DinoStackAI/qasper-rag huggingface dataset:
What are the baselines model?
What languages are explored in the work?
What is the state-of-the-art neural coreference resolution model?
What features do they extract?
What they use as a metric of finding hot spots in meeting?
Is this approach compared to some baseline?
How big is ICSI meeting corpus?
What annotations are available in ICSI meeting corpus?
Is such bias caused by bad annotation?
How do they determine similar environments for fragments in their data augmentation scheme?
Which languages do they test on?
What limitations are mentioned?
What examples of applications are mentioned?
Did they crowdsource the annotations?
Why they conclude that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting?
What semantic features help in detecting whether a piece of text is genuine or generated? of
Which language models generate text that can be easier to classify as genuine or generated?
Is the assumption that natural language is stationary and ergodic valid?
Do they compare executionttime of their model against other models?
What architectural factors were investigated?
What is the introduced meta-embedding method introduced in this paper?
How long are dialogue recordings used for evaluation?
What do the models that they compare predict?
What SMT models did they look at?
Which NMT models did they experiment with?
How big PIE datasets are obtained from dictionaries?
What compleentary PIE extraction methods are used to increase reliability further?
Are PIEs extracted automatically subjected to human evaluation?
What dictionaries are used for automatic extraction of PIEs?
Are experiments performed with any other pair of languages, how did proposed method perform compared to other models?
Is pivot language used in experiments English or some other language?
What are multilingual models that were outperformed in performed experiment?
What are the common captioning metrics?
Which English domains do they evaluate on?
What is the road exam metric?
How is the input triple translated to a slot-filling task?
Is model compared against state of the art models on these datasets?
How is octave convolution concept extended to multiple resolutions and octaves?
On which dataset is model trained?
How is module that analyzes behavioral state trained?
Can the model add new relations to the knowledge graph, or just new entities?
Why is a Gaussian process an especially appropriate method for this classification problem?
Do the authors do manual evaluation?
Do twitter users tend to tweet about the DOS attack when it occurs? How much data supports this assumption?
What is the training and test data used?
Was performance of the weakly-supervised model compared to the performance of a supervised model?
Do the tweets come from a specific region?
How did they determine the distinct classes?
Do they jointly tackle multiple tagging problems?
How do they confirm their model working well on out-of-vocabulary problems?
What approach does this work propose for the new task?
What is the new task proposed in this work?
Which news organisations are the headlines sourced from?
What meta-information is being transferred?
What datasets are used to evaluate the approach?
Does their solution involve connecting images and text?
Which model do they use to generate key messages?
What experiments they perform to demonstrate that their approach leads more accurate region based representations?
How they indentify conceptual neighbours?
What experiment result led to conclussion that reducing the number of layers of the decoder does not matter much?
How much is performance hurt when using too small amount of layers in encoder?
What was previous state of the art model for automatic post editing?
What neural machine translation models can learn in terms of transfer learning?
Is annotation done manually?
How large is the proposed dataset?
How is the dataset created?
What is binary variational dropout?
Which strategies show the most promise in deterring these attacks?
What are baseline models on WSJ eval92 and LibriSpeech test-clean?
Do they use the same architecture as LSTM-s and GRUs with just replacing with the LAU unit?
So this paper turns unstructured text inputs to parameters that GNNs can read?
What empirical data are the Blending Game predictions compared to?
How does the semi-automatic construction process work?
Does the paper report translation accuracy for an automatic translation model for Tunisian to Arabish words?
Did participants behave unexpectedly?
Was this experiment done in a lab?
How much does HAS-QA improve over baselines?
What does "explicitly leverages their probabilistic correlation to guide the training process of both models" mean?
How does this compare to contextual embedding methods?
Does the new system utilize pre-extracted bounding boxes and/or features?
To which previous papers does this work compare its results?
What were the model's results on flood detection?
What exactly is new about this stochastic gradient descent algorithm?
What codemixed language pairs are evaluated?
How do they compress the model?
What is the multilingual baseline?
Which features do they use?
By how much do they outperform state-of-the-art solutions on SWDA and MRDA?
What type and size of word embeddings were used?
What data was used to build the word embeddings?
How are templates discovered from training data?
What is WNGT 2019 shared task?
Do they use pretrained word representations in their neural network models?
How do they combine the two proposed neural network models?
Which dataset do they evaluate grammatical error correction on?
what was their baseline comparison?
Was any variation in results observed based on language typology?
Which datasets are used in this work?
Does the training dataset provide logical form supervision?
What is the difference between the full test set and the hard test set?
How is the discriminative training formulation different from the standard ones?
How are the two datasets artificially overlapped?
What type of lexical, syntactic, semantic and polarity features are used?
How does nextsum work?
Can the approach be generalized to other technical domains as well?
How many tweets were manually labelled?
What dataset they use for evaluation?
What is the source of the tables?
What state-of-the-art general-purpose pretrained models are made available under the unified API?
What models are included in the toolkit?
Is there any human evaluation involved in evaluating this famework?
How big is dataset used for fine-tuning BERT?
What is novel in author's approach?
What dataset is used for train/test of this method?
How much is the gap between using the proposed objective and using only cross-entropy objective?
How many domains of ontologies do they gather data from?
How is the semi-structured knowledge base created?
what is the practical application for this paper?
Do they use a neural model for their task?
What's the method used here?
By how much does their method outperform state-of-the-art OOD detection?
What are dilated convolutions?
what was the evaluation metrics studied in this work?
what datasets did the authors use?
What are three possible phases for language formation?
Do the experiments explore how various architectures and layers contribute towards certain decisions?
How much performance improvements they achieve on SQuAD?
Do the authors perform experiments using their proposed method?
What NLP tasks do the authors evaluate feed-forward networks on?
What are three challenging tasks authors evaluated their sequentially aligned representations?
What is the difference in findings of Buck et al? It looks like the same conclusion was mentioned in Buck et al..
What is the unsupervised task in the final layer?
What is the network architecture?
What does recurrent deep stacking network do?
Does the latent dialogue state heklp their model?
What is the reward model for the reinforcement learning appraoch?
Does this paper propose a new task that others can try to improve performance on?
What knowledge base do they use?
What task do they evaluate on?
Do some pretraining objectives perform better than others for sentence level understanding tasks?
Did the authors try stacking multiple convolutional layers?
How many feature maps are generated for a given triple?