What data are the embeddings trained on?
how much was the parameter difference between their model and previous methods?
how many parameters did their model use?
which datasets were used?
what was their system's f1 performance?
What language pairs did they experiment with?
How much more coverage is in the new dataset?
How was coverage measured?
How was quality measured?
How was the corpus obtained?
How are workers trained?
What is different in the improved annotation protocol?
How was the previous dataset annotated?
What data were they used to train the multilingual encoder?
From when are many VQA datasets collected?
What is task success rate achieved?
What simulations are performed by the authors to validate their approach?
Does proposed end-to-end approach learn in reinforcement or supervised learning manner?
Are synonymous relation taken into account in the Japanese-Vietnamese task?
Is the supervised morphological learner tested on Japanese?
What non-contextual properties do they refer to?
What are their proposed features?
What are overall baseline results on new this new task?
What metrics are used in evaluation of this task?
Do authors provide any explanation for intriguing patterns of word being echoed?
What features are proposed?
Which datasets are used to train this model?
How is performance of this system measured?
How many questions per image on average are available in dataset?
Is machine learning system underneath similar to image caption ML systems?
How big dataset is used for training this system?
How do they obtain word lattices from words?
Which metrics do they use to evaluate matching?
Which dataset(s) do they evaluate on?
Do they propose any further additions that could be made to improve generalisation to unseen speakers?
What are the characteristics of the dataset?
What type of models are used for classification?
How many instances does their dataset have?
What model do they use to classify phonetic segments?
How many speakers do they have in the dataset?
How better is proposed method than baselines perpexity wise?
How does the multi-turn dialog system learns?
How is human evaluation performed?
Is some other metrics other then perplexity measured?
What two baseline models are used?
What is the baseline model for the agreement-based mode?
Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages?
Who manually annotated the semantic roles for the set of learner texts?
By how much do they outperform existing state-of-the-art VQA models?
How do they measure the correlation between manual groundings and model generated ones?
How do they obtain region descriptions and object annotations?
Which model generalized the best?
Which models were compared?
Which datasets were used?
Is the data all in Vietnamese?
What classifier do they use?
What is private dashboard?
What is public dashboard?
What other interesting correlations are observed?
what is the state of the art for ranking mc test answers?
What evaluation metric is used?
What are three main machine translation tasks?
How big is improvement in performance over Transformers?
How do they determine the opinion summary?
Do they explore how useful is the detection history and opinion summary?
Which dataset(s) do they use to train the model?
By how much do they outperform state-of-the-art methods?
What is the average number of turns per dialog?
What baseline models are offered?
Which six domains are covered in the dataset?
What other natural processing tasks authors think could be studied by using word embeddings?
What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?
Do the use word embeddings alone or they replace some previous features of the model with word embeddings?
On what model architectures are previous co-occurence networks based?
Is model explanation output evaluated, what metric was used?
How many annotators are used to write natural language explanations to SNLI-VE-2.0?
How many natural language explanations are human-written?
How much is performance difference of existing model between original and corrected corpus?
What is the class with highest error rate in SNLI-VE?
What is the dataset used as input to the Word2Vec algorithm?
Are the word embeddings tested on a NLP task?
Are the word embeddings evaluated?
How big is dataset used to train Word2Vec for the Italian Language?
How does different parameter settings impact the performance and semantic capacity of resulting model?
What dataset is used for training Word2Vec in Italian language?
How are the auxiliary signals from the morphology table incorporated in the decoder?
What type of morphological information is contained in the "morphology table"?
Which machine learning models are used?
What methodology is used to compensate for limited labelled data?
Which five natural disasters were examined?
Which social media platform is explored?
What model did they use?
What patterns were discovered from the stories?
Does the performance increase using their method?
What tasks are they experimenting with in this paper?
How do they select answer candidates for their QA task?
How do they extract causality from text?
What is the source of the "control" corpus?
What are the selection criteria for "causal statements"?
Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora?
how do they collect the comparable corpus?
How do they collect the control corpus?
What was the inter-annotator agreement?
Are resolution mode variables hand crafted?
What are resolution model variables?
Is the model presented in the paper state of the art?
What problems are found with the evaluation scheme?
How many intents were classified?
What was the result of the highest performing system?
What metrics are used in the evaluation?
How do they measure the quality of summaries?
Does their model also take the expected answer style as input?
What do they mean by answer styles?
Is there exactly one "answer style" per dataset?
What is an "answer style"?
What was the previous state of the art model for this task?
What syntactic structure is used to model tones?
What visual information characterizes tones?
How do they demonstrate the robustness of their results?
What baseline and classification systems are used in experiments?
How are the EAU text spans annotated?
Which Twitter corpus was used to train the word vectors?
How does proposed word embeddings compare to Sindhi fastText word representations?
Are trained word embeddings used for any other NLP task?
How many uniue words are in the dataset?
How is the data collected, which web resources were used?
What trends are found in musical preferences?
Which decades did they look at?
How many genres did they collect from?
Does the paper mention other works proposing methods to detect anglicisms in Spanish?
What is the performance of the CRF model on the task described?
Does the paper motivate the use of CRF as the baseline model?
What are the handcrafted features used?
What are three new proposed architectures?
How much does the standard metrics for style accuracy vary on different re-runs?
Which baseline methods are used?
Which datasets are used in experiments?
What regularizers were used to encourage consistency in back translation cycles?
What are new best results on standard benchmark?
How better is performance compared to competitive baselines?
What 6 language pairs is experimented on?
What are current state-of-the-art methods that consider the two tasks independently?
Which pre-trained transformer do they use?
What is the FEVER task?
How is correctness of automatic derivation proved?
Do they conduct any human evaluation?
What dataset do they use for experiments?
How do they enrich the positional embedding with length information
How do they condition the output to a given target-source class?
Which languages do they focus on?
Do they experiment with combining both methods?
What state-of-the-art models are compared against?
Does API provide ability to connect to models written in some other deep learning framework?
Is this library implemented into Torch or is framework agnostic?
What baselines are used in experiments?
What general-purpose optimizations are included?
what baseline do they compare to?
How does this compare to traditional calibration methods like Platt Scaling?
What's the input representation of OpenIE tuples into the model?
What statistics on the VIST dataset are reported?
What are puns?
What are the categories of code-mixed puns?
How is dialogue guided to avoid interactions that breach procedures and processes only known to experts?
What is meant by semiguided dialogue, what part of dialogue is guided?
Is CRWIZ already used for data collection, what are the results?
How does framework made sure that dialogue will not breach procedures?
How do they combine the models?
What context do they use?
What architecture has the neural network?
How is human interaction consumed by the model?
How do they evaluate generated stories?
What is used a baseline?
What contextual features are used?
Where are the cybersecurity articles used in the model sourced from?
How is the data in RAFAEL labelled?
How do they handle polysemous words in their entity library?
How is the fluctuation in the sense of the word and its neighbors measured?
What language do they look at?
Where does the vocabulary come from?
How do they measure performance?
Which four QA systems do they use?
Do they test performance of their approaches using human judgements?
What are the sizes of both datasets?
What are lyrical topics present in the metal genre?
By how much does SPNet outperforms state-of-the-art abstractive summarization methods on evaluation metrics?
What automatic and human evaluation metrics are used to compare SPNet to its counterparts?
Is it expected to have speaker role, semantic slot and dialog domain annotations in real world datasets?
How does SPNet utilize additional speaker role, semantic slot and dialog domain annotations?
What are previous state-of-the-art document summarization methods used?
How does new evaluation metric considers critical informative entities?
Is new evaluation metric extension of ROGUE?
What measures were used for human evaluation?
What automatic metrics are used for this task?
Are the models required to also generate rationales?
Are the rationales generated after the sentences were written?
Are the sentences in the dataset written by humans who were shown the concept-sets?
Where do the concept sets come from?
How big are improvements of MMM over state of the art?
What out of domain datasets authors used for coarse-tuning stage?
What four representative datasets are used for bechmark?
What baselines did they consider?
What are the problems related to ambiguity in PICO sentence prediction tasks?
How is knowledge retrieved in the memory?
How is knowledge stored in the memory?
What are the relative improvements observed over existing methods?
What is the architecture of the neural network?
What methods is RelNet compared to?
How do they measure the diversity of inferences?
By how much do they improve the accuracy of inferences over state-of-the-art methods?
Which models do they use as baselines on the Atomic dataset?
How does the context-aware variational autoencoder learn event background information?
what standard speech transcription pipeline was used?
What kinds of neural networks did they use in this paper?
How did they use the domain tags?
Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?
How is speed measured?
What is the architecture of their model?
What are the nine types?
How do they represent input features of their model to train embeddings?
Which dimensionality do they use for their embeddings?
How does Frege's holistic and functional approach to meaning relates to general distributional hypothesis?
What does Frege's holistic and functional approach to meaning states?
How many paraphrases are generated per question?
What latent variables are modeled in the PCFG?
How strong was the correlation between exercise and diabetes?
How were topics of interest about DDEO identified?
What datasets are used for evaluation?
How do their train their embeddings?
How do they model travel behavior?
How do their interpret the coefficients?
By how much do they outperform previous state-of-the-art models?
How is the lexicon of trafficking flags expanded?
Do they experiment with the dataset?
Do they use a crowdsourcing platform for annotation?
What is an example of a difficult-to-classify case?
What potential solutions are suggested?
How strong is negative correlation between compound divergence and accuracy in performed experiment?
What are results of comparison between novel method to other approaches for creating compositional generalization benchmarks?
How authors justify that question answering dataset presented is realistic?
What three machine architectures are analyzed?
How big is new question answering dataset?
What are other approaches into creating compositional generalization benchmarks?
What problem do they apply transfer learning to?
What languages are considered?
What is the performance difference between proposed method and state-of-the-arts on these datasets?
What non autoregressive NMT models are used for comparison?
What are three neural machine translation (NMT) benchmark datasets used for evaluation?
What is result of their attention distribution analysis?
What is result of their Principal Component Analysis?
What are 3 novel fusion techniques that are proposed?
What are two models' architectures in proposed solution?
How do two models cooperate to select the most confident chains?
What benchmarks are created?
What empricial investigations do they reference?
What languages do they investigate for machine translation?
What recommendations do they offer?
What percentage fewer errors did professional translations make?
What was the weakness in Hassan et al's evaluation design?
By how much they improve over the previous state-of-the-art?
Is there any evidence that encoders with latent structures work well on other tasks?
What evidence do they present that the model attends to shallow context clues?
In what way is the input restructured?
What are their results on the entity recognition task?
What task-specific features are used?
What kind of corpus-based features are taken into account?
Which machine learning algorithms did the explore?
What language is the Twitter content in?
What is the architecture of the siamese neural network?
Which are the four Arabic dialects?
What factors contribute to interpretive biases according to this research?
Which interpretative biases are analyzed in this paper?
How many words are coded in the dictionary?
Is the model evaluated on the graphemes-to-phonemes task?
How does the QuaSP+Zero model work?
Which off-the-shelf tools do they use on QuaRel?
How do they obtain the logical forms of their questions in their dataset?
Do all questions in the dataset allow the answers to pick from 2 options?
What is shared in the joint model?
Are the intent labels imbalanced in the dataset?
What metrics are considered?
Did the authors evaluate their system output for coherence?
What metrics are used to evaluation revision detection?
How large is the Wikipedia revision dump dataset?
What are simulated datasets collected?
Which are the state-of-the-art models?
Where did this model place in the final evaluation of the shared task?
What in-domain data is used to continue pre-training?
What dialect is used in the Google BERT model and what is used in the task data?
What are the tasks used in the mulit-task learning setup?
What human evaluation metrics were used in the paper?
For the purposes of this paper, how is something determined to be domain specific knowledge?
Does the fact that GCNs can perform well on this tell us that the task is simpler than previously thought?
Are there conceptual benefits to using GCNs over more complex architectures like attention?
Do they evaluate only on English?
What are the 7 different datasets?
What are the three different sources of data?
What type of model are the ELMo representations used in?
Which morphosyntactic features are thought to indicate irony or sarcasm?
Is the model evaluated on other datasets?
Does the model incorporate coreference and entailment?
Which frozen acoustic model do they use?
By how much does using phonetic feedback improve state-of-the-art systems?
Do they compare to previous models?
How do they incorporate sentiment analysis?
What is the dataset used?
How do they extract topics?
What translationese effects are seen in the analysis?
What languages are seen in the news and TED datasets?
How are the (possibly incorrect) coreference chains in the MT outputs annotated?
Which three neural machine translation systems are analyzed?
Which coreference phenomena are analyzed?
What new interesting tasks can be solved based on the uncanny semantic structures of the embedding space?
What are the uncanny semantic structures of the embedding space?
What is the general framework for data exploration by semantic queries?
What data exploration is supported by the analysis of these semantic structures?
what are the existing models they compared with?
What conclusions do the authors draw from their detailed analyses?
Do the BERT-based embeddings improve results?
What were the traditional linguistic feature-based models?
What type of baseline are established for the two datasets?
What model achieves state of the art performance on this task?
Which multitask annotated corpus is used?
What are the tasks in the multitask learning setup?
how are rare words defined?
which public datasets were used?
What are the results of the experiment?
Do the authors mention any possible confounds in their study?
What is the relationship between the co-voting and retweeting patterns?
Does the analysis find that coalitions are formed in the same way for different policy areas?
What insights does the analysis give about the cohesion of political groups in the European parliament?
Do they authors account for differences in usage of Twitter amongst MPs into their model?
How do they show their model discovers underlying syntactic structure?
Which dataset do they experiment with?
How do they measure performance of language model tasks?
How are content clusters used to improve the prediction of incident severity?
What cluster identification method is used in this paper?
How can a neural model be used for a retrieval if the input is the entire Wikipedia?
Which algorithm is used in the UDS-DFKI system?
Does the use of out-of-domain data improve the performance of the method?
Do they impose any grammatical constraints over the generated output?
Why did they think this was a good idea?
What QA models were used?
Can this approach model n-ary relations?
Was this benchmark automatically created from an existing dataset?
How does morphological analysis differ from morphological inflection?
What are the architectures used for the three tasks?
Which language family does Chatino belong to?
What system is used as baseline?
How do their results compare against other competitors in the PAN 2017 shared task on Author Profiling?
On which task does do model do worst?
On which task does do model do best?
How is Super Character method modified to handle tabular data also?
How are the substitution rules built?
What baseline is used to compare the experimental results against?
How does counterfactual data augmentation aim to tackle bias?
In the targeted data collection approach, what type of data is targetted?
Which language models do they compare against?
Is their approach similar to making an averaged weighted sum of word vectors, where weights reflect word frequencies?
How do they determine which words are informative?
How does soft contextual data augmentation work?
How does muli-agent dual learning work?
Which language directions are machine translation systems of WMT evaluated on?
What improvement does the MOE model make over the SOTA on machine translation?
What improvement does the MOE model make over the SOTA on language modelling?
How is the correct number of experts to use decided?
What equations are used for the trainable gating network?
What is the difference in performance between the interpretable system (e.g. vectors and cosine distance) and LSTM with ELMo system?
How is PIEWi annotated?
What methods are tested in PIEWi?
Which specific error correction solutions have been proposed for specialized corpora in the past?
What was the criteria for human evaluation?
What automatic metrics are used to measure performance of the system?
What existing methods is SC-GPT compared to?
Which datasets were used in the experiment?
What are the differences in the use of emojis between gang member and the rest of the Twitter population?
What are the differences in the use of YouTube links between gang member and the rest of the Twitter population?
What are the differences in the use of images between gang member and the rest of the Twitter population?
What are the differences in language use between gang member and the rest of the Twitter population?
How is gang membership verified?
Do the authors provide evidence that 'most' street gang members use Twitter to intimidate others?
What is English mixed with in the TRAC dataset?
Which psycholinguistic and basic linguistic features are used?
How have the differences in communication styles between Twitter and Facebook increase the complexity of the problem?
What data/studies do the authors provide to support the assertion that the majority of aggressive conversations contain code-mixed languages?
What scoring function does the model use to score triples?
What datasets are used to evaluate the model?
How better are results for pmra algorithm  than Doc2Vec in human evaluation?
What Doc2Vec architectures other than PV-DBOW have been tried?
What four evaluation tasks are defined to determine what influences proximity?
What six parameters were optimized with grid search?
What are the differences with previous applications of neural networks for this task?
How much improvement is gained from the proposed approaches?
Is infinite-length sequence generation a result of training with maximum likelihood?
What metrics are used in challenge?
What model was winner of the Visual Dialog challenge 2018?
Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?
How big is dataset for this challenge?
What open relation extraction tasks did they experiment on?
How is Logician different from traditional seq2seq models?
What's the size of the previous largest OpenIE dataset?
How does propose model model that capture three-way interactions?
Do transferring hurt the performance is the corpora are not related?
Is accuracy the only metric they used to compare systems?
How do they transfer the model?
Will these findings be robust through different datasets and different question answering algorithms?
What is the underlying question answering algorithm?
What datasets have this method been evaluated on?
What DCGs are used?
What else is tried to be solved other than 12 tenses, model verbs and negative form?
How much is performance improved by disabling attention in certain heads?
In which certain heads was attention disabled in experiments?
What handcrafter features-of-interest are used?