DataHammer/scimrc huggingface dataset:
Question: What are the two variants used in this work?
Evidence: The AUD model described in BIBREF0 , BIBREF1 is a non-parametric Bayesian Hidden Markov Model (HMM). This model is topologically equivalent to a phone-loop model with two major differences:
In this work, we have used two variants of this original model. The first one (called HMM model in the remainder of this paper), following the analysis led in BIBREF8 , approximates the Dirichlet Process prior by a mere symmetric Dirichlet prior. This approximation, while retaining the sparsity constraint, avoids the complication of dealing with the variational treatment of the stick breaking process frequent in Bayesian non-parametric models. The second variant, which we shall denote Structured Variational AutoEncoder (SVAE) AUD, is based upon the work of BIBREF4 and embeds the HMM model into the Variational AutoEncoder framework BIBREF9 . A very similar version of the SVAE for AUD was developed independently and presented in BIBREF5 . The main noteworthy difference between BIBREF5 and our model is that we consider a fully Bayesian version of the HMM embedded in the VAE; and the posterior distribution and the VAE parameters are trained jointly using the Stochastic Variational Bayes BIBREF4 , BIBREF10 . For both variants, the prior over the HMM parameters were set to the conjugate of the likelihood density: Normal-Gamma prior for the mean and variance of the Gaussian components, symmetric Dirichlet prior over the HMM's state mixture's weights and symmetric Dirichlet prior over the acoustic units' weights. For the case of the uninformative prior, the prior was set to be vague prior with one pseudo-observation BIBREF11 .
In this work, we have used two variants of this original model. The first one (called HMM model in the remainder of this paper), following the analysis led in [9], approximates the Dirichlet Process prior by a mere symmetric Dirichlet prior. This approximation, while retaining the sparsity constraint, avoids the complication of dealing with the variational treatment of the stick breaking process frequent in Bayesian non-parametric models. The second variant, which we shall denote Structured Variational AutoEncoder (SVAE) AUD, is based upon the work of [5] and embeds the HMM model into the Variational AutoEncoder framework [10].
Answer: The first one is called HMM model. The second variant is Structured Variational AutoEncoder (SVAE) AUD.
Question: Which of the following countries are in Asia: India, South Korea, Japan, United States of America, Morocco, New Zealand, Indonesia, Singapore, Mexico?
Answer: The Asian countries are - India, South Korea, Japan, Indonesia, Singapore.
Question: When talking about paint colours, which one of these colours is considered "cold", "warm" or "both". Both meaning the way the colour is perceived depends on the colours surrounding it.
Yellow, Yellow-Orange, Orange, Red-Orange, Red, Red-Violet, Violet, Blue-Violet, Blue, Blue-Green, Green, Yellow-Green
Answer: Warm: Yellow, Yellow-Orange, Orange, Red-Orange, Red
Cold: Violet, Blue-Violet, Blue, Blue-Green, Green
Both: Red-Violet, Yellow-Green
Question: In the series A Song of Ice and Fire, who is the founder of House Slynt?
Answer: Janos Slynt
Question: Why do players smear black under their eyes?
Answer: Black eye smear reduces the blinding sunlight when looking up towards the sun, when catching/hitting the ball
Question: What is the best age to drive a car?
Answer: In U.S legal age to drive is 16 years old. You will need to pass a written test and driving test in DMV to get your driver's license. However, I will not recommend driving at 16 because the insurance cost for young teen drivers is very high. It can be over $3000 per year and some states can even reach $5000. Also, most 16 years old is not prepared to handle the mental and physical demands of driving, their also easy to blend in their emotion when they are driving. So, my suggestion is to wait until they are 18 years old to drive a car.
Question: what is Kubernetes?
Answer: Kubernetes is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by the Cloud Native Computing Foundation.
Question: What years was the BMW 1M made?
Answer: The BMW 1M was made in 2011 only. It was only manufactured with a 6-speed manual transmission, none were made with an automatic transmission. The 1M was originally manufactured in 3 colors: Alpine White, Sapphire Black, and Valencia Orange.
Question: What are the basic rules of baseball?
Answer: Baseball is a complex sport that can be confusing to newcomers. Baseball is a game played by two teams of 9 players on a large field with 4 bases. The team on defense has 9 players out in the field at once, which includes the pitcher and the catcher. The hitting team only has one player "at-bat." The pitcher throws the ball to the catcher, and the hitter attempts to hit the ball into the field and run to a base if they hit the ball into fair territory. If the player misses 3 times, they are out. If the pitcher throws a bad pitch 4 times, the hitter gets to go to first base automatically. The player can also get out if a fielder catches the ball in the air or tags first base before the runner gets there. The game itself is 9 innings of 3 outs for each team. Whoever has the most runs (when a player runs to all 4 bases) at the end of the game wins!
Question: Identify which animal species is alive or extinct: Spinosaurus, Common Bottlenose Dolphin
Answer: Common Bottlenose Dolphin is alive, Spinosaurus is extinct.
Question: What dataset is the experiment conducted on?
Evidence: Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset. This dataset contains the sentences collected from daily newspaper of the year 2015-2016. This dataset has three major classes Person (PER), Location (LOC) and Organization (ORG). Pre-processing was performed on the text before creation of the dataset, for example all punctuations and numbers besides ',', '-', '|' and '.' were removed. Currently, the dataset is in standard CoNLL-2003 IO formatBIBREF25.
Since, this dataset is not lemmatized originally, we lemmatized only the post-positions like Ek, kO, l, mA, m, my, jF, sg, aEG which are just the few examples among 299 post positions in Nepali language. We obtained these post-positions from sanjaalcorps and added few more to match our dataset. We will be releasing this list in our github repository. We found out that lemmatizing the post-positions boosted the F1 score by almost 10%.
In order to label our dataset with POS-tags, we first created POS annotated dataset of 6946 sentences and 16225 unique words extracted from POS-tagged Nepali National Corpus and trained a BiLSTM model with 95.14% accuracy which was used to create POS-tags for our dataset.
The dataset released in our github repository contains each word in newline with space separated POS-tags and Entity-tags. The sentences are separated by empty newline. A sample sentence from the dataset is presented in table FIGREF13.
Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset. This dataset contains the sentences collected from daily newspaper of the year 2015-2016. This dataset has three major classes Person (PER), Location (LOC) and Organization (ORG).
Answer: They had to create their own dataset which contains the sentences collected from daily newspaper of the year 2015-2016.
Question: Why do people like sweet treats so much?
Answer: Sugar has been added heavily to the American diet. It's in foods you wouldn't even suspect, such as spaghetti sauce or juices, even though they are already sweet. Sugar is also highly addictive. It gives your brain a high, just like other addictive substances. Additionally, it does taste great, so the taste alone keeps someone coming back for more.
Question: To demonstrate the effect of using mixed training data, what are the two models trained by the author to do respectively?
Evidence: To show the effects of using the hybrid training data, we trained two models of the same architecture and hyperparameters. One model was trained on the hybrid data and will be referred to as hybrid NER model. The other model was trained on clinical notes only and will be referred to as i2b2 NER model. We evaluated the performance of the NER models by micro-F1 score on the test set of both the synthesized queries and the i2b2 data.
We used the BiLSTM-CRF implementation provided by the flair package BIBREF16. We set the hidden size value to be 256 in the LSTM structure and left everything else at default values for the SequenceTagger model on flair. For word embeddings, we used the ELMo embeddings fine-tuned on PubMed articles and flair embeddings BIBREF13 trained on $5\%$ of PubMed abstracts , respectively. We trained models for 10 epochs and experimented with different learning rate, mini batch size, and dropouts. We ran hyperparameter optimization tests to find the best combination. $S_c$ is set to be 0.6 in our experiment.
To show the effects of using the hybrid training data, we trained two models of the same architecture and hyperparameters. One model was trained on the hybrid data and will be referred to as hybrid NER model. The other model was trained on clinical notes only and will be referred to as i2b2 NER model. We evaluated the performance of the NER models by micro-F1 score on the test set of both the synthesized queries and the i2b2 data.
Answer: One model was trained on the hybrid data and will be referred to as hybrid NER model. The other model was trained on clinical notes only and will be referred to as i2b2 NER model. They evaluated the performance of the NER models by micro-F1 score on the test set of both the synthesized queries and the i2b2 data.
Question: From the passage provided extract how many European and worldwide tittles Barcelona has won.
Evidence: Domestically, Barcelona has won a record 76 trophies: 26 La Liga, 31 Copa del Rey, fourteen Supercopa de España, three Copa Eva Duarte, and two Copa de la Liga titles, as well as being the record holder for the latter four competitions. In international club football, the club has won 22 European and worldwide titles: five UEFA Champions League titles, a record four UEFA Cup Winners' Cups, a joint record five UEFA Super Cups, a record three Inter-Cities Fairs Cups, a joint record two Latin Cups and three FIFA Club World Cups.
Answer: Barcelona has won 22 European and worldwide titles
Question: Whether using only monolingual data that the proposed models can be efficiently trained?
Evidence: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.
In this paper, we propose simple yet effective methods to improve word-by-word translation of crosslingual embeddings, using only monolingual corpora but without any back-translation.
Answer: Yes.
Question: For POS tagging, what idea has been proposed by Naseem et al.?
Evidence: As discussed in section SECREF24 , our work is closely related to the crosslingual unsupervised SRL work of titovcrosslingual. The idea of using superlingual latent variables to capture cross-lingual information was proposed for POS tagging by naseem2009multilingual, which we use here for SRL. In a semi-supervised setting, pado2009cross used a graph based approach to transfer semantic role annotations from English to German. furstenau2009graph used a graph alignment method to measure the semantic and syntactic similarity between dependency tree arguments of known and unknown verbs.
For monolingual unsupervised SRL, swier2004unsupervised presented the first work on a domain-general corpus, the British National Corpus, using 54 verbs taken from VerbNet. garg2012unsupervised proposed a Bayesian model for this problem that we use here. titov2012bayesian also proposed a closely related Bayesian model. grenager2006unsupervised proposed a generative model but their parameter space consisted of all possible linkings of syntactic constituents and semantic roles, which made unsupervised learning difficult and a separate language-specific rule based method had to be used to constrain this space. Other proposed models include an iterative split-merge algorithm BIBREF18 and a graph-partitioning based approach BIBREF1 . marquez2008semantic provide a good overview of the supervised SRL systems.
The idea of using superlingual latent variables to capture crosslingual information was proposed for POS tagging by Naseem et al. (2009), which we use here for SRL.
Answer: Using superlingual latent variables to capture crosslingual information.
Question: How many knowledge bases do they use for ontology prediction experiments?
Evidence: In this work, we use the ConceptNet BIBREF3 , WordNet BIBREF2 , and Microsoft Concept Graph (MCG) BIBREF11 , BIBREF12 knowledge bases for our ontology prediction experiments.
WordNet is a knowledge base (KB) of single words and relations between them such as hypernymy and meronymy. For our task, we use the hypernym relations only. ConceptNet is a KB of triples consisting of a left term $t_1$ , a relation $R$ , and a right term $t_2$ . The relations come from a fixed set of size 34. But unlike WordNet, terms in ConceptNet can be phrases. We focus on the Is-A relation in this work. MCG also consists of hierarchical relations between multi-word phrases, ranging from extremely general to specific. Examples from each dataset are shown in Table 1 .
For experiments involving unstructured text, we use the WaCkypedia corpus BIBREF13 .
In this work, we use the ConceptNet, WordNet , and Microsoft Concept Graph (MCG) knowledge bases for our ontology prediction experiments.
Answer: Four.
Question: Think up a bunch of different ways to exercise
Answer: Running, walking, jogging, weight lifting, swimming, yoga, biking, boxing, sit ups, pushups, planks, jumping jacks
Question: Does EV help address the energy crisis?
Evidence: Electric cars (or electric vehicles, EVs) have a smaller environmental footprint than conventional internal combustion engine vehicles (ICEVs). While aspects of their production can induce similar, less or alternative environmental impacts, they produce little or no tailpipe emissions, and reduce dependence on petroleum, greenhouse gas emissions, and health effects from air pollution. Electric motors are significantly more efficient than internal combustion engines and thus, even accounting for typical power plant efficiencies and distribution losses, less energy is required to operate an EV. Manufacturing batteries for electric cars requires additional resources and energy, so they may have a larger environmental footprint from the production phase. EVs also generate different impacts in their operation and maintenance. EVs are typically heavier and could produce more tire and road dust air pollution, but their regenerative braking could reduce such particulate pollution from brakes. EVs are mechanically simpler, which reduces the use and disposal of engine oil.
Answer: Although all cars have effects on other people, battery electric cars have major environmental benefits over conventional internal combustion engine vehicles (ICEVs), such as:

Elimination of harmful tailpipe pollutants such as various oxides of nitrogen, which kill thousands of people every year. EVs use 38 megajoules per 100 km in comparison to 142 megajoules per 100 km for ICE cars. Less CO2 emissions globally than fossil-fuelled cars, thus limiting climate change.  Plug-in hybrids capture most of these benefits when they are operating in all-electric mode.

Electric cars have some disadvantages, such as:

Possible increased particulate matter emissions from tires compared to fossil-fueled cars. This is sometimes caused by the fact that most electric cars have a heavy battery, which means the car's tires are subjected to more wear. This is drastically reduced when EV-rated weight-specific tires are used on the EV which are built specifically for the extra weight.[citation needed] Devices to capture tyre particulates are being developed.
The brake pads, however, can be used less frequently than in non-electric cars, if regenerative braking is available and may thus sometimes produce less particulate pollution than brakes in non-electric cars. Also, some electric cars may have a combination of drum brakes and disc brakes, and drum brakes are known to cause less particulate emissions than disc brakes.

Reliance on rare-earth elements such as neodymium, lanthanum, terbium, and dysprosium, and other critical metals such as lithium and cobalt,though the quantity of rare metals used differs per car. Despite the name rare earth metals are plentiful.They make up a tiny share of the minerals used to make a car.
Question: Classify each method of communication as "verbal" or "written": e-mail, phone call, slack chat, zoom meeting, text, post mail
Answer: E-mail: Written
Phone Call: Verbal
Slack Chat: Written
Zoom Meeting: Verbal
Text: Written
Post Mail: Written
Question: How do they produce insights from PA text in the paper?
Evidence: Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers, such as self-appraisal, supervisor assessment and peer feedback. Analysis of the structured data and text produced in PA is crucial for measuring the quality of appraisals and tracking actual improvements. In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries. All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.
In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries.
Answer: First, they perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next they use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, they propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries.
Question: Which months of the year are considered summer, spring, winter and autumn.
Answer: Seasons depend on where you live. In the US, summer is usually June through August. Spring is March through May. Fall is September through November. Winter is December through February.
Question: Does a neural scoring function take both the question and the logical form as inputs?
Evidence: Let $u,v \in \mathbb {R}^{d}$ be the sentence embeddings of question $q$ and of paraphrase $t$ . We experiment with the following similarity scores: i) DOTPRODUCT : $u^{T}v$ ; ii) BILIN : $u^{T}Sv$ , with $S\in \mathbb {R}^{d\times d}$ being a trainable matrix; iii) FC: u and v concatenated, followed by two sequential fully connected layers with ELU non-linearities; iv) FC-BILIN: weighted average of BILIN and FC. These models define parametrized similarity scoring functions $: Q\times T\rightarrow \mathbb {R}$ , where $Q$ is the set of natural language questions and $T$ is the set of paraphrases of logical forms.
These models define parametrized similarity scoring functions $: Q\times T\rightarrow \mathbb {R}$ , where $Q$ is the set of natural language questions and $T$ is the set of paraphrases of logical forms.
Answer: Yes.
Question: What is the author's plan for his future work?
Evidence: In this paper, we have shown that using the designed RNN based network we could increase the performance of classification dramatically. We showed that keeping the sequential nature of the data can be hugely beneficial when working with textual data especially faced with the hard task of detecting more complex phenomena like emotions. We accomplished that by using a recurrent network in the process of generating our hidden representation. We have also used a max-pooling layer to capture the most relevant features and an average pooling layer to capture the text as a whole proving that we can achieve better performance by focusing on creating a more informative hidden representation. In future we can focus on improving these representations for example by using attention networks BIBREF37 , BIBREF38 to capture a more contextual representation or using language model based methods like BERT BIBREF39 that has been shown very successful in various NLP tasks.
In future we can focus on improving these representations for example by using attention networks to capture a more contextual representation or using language model based methods like BERT that has been shown very successful in various NLP tasks.
Answer: They can focus on improving these representations for example by using attention networks to capture a more contextual representation or using language model based methods like BERT that has been shown very successful in various NLP tasks.
Question: Was the hermit hummingbird hawkmoth described in the 20th or 21st century?
Evidence: Macroglossum insipida, the hermit hummingbird hawkmoth, is a moth of the family Sphingidae. It was described by Arthur Gardiner Butler in 1875.
Answer: Neither.  It was first described by Arthur Gardiner Butler in 1875, which is in the 19th century.
Question: What are the healthiest cooking methods for preparing meals?
Answer: Steaming - cooking food by exposing retains nutrients and flavor.
Boiling - A healthy cooking method but boiling for too long causes the loss of some nutrients.
Grilling - cooking food over an open flame allows surplus fat to drip from the food.
Baking and roasting - involve cooking in an oven, which can retain nutrients and flavors.
Stir-frying - A healthy method of cooking food in high heat.
Poaching - involves cooking food in liquid at a low temperature.
The healthiest cooking methods involve minimal added fats, salt, and sugar.