DataHammer/scimrc huggingface dataset:
Question: How to lose weight fast?
Answer: Generally speaking the best way to lose weight is to create a calorie deficit. This means spending or burning more calories than the ones you consume during a day. It’s important to note that this is not the only factor and there are many other variables, such as your macro intake, working out, allergies, and preexisting health conditions. That’s why it’s always important to consult with a medical professional.
Question: Which of the following foods could a vegan eat and which would they have to avoid: egg, almond milk, cheese, gelatin, chicken noodle soup, lettuce, and bread.
Answer: Vegans could eat almond milk, lettuce, and bread. A vegan could not eat egg, cheese, gelatin, or chicken noodle soup.
Question: What are the health benefits of walking for exercise?
Evidence: Regular, brisk exercise of any kind can improve confidence, stamina, energy, weight control and life expectancy and reduces stress. It can also decrease the risk of coronary heart disease, strokes, diabetes, high blood pressure, bowel cancer and osteoporosis. Scientific studies have also shown that walking, besides its physical benefits, is also beneficial for the mind, improving memory skills, learning ability, concentration, mood, creativity, and abstract reasoning. Sustained walking sessions for a minimum period of thirty to sixty minutes a day, five days a week, with the correct walking posture, reduce health risks and have various overall health benefits, such as reducing the chances of cancer, type 2 diabetes, heart disease, anxiety disorder and depression. Life expectancy is also increased even for individuals suffering from obesity or high blood pressure. Walking also improves bone health, especially strengthening the hip bone, and lowering the harmful low-density lipoprotein (LDL) cholesterol, and raising the useful high-density lipoprotein (HDL) cholesterol. Studies have found that walking may also help prevent dementia and Alzheimer's.
Answer: Walking daily, as little as 30 minutes a day, provides several physical and mental benefits.  It can lower stress, is beneficial for the mind and reduces several health risks.
Question: Which of these are different types of renewable energy resources? Geothermal, Hydro, Machine, Solar, Star, Wind, biomass.
Answer: The following are different types of renewable energy resources: Geothermal, Hydro, Solar, Wind and Biomass.
Question: What's the target of the model?
Evidence: In this question answering task, a reading passage , a query and several answer choices are given. P denotes the passage, Q denotes query and C denotes one of the multiple choices. The target of the model is to choose a correct answer A from multiple choices based on informations of P and Q.
Fig. FIGREF1 is the pipeline overview of QACNN. First, we use embedding layer to transform P, Q, and C into word embedding. Then the compare layer generates passage-query similarity map INLINEFORM0 and passage-choice similarity map INLINEFORM1 . The following part is the main component of QACNN. It consists of two-staged CNN architecture. The first stage projects word-level feature into sentence-level, and the second stage projects sentence-level feature into passage-level. Moreover, we apply query-based attention mechanism to each stage on the basis of INLINEFORM2 feature at word level and sentence level respectively. After QACNN Layer, we obtain each choice answer feature. Finally, a prediction layer collects output information from every choice feature and returns the most possible answer.
The target of the model is to choose a correct answer A from multi ple choices based on informations of P and Q.
Answer: The target of the model is to choose a correct answer A from multi ple choices based on informations of P and Q.
Question: Whether the attention distributions of the NART model more ambiguous than those of the ART model?
Evidence: Neural machine translation has attracted much attention in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3. Given a sentence $x=(x_1, \dots ,x_{T_x})$ from the source language, the straight-forward way for translation is to generate the target words $y=(y_1, \dots , y_{T_y})$ one by one from left to right. This is also known as the AutoRegressive Translation (ART) models, in which the joint probability is decomposed into a chain of conditional probabilities:
While the ART models have achieved great success in terms of translation quality, the time consumption during inference is still far away from satisfactory. During training, the predictions at different positions can be estimated in parallel since the ground truth pair $(x,y)$ is exposed to the model. However, during inference, the model has to generate tokens sequentially as $y_{<t}$ must be inferred on the fly. Such autoregressive behavior becomes the bottleneck of the computational time BIBREF4.
In order to speed up the inference process, a line of works begin to develop non-autoregressive translation models. These models break the autoregressive dependency by decomposing the joint probability with
The lost of autoregressive dependency largely hurt the consistency of the output sentences, increase the difficulty in the learning process and thus lead to a low quality translation. Previous works mainly focus on adding different components into the NART model to improve the expressiveness of the network structure to overcome the loss of autoregressive dependency BIBREF5, BIBREF6, BIBREF7. However, the computational overhead of new components will hurt the inference speed, contradicting with the goal of the NART models: to parallelize and speed up neural machine translation models.
To tackle this, we proposed a novel hint-based method for NART model training. We first investigate the causes of the poor performance of the NART model. Comparing with the ART model, we find that: (1) the positions where the NART model outputs incoherent tokens will have very high hidden states similarity; (2) the attention distributions of the NART model are more ambiguous than those of ART model. Therefore, we design two kinds of hints from the hidden states and attention distributions of the ART model to help the training of the NART model. The experimental results show that our model achieves significant improvement over the NART baseline models and is even comparable to a strong ART baseline in BIBREF4.
Comparing with the ART model, we find that: (1) the positions where the NART model outputs incoherent tokens will have very high hidden states similarity; (2) the attention distributions of the NART model are more ambiguous than those of ART model.
Answer: Yes.
Question: What are some physical activities I could do on a sunny day?
Answer: On a sunny day, you can take advantage of the nice weather by doing outdoor activities. For solo activities, you can go on a run, a hike or a bike ride. For group activities, consider playing frisbee or playing tennis.
Question: Which phrases are French and which words are English: mon frère, c'est la vie, good morning, what's up, break the ice, bite the bullet, and je suis désolé.
Answer: The French phrases are: mon frère, c'est la vie, and je suis désolé. The English phrases are: good morning, what's up, break the ice, and bite the bullet.
Question: What individual has won the most Olympic gold medals in the history of the games?
Answer: Michael Phelps has won the most gold medals of all time with 23 golds.
Question: From the text below, tell me where Mount Balinhard got its name.
Evidence: Mount Balinhard is a summit in Alberta, Canada.

Mount Balinhard was named for a title bestowed on the Earl of Southesk.
Answer: Balinhard was a title bestowed on the Earl of Southesk, from which Mount Balinhard gets its name.
Question: Does Gen Z appreciate Radiohead's music?
Answer: Everyone appreciates Radiohead's music. It's timeless, the tune is catchy, the lyrics are deep, the vocal is excellent, the moodiness of the songs resonate with young people from every nation.
Question: Give me a bulleted list of the fastest way to get from to cross the English Channel in a nonconventional way?
Answer: First, you need to pick the shortest distance to travel across the English Channel.  The Straight of Dover is the narrowest point at only 20 miles wide. This will give you the best chance to complete your crossing quickly. 

Outside of conventional means of transport such as car, train, airplane and boats, you could try the following options: 

Hot air balloon 
Catapult 
Jetpack 
Hovercraft
Submarine
Swimming
Question: Of the following, identify which are TV shows that originally aired in black-and-white, brands of toothpaste, and places where you can participate in water sports: the beach, The Honeymooners, I Love Lucy, Colgate, Florida, a river, The Munsters, Crest, Aquafresh, a lake, a pool, Rembrandt, The Addams Family
Answer: TV shows that originally aired in black-and-white: The Honeymooners, I Love Lucy, The Munsters, The Addams Family
Toothpaste brands: Crest, Colgate, Aquafresh, Rembrandt
Places you can participate in water sports: the beach, Florida, a river, a lake, a pool
Question: What is the Genomie Aggregation Databaise (genomAD)?
Answer: The Genome Aggregation Database (genomAD) is a large-scale database of genetic variation data that aggregates and harmonizes genetic data from multiple population genetics studies. The database was created by the Broad Institute of MIT and Harvard in collaboration with other research institutions, and it contains genomic data from over 140,000 individuals from diverse populations around the world. The goal of genomAD is to provide a comprehensive resource for the study of genetic variation, allowing researchers to identify rare and common variants, assess their frequency in different populations, and analyze their potential effects on human health and disease. GenomAD includes both exome and whole-genome sequencing data, and it is widely used in genomics research and clinical genomics applications.
Question: In all word pairs, which score is higher? F1-OK or F1-BAD?
Evidence: In Table TABREF21 , we show the ablation study of the features used in our model on English-German, German-English, and English-Czech. For each language pair, we show the performance of CEQE without adding the corresponding components specified in the second column respectively. The last row shows the performance of the complete CEQE with all the components. As the baseline features released in the WMT2018 QE Shared Task for English-Latvian are incomplete, we train our CEQE model without using such features. We can glean several observations from this data:
Because the number of “OK” tags is much larger than the number of “BAD” tags, the model is easily biased towards predicting the “OK” tag for each target word. The F1-OK scores are higher than the F1-BAD scores across all the language pairs.
For German-English, English Czech, and English-German (SMT), adding the baseline features can significantly improve the F1-BAD scores.
For English-Czech, English-German (SMT), and English-German (NMT), removing POS tags makes the model more biased towards predicting “OK” tags, which leads to higher F1-OK scores and lower F1-BAD scores.
Adding the convolution layer helps to boost the performance of F1-Multi, especially on English-Czech and English-Germen (SMT) tasks. Comparing the F1-OK scores of the model with and without the convolution layer, we find that adding the convolution layer help to boost the F1-OK scores when translating from English to other languages, i.e., English-Czech, English-German (SMT and NMT). We conjecture that the convolution layer can capture the local information more effectively from the aligned source words in English.
Because the number of “OK” tags is much larger than the number of “BAD” tags, the model is easily biased towards predicting the “OK” tag for each target word. The F1-OK scores are higher than the F1-BAD scores across all the language pairs.
Answer: The F1-OK scores.
Question: What was the name of Dr Dolittle’s Parrot
Answer: Polynesia
Question: Which one of these is a country: Banana, Chicken, Pine tree, or Japan?
Answer: Japan
Question: What is the meaning of "Nnedi" in Nnedi Okarafor's name?
Evidence: Nnedimma Nkemdili Okorafor was born in Cincinnati, Ohio, in 1974 to Igbo Nigerian parents who travelled to America in 1969 to attend school but purportedly could not return to Nigeria due to the Nigerian Civil War. Okorafor is the third child in a family of four children and grew up in Chicago, Illinois, and would often travel to Nigeria to spend her holidays with her extended family. Her first name is Igbo for "mother is good".
Answer: "Nnedi" is the short form of her full name "Nnedimma" which translates to "mother is good", in Nigerian Igbo language.
Question: Please write a thank you message for a jeans day pass donated by a parent to a teacher
Answer: Thank you for the jeans day pass-it's like a vacation from my daily wardrobe and enhances my comfort and enjoyment when teaching your child. I'm excited to see the looks on my students' faces when I rock my new jeans!
Question: What is quoted for the Wiki(GOLD)?
Evidence: The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)
For the Wiki(GOLD) we quote the micro averaged F-1 scores for the entire top level entity category.
Answer: The micro averaged F-1 scores for the entire top level entity category.
Question: Give me 10 different ideas for how to spend a rainy Saturday afternoon
Answer: - Go to a movie
- Play a video game
- Read a book
- Shop at an indoor mall
- Host a lunch party
- Exercise at the gym
- Clean the house
- Meditate
- Call an old friend
- Catch up on work
Question: How can I treat a cough without medication?
Answer: You can help soothe a cough by getting some rest and drinking plenty of fluids, often hot drinks such as lemon and honey will help. Using a humidifier or having a steamy shower will also help. To help at night, raise your head in bed with an extra pillow.
Question: What is baseline used?
Evidence: We follow the BiLSTM architecture from BIBREF3. Only the optimizer was changed for all models to NADAM BIBREF22 as this helped with convergence problems for increasing cluster numbers. The Base is trained only on clean data while Base+Noise is trained on both the clean and the noisy data without noise handling. Global-CM uses a global confusion matrix for all noisy instances to model the noise as proposed by BIBREF3 and presented in Section SECREF3. The same architecture is used for Global-ID-CM, but the confusion matrix is initialized with the identity matrix (instead of Formula DISPLAY_FORM5) and only adapted during training.
The cluster-based models we propose in Section SECREF4 are Brown-CM and K-Means-CM. We experimented with numbers of clusters of 5, 10, 25 and 50. The models that select only the largest groups $G$ are marked as *-Freq and select either 30% or 50% of the clusters. The interpolation models have the postfix *-IP with $\lambda \in \lbrace 0.3, 0.5, 0.7\rbrace $ . The combination of both is named *-Freq-IP. As for all other hyperparameters, the choice was taken on the development set.
We implemented the Cleaning BIBREF15 and Dynamic-CM BIBREF14 models. Both were not developed for sequence labeling tasks and therefore needed to be adapted. For the Cleaning model, we followed the instructions by BIBREF3. The embedding and prediction components of the Dynamic-CM model were replaced according to our base model. The output of the dense layer was used as input to the dynamic matrix generation. We experimented with and without their proposed trace loss.
 The Base is trained only on clean data while Base+Noise is trained on both the clean and the noisy data without noise handling. Global-CM uses a global confusion matrix for all noisy instances to model the noise as proposed by BIBREF3 and presented in Section SECREF3. The same architecture is used for Global-ID-CM, but the confusion matrix is initialized with the identity matrix (instead of Formula DISPLAY_FORM5) and only adapted during training.

The cluster-based models we propose in Section SECREF4 are Brown-CM and K-Means-CM. We experimented with numbers of clusters of 5, 10, 25 and 50. The models that select only the largest groups $G$ are marked as *-Freq and select either 30% or 50% of the clusters. The interpolation models have the postfix *-IP with $\lambda \in \lbrace 0.3, 0.5, 0.7\rbrace $ . The combination of both is named *-Freq-IP. As for all other hyperparameters, the choice was taken on the development set.

We implemented the Cleaning BIBREF15 and Dynamic-CM BIBREF14 models. Both were not developed for sequence labeling tasks and therefore needed to be adapted. For the Cleaning model, we followed the instructions by BIBREF3. The embedding and prediction components of the Dynamic-CM model were replaced according to our base model. The output of the dense layer was used as input to the dynamic matrix generation. We experimented with and without their proposed trace loss.
Answer: The answers are shown as follows:
* Base 
* Base+Noise
* Cleaning 
* Dynamic-CM 
*  Global-CM
*  Global-ID-CM
* Brown-CM 
*  K-Means-CM
Question: Imagine you're seeing colors for the first time. Explain what you see and how you feel.
Answer: I honestly never thought I would experience colors in my lifetime. I'm overwhelmed with the amount of new colors I see. I now see more than just two different options when viewing the world. Although I'm aware, It's still unclear to me how to match the colors I see to their proper names. It will be very helpful to match what I'm seeing with their proper names. What a wonderful day; I'm wiping the tears from my eyes in pure joy!
Question: Why Rafael Nadal succeeds on clay?
Evidence: Rafael Nadal on clay is like a Grand Theft Auto game with the 'Infinite Ammo' cheat code enabled. Several have compared the Spaniard displaying his prowess on the red dirt to Picasso painting a live mural, Ennio Morricone producing a melody exactly congruent with the scenario, or Jorge Lorenzo posting similar times lap after lap in an almost mechanical yet fluid approach.
Answer: Rafael Nadal on clay is like a Grand Theft Auto game with the 'Infinite Ammo' cheat code enabled. Several have compared the Spaniard displaying his prowess on the red dirt to Picasso painting a live mural, Ennio Morricone producing a melody exactly congruent with the scenario, or Jorge Lorenzo posting similar times lap after lap in an almost mechanical yet fluid approach.

Rafael Nadal's ability on clay is mostly due to the surface itself. This may appear to be a quip, but the particular qualities of claycourts - which distinguish them from grass and hardcourts - are a fundamental reason why Rafael Nadal has dominated the surface the way he has.

What distinguishes clay? It's sluggish and bouncy. Fundamentally, clay reduces the speed of the ball once it bounces on the surface. Of course, there's more to it than just making the game slow and monotonous, but that's the distinguishing quality of a claycourt.

The surface is also less skiddish than grass or hard, allowing the ball to bounce high, which suits Rafael Nadal's high topspin-based game.

The slick nature of grass and hardcourt surfaces has been one of Rafael Nadal's main challenges throughout his career. Because there is significantly more skid on the ball on grass and hard than on clay, the ball tends to stay low. That in turn takes away vital time from the Spaniard to apply enough topspin.

For many of Rafael Nadal's career, his allegedly subpar serve in compared to his opponents was regarded as his most serious flaw. When his ground game isn't functioning, Nadal rarely has the luxury of a great first serve that can bail him out of trouble. To save break points, the 19-time Grand Slam champion frequently needs to drag out rallies.

Despite having a somewhat weak serve for the majority of his career, Rafael Nadal has able to take use of his southpaw serve on the ad side (to a right-handed opponent's normally weaker side). And nowhere has he done it better than on his favourite red dirt; just ask Roger Federer how much it sucks for the opponent.

The southpaw's serve forces the right-handed returner wide into his backhand corner, resulting in a weak return. This opens up practically the entire court for Rafael Nadal to unleash his most lethal weapon, the forehand down the line.

Rafael Nadal's obsession with patterns has been a feature of his game throughout his career, regardless of the surface.

In fact, because he is preoccupied on keeping to his rallying patterns, Nadal sometimes lets go of an opening and misses a chance to strike a winner into the open court. On social media, this has become known as Nadal's "Open Court atheism."
Question: How are the keywords associated with events such as protests selected?
Evidence: We approached the first and second challenges by using a Bayesian approach to learn which terms were associated with events, regardless of whether they are standard language, acronyms, or even a made-up word, so long as they match the events of interest. The third and fourth challenges are approached by using word-pairs, where we extract all the pairs of co-occurring words within each tweet. This allows us to recognize the context of the word ('Messi','strike' ) is different than ('labour','strike').
According to the distributional semantic hypothesis, event-related words are likely to be used on the day of an event more frequently than any normal day before or after the event. This will form a spike in the keyword count magnitude along the timeline as illustrated in Figure FIGREF6 . To find the words most associated with events, we search for the words that achieve the highest number of spikes matching the days of events. We use the Jaccard similarity metric as it values the spikes matching events and penalizes spikes with no event and penalizes events without spikes. Separate words can be noisy due to the misuse of the term by people, especially in big data environments. So, we rather used the word-pairs as textual features in order to capture the context of the word. For example, this can differentiate between the multiple usages of the word “strike” within the contexts of “lightning strike”, “football strike” and “labour strike”
We approached the first and second challenges by using a Bayesian approach to learn which terms were associated with events, regardless of whether they are standard language, acronyms, or even a made-up word, so long as they match the events of interest. The third and fourth challenges are approached by using word-pairs, where we extract all the pairs of co-occurring words within each tweet.
To find the words most associated with events, we search for the words that achieve the highest number of spikes matching the days of events. 
Answer: By using a Bayesian approach  and by using word-pairs, where they extract all the pairs of co-occurring words within each tweet.  They search for the words that achieve the highest number of spikes matching the days of events.