DataHammer/scimrc huggingface dataset:
Question: What was the baseline?
Evidence: Early approaches to Opinion Target Extraction (OTE) were unsupervised, although later on the vast majority of works have been based on supervised and deep learning models. To the best of our knowledge, the first work on OTE was published by BIBREF8 . They created a new task which consisted of generating overviews of the main product features from a collection of customer reviews on consumer electronics. They addressed such task using an unsupervised algorithm based on association mining. Other early unsupervised approaches include BIBREF9 which used a dependency parser to obtain more opinion targets, and BIBREF10 which aimed at extracting opinion targets in newswire via Semantic Role Labelling. From a supervised perspective, BIBREF11 presented an approach which learned the opinion target candidates and a combination of dependency and part-of-speech (POS) paths connecting such pairs. Their results improved the baseline provided by BIBREF8 . Another influential work was BIBREF12 , an unsupervised algorithm called Double Propagation which roughly consists of incrementally augmenting a set of seeds via dependency parsing.
In spite of this, Table TABREF20 shows that our system outperforms the best previous approaches across the five languages. In some cases, such as Turkish and Russian, the best previous scores were the baselines provided by the ABSA organizers, but for Dutch, French and Spanish our system is significantly better than current state-of-the-art. In particular, and despite using the same system for every language, we improve over GTI's submission, which implemented a CRF system with linguistic features specific to Spanish BIBREF28 .
Their results improved the baseline provided by BIBREF8 . Another influential work was BIBREF12 , an unsupervised algorithm called Double Propagation which roughly consists of incrementally augmenting a set of seeds via dependency parsing.
n spite of this, Table TABREF20 shows that our system outperforms the best previous approaches across the five languages. In some cases, such as Turkish and Russian, the best previous scores were the baselines provided by the ABSA organizers, but for Dutch, French and Spanish our system is significantly better than current state-of-the-art. In particular, and despite using the same system for every language, we improve over GTI's submission, which implemented a CRF system with linguistic features specific to Spanish BIBREF28 .
Answer: The answers are shown as follows:
* the baseline provided by BIBREF8
* the baselines provided by the ABSA organizers
Question: Identify which instrument is string or percussion: Kecer, Saz
Answer: Saz is string, Kecer is percussion.
Question: List me down some key aspects about windmills from given text.
Evidence: Wind-powered machines may have been known earlier, but there is no clear evidence of windmills
before the 9th century. Hero of Alexandria (Heron) in first-century Roman Egypt described what appears
to be a wind-driven wheel to power a machine. His description of a wind-powered organ is not a
practical windmill but was either an early wind-powered toy or a design concept for a wind-powered
machine that may or may not have been a working device, as there is ambiguity in the text and issues
with the design. Another early example of a wind-driven wheel was the prayer wheel, which is believed
to have been first used in Tibet and China, though there is uncertainty over the date of its first
appearance, which could have been either c. 400, the 7th century, or after the 9th century.
Answer: 1. Before 9th century there is no evidence of Windmills.
2. In first century roman Egypt , Heron described what is wind driven wheel to power machine.
3. As per the description given by him the wind-powered organ was either an early wind-powered
toy or a design concept for a wind-powered machine that may or may not have been a working
instrument.
4. One more early example(either the 7th century, or after the 9th century) of a wind-driven wheel
was the prayer wheel, which was first used in Tibet and China.
Question: Tell me whether the following countries are located in Europe, North America, South America, Africa, or Asia: France, Mexico, Peru, India, Nepal, Rwanda, Egypt, Portugal, Kenya, Argentina, Canada
Answer: France: Europe
Mexico: North America
Peru: South America
India: Asia
Nepal: Asia
Rwanda: Africa
Egypt: Africa
Portugal: Europe
Kenya: Africa
Argentina: South America
Canada: North America
Question: What will the team focus on in their future work?
Evidence: In this paper, we incorporated subword information (simple n-grams and unsupervised morphemes) into the LexVec word embedding model and evaluated its impact on the resulting IV and OOV word vectors. Like fastText, subword LexVec learns better representations for rare words than its word-level counterpart. All models generated coherent representations for OOV words, with simple n-grams demonstrating more robustness than unsupervised morphemes. In future work, we will verify whether using OOV representations in downstream tasks improves performance. We will also explore the trade-off between semantics and syntax when subword information is used.
In future work, we will verify whether using OOV representations in downstream tasks improves performance. We will also explore the trade-off between semantics and syntax when subword information is used.
Answer: They will verify whether using OOV representations in downstream tasks improves performance. They will also explore the trade-off between semantics and syntax when subword information is used.
Question: What was previous state-of-the-art on four Chinese reviews datasets?
Evidence: GANN is novel neural network model for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the Gate Truncation RNN (GTR) to learn informative aspect-dependent sentiment clue representations. GANN obtained the state-of-the-art APC performance on the Chinese review datasets.
GANN is novel neural network model for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the Gate Truncation RNN (GTR) to learn informative aspect-dependent sentiment clue representations. GANN obtained the state-of-the-art APC performance on the Chinese review datasets.
Answer: The answers are shown as follows:
* GANN obtained the state-of-the-art APC performance on the Chinese review datasets
Question: why would someone run the Texas Independence Relay?
Answer: The Texas Independence Relay (TIR) is an annual runners' festival in Texas with a lot of fun! It is a 200 mile relay starting from Gonzales town center, the course passes Shiner, Flatonia, Schulenburg, Weimar, and Columbus, and ends in downtown Houston. The race commemorates the Runaway Scrape of Texas independence from Mexico, and Gonzales is the pioneer place of Texas Revolution. The race has 36 legs and requires at most 12 runners in a team. It usually happens end of Mar or the beginning of Apr, which is the blooming time in Texas for wildflowers like bluebonnets. Overcoming the tough course with a team of cheerful and energetic run mates and enjoying the scenery in the meantime can be a memorable experience. In fact, a lot of the TIR teams are veteran teams who participate every year. It’s like an addiction! The supportive, caring vibes from all the runners, and festival-like celebrations along the course are the major attractors to runners.
Question: Classify each score as "Under Par" or "Over Par" in golf: Eagle, Bogey, Double Eagle, Hole in One, Triple Bogey, Birdie
Answer: Eagle: Under Par
Bogey: Over Par
Double Eagle: Under Par
Hole in One: Under Par
Triple Bogey: Over Par
Birdie: Under Par
Question: Choose the word which is different from the rest:  Trigger, Muzzle, Pallete, Barrel, Bullet
Answer: All except Pallete are parts of a gun
Question: Why do people need sunshine?
Answer: Ultraviolet waves from sunlight are absorbed through the skin as part of the process of producing Vitamin D. This is a critical vitamin for the immune system's ability to ward off disease, as well as for a person's hormonal balance. So sunshine can help make people both healthier and happier. On the other hand, too much sunshine can cause sunburn, which can make a person both unhealthy and unhappy.
Question: Did they use any crowdsourcing platform?
Evidence: We deployed the task on Amazon Mechanical Turk (AMT). To see how reasoning varies across workers, we hire 3 crowdworkers per one instance. We hire reliable crowdworkers with $\ge 5,000$ HITs experiences and an approval rate of $\ge $ 99.0%, and pay ¢20 as a reward per instance.
We deployed the task on Amazon Mechanical Turk (AMT). To see how reasoning varies across workers, we hire 3 crowdworkers per one instance. We hire reliable crowdworkers with $\ge 5,000$ HITs experiences and an approval rate of $\ge $ 99.0%, and pay ¢20 as a reward per instance.
Answer: Yes.
Question: What metric is used to evaluate the attacking performance of adversarial examples?
Evidence: As sentiment classification is our target task, we use the standard classification accuracy (ACC) to evaluate the attacking performance of adversarial examples (criterion (a)).
To assess the similarity between the original and (transformed) adversarial examples (criteria (b)), we compute BLEU scores BIBREF21.
To measure fluency, we first explore a supervised BERT model fine-tuned to predict linguistic acceptability BIBREF17. However, in preliminary experiments we found that BERT performs very poorly at predicting the acceptability of adversarial examples (e.g. it predicts word-salad-like sentences generated by FGVM as very acceptable), revealing the brittleness of these supervised models. We next explore an unsupervised approach BIBREF22, BIBREF23, using normalised sentence probabilities estimated by pre-trained language models for measuring acceptability. In the original papers, the authors tested simple recurrent language models; here we use modern pre-trained language models such as GPT-2 BIBREF24 and XLNet BIBREF18. Our final acceptability metric (ACPT) is based on normalised XLNet sentence probabilities: ${\log P(s)} / ({((5+|s|)/(5+1))^\alpha })$, where $s$ is the sentence, and $\alpha $ is a hyper-parameter (set to 0.8) to dampen the impact of large values BIBREF25.
We only computed BLEU and ACPT scores for adversarial examples that have successfully fooled the classifier. Our rationale is that unsuccessful examples can artificially boost these scores by not making any modifications, and so the better approach is to only consider successful examples.
As sentiment classification is our target task, we use the standard classification accuracy (ACC, the lower the better) to evaluate the attacking performance of adversarial examples (criterion (a)).
Answer: The standard classification accuracy.
Question: What strength does the method have in the application to other Neural Text Simplification (NTS) systems?
Evidence: In this paper, we propose one simple method to use simplified corpora during training of NMT systems, with no changes to the network architecture. In the experiments on two datasets, we achieve substantial gains in all tasks, and new SOTA results, via back-translation of simplified sentences into the ordinary sentences, and treating this synthetic data as additional training data. Because we do not change the neural network architecture to integrate simplified corpora, our method can be easily applied to other Neural Text Simplification (NTS) systems. We expect that the effectiveness of our method not only varies with the quality of the NTS system used for back-translation, but also depends on the amount of available parallel and simplified corpora. In the paper, we have only utilized data from Wikipedia for simplified sentences. In the future, many other text sources are available and the impact of not only size, but also of domain should be investigated.
Because we do not change the neural network architecture to integrate simplified corpora, our method can be easily applied to other Neural Text Simplification (NTS) systems.
Answer: The method proposed by the team would not change the neural network architecture to integrate simplified corpora.
Question: Group following sports based on their similarities. Consider the instruments used, place and duration:

Tennis, kick-boxing, football, squash, boxing, rugby, table tennis, taekwondo
Answer: These sports can be categorized into as following groups;
- Footballs sports: Football, rugby 
- Racket sports: tennis, table tennis, squash
- Combat sports: kick-boxing, boxing, taekwondo
Question: How is your work different from other single image tasks that has also adopted gradient penalty?
Evidence: We provide more qualitative results on our model generalizing to unseen images downloaded from the Internet in Fig. 19. Each result is visualized in 6 views. Once again, these additional visual results further indicate that our model demonstrates good generalizability capability for handling unseen indoor scene images.
Answer: Existing works [A, B] do not face the difficulties because [A, B] use global features for predictions, without requiring the local feature sampling procedures. It is important to extract local features when extending from single objects [A, B] to real scenes (see visual results of OccNet + GeoReg v.s. DGS). We added the reference and the discussion in the revision.
Question: What is SVM?
Evidence: In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997[citation needed]) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

The support vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data.[citation needed] These data sets require unsupervised learning approaches, which attempt to find natural clustering of the data to groups and, then, to map new data according to these clusters.
Answer: SVM stands for ""support vector machine". It is a machine learning model for supervised learning tasks. It was popular in the 1990s and early 2000s, before deep learning take off. The classic use case is binary classification, but its extension can also do multi-label classification as we as regression. Kernel methods greatly enhanced SVM's capability to perform non-linear machine learning tasks.
Question: What's the origin of Dog Day Afternoon?
Evidence: Dog Day Afternoon is a 1975 American biographical crime drama film directed by Sidney Lumet and produced by Martin Bregman and Martin Elfand. The film stars Al Pacino, John Cazale, James Broderick, and Charles Durning. The screenplay is written by Frank Pierson and is based on the Life magazine article "The Boys in the Bank" by P. F. Kluge and Thomas Moore. The feature chronicled the 1972 robbery and hostage situation led by John Wojtowicz and Salvatore Naturile at a Chase Manhattan branch in Brooklyn.
Answer: This is a real incident that happened in NY in the mid-70s.  It's about this man (played by Al Pacino) who's married to a woman.  But then he also fell in love with a man.  His partner said he's trapped in the wrong body. So, Al Pacino decided to rob a bank to get money for his partner's sex change.
Question: what is Asia
Evidence: Asia (/ˈeɪʒə/ (listen), also UK: /ˈeɪʃə/) is the largest continent in the world by both land area and population. It covers an area of more than 44 million square kilometers, about 30% of Earth's total land area and 8% of Earth's total surface area.
Answer: Asia (/ˈeɪʒə/ (listen), also UK: /ˈeɪʃə/) is the largest continent in the world by both land area and population. It covers an area of more than 44 million square kilometers, about 30% of Earth's total land area and 8% of Earth's total surface area. The continent, which has long been home to the majority of the human population, was the site of many of the first civilizations. Its 4.7 billion people constitute roughly 60% of the world's population, having more people than all other continents combined.
Question: Why should I change my car oil?
Answer: Changing your car's engine oil is important for the engine's performance and longevity. The oil plays a critical role in engine operation, by providing lubrication between metal surfaces. Without oil, these surfaces will rub against one another and cause permanent damage and unrecoverable damage to the engine. Always use the engine oil type that is suggested by your car's manufacturer, and change the oil at the suggested interval. For many modern cars, this is either every 12 months or after 5000 to 10000 miles, whichever come first.
Question: What is a good place to pick up Thai food when driving from MountainView to Livermore?
Answer: There are a number of places to pick up Thai food when driving north from Mountain view, one that has good reviews and is only a 2 minute detour is RHaan thai.
Question: Where can I go to find out where my polling place is in Wisconsin so that I know where I can vote?
Answer: The MyVote.wi.gov site has several resources to help Wisconsin voters.  One resource allows you to enter in your address, and based on that address, it will show you the address of your polling place, along with a Google map to help you to navigate to it.  Polling places are often changed, so it is a good idea to verify where your polling place is before each election. It can be very frustrating to wait in line at a polling place to vote, only to find out once you get to check-in that you are at the wrong site and have to another polling site where you might have to wait again. This website is provided by the Wisconsin Elections Commission, which is a bipartisan commission charged with implementing Wisconsin's election statutes.
Question: Based on the following paragraph on drinking water, is there a risk of drinking too much water during exercising?
Evidence: The human body contains from 55% to 78% water, depending on body size.[user-generated source?] To function properly, the body requires between one and seven liters (0.22 and 1.54 imp gal; 0.26 and 1.85 U.S. gal)[citation needed] of water per day to avoid dehydration; the precise amount depends on the level of activity, temperature, humidity, and other factors. Most of this is ingested through foods or beverages other than drinking straight water. It is not clear how much water intake is needed by healthy people, though the British Dietetic Association advises that 2.5 liters of total water daily is the minimum to maintain proper hydration, including 1.8 liters (6 to 7 glasses) obtained directly from beverages. Medical literature favors a lower consumption, typically 1 liter of water for an average male, excluding extra requirements due to fluid loss from exercise or warm weather.
Healthy kidneys can excrete 0.8 to 1 liter of water per hour, but stress such as exercise can reduce this amount. People can drink far more water than necessary while exercising, putting them at risk of water intoxication (hyperhydration), which can be fatal. The popular claim that "a person should consume eight glasses of water per day" seems to have no real basis in science. Studies have shown that extra water intake, especially up to 500 milliliters (18 imp fl oz; 17 U.S. fl oz) at mealtime, was associated with weight loss. Adequate fluid intake is helpful in preventing constipation.
Answer: Yes, since exercise induced stress can reduce the volume of water that the kidneys can excrete, drinking too much water during exercise can result in water intoxication which can be fatal.
Question: What artists made up the supergroup The Traveling Wilburys?
Answer: George Harrison, Roy Orbison, Jeff Lynne, Tom Petty and Bob Dylan
Question: Why do children sometimes leave milk and cookies out on Christmas Eve?
Answer: Leaving milk and cookies out the night before Christmas is a tradition based on the assumption that Santa Clause will come down the chimney overnight to deliver presents and he might enjoy a snack.
Question: What is public dashboard?
Evidence: For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.
The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.
Answer: Public dashboard where competitors can see their results during competition, on part of the test set (public test set).
Question: What is the difference between Football and Futsal?
Answer: The size of a football pitch is bigger and longer than that of futsal. The number of football players is 11 while that of futsal players is 5. The size of a football is bigger than that of a futsal ball. Football players do a throwing when a ball crosses over a line while futsal players do kick-in. The game time of football is 45 minutes while that of futsal is 20 minutes.