DataHammer/scimrc huggingface dataset:
Question: What types are the bullying mainly like?
Evidence: While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.
The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying.
Answer: The types are bullies, victims, and bystanders.
Question: Why age 27 is given so much importance specially in Music industry?
Answer: Several renowned music artists died at age 27 which is also sometimes referred as the "27 Club". Often times, the cause of the death is attributed to drug and alcohol abuse or some form of violence like homicide, self harm or accidents. Jimi Hendrix, Janis Joplin, Jim Morrison, Kurt Cobain and Amy Winehouse are some of the few notable personalities in music industry who died at this age.
Question: What is sleep training?
Answer: Sleep training typically refers to the method by which babies and children are taught to sleep on their own through the night without the need for feeding, comfort or support from parents. There are different methods to accomplish this, but a doctor should be consulted before deciding to sleep train a young baby.
Question: What sentiment classifiers are pre-trained in the text?
Evidence: There are a number of off-the-shelf neural models for sentiment classification BIBREF14, BIBREF15, most of which are based on long-short term memory networks (LSTM) BIBREF16 or convolutional neural networks (CNN) BIBREF14. In this paper, we pre-train three sentiment classifiers: BiLSTM, BiLSTM$+$A, and CNN. These classifiers are targeted by white-box attacking methods to generate adversarial examples (detailed in Section SECREF9). BiLSTM is composed of an embedding layer that maps individual words to pre-trained word embeddings; a number of bi-directional LSTMs that capture sequential contexts; and an output layer that maps the averaged LSTM hidden states to a binary output. BiLSTM$+$A is similar to BiLSTM except it has an extra self-attention layer which learns to attend to salient words for sentiment classification, and we compute a weighted mean of the LSTM hidden states prior to the output layer. Manual inspection of the attention weights show that polarity words such as awesome and disappointed are assigned with higher weights. Finally, CNN has a number of convolutional filters of varying sizes, and their outputs are concatenated, pooled and fed to a fully-connected layer followed by a binary output layer.
Recent development in transformer-based pre-trained models have produced state-of-the-art performance on a range of NLP tasks BIBREF17, BIBREF18. To validate the transferability of the attacking methods, we also fine-tune a BERT classifier for black-box tests. That is, we use the adversarial examples generated for attacking the three previous classifiers (BiLSTM, BiLSTM$+$A and CNN) as test data for BERT to measure its classification performance to understand whether these adversarial examples can fool BERT.
There are a number of off-the-shelf neural models for sentiment classification (Kim, 2014; Wang et al., 2016), most of which are based on long-short term memory networks (LSTM; Hochreiter and Schmidhuber (1997)) or convolutional neural networks (CNN; Kim (2014)). In this paper, we pre-train three sentiment classifiers: BiLSTM, BiLSTM+A, and CNN.
Answer: BiLSTM, BiLSTM+A, and CNN.
Question: In what kind of form do they model the input tweets?
Evidence: We first elaborate into the details of the features derived to describe each user's tendency towards each class (Neutral, Racism or Sexism), as captured in their tweeting history. In total, we define the three features INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , representing a user's tendency towards posting Neutral, Racist and Sexist content, respectively. We let INLINEFORM3 denote the set of tweets by user INLINEFORM4 , and use INLINEFORM5 , INLINEFORM6 and INLINEFORM7 to denote the subsets of those tweets that have been labeled as Neutral, Racist and Sexist respectively. Now, the features are calculated as INLINEFORM8 , INLINEFORM9 ,and INLINEFORM10 .
Furthermore, we choose to model the input tweets in the form of vectors using word-based frequency vectorization. That is, the words in the corpus are indexed based on their frequency of appearance in the corpus, and the index value of each word in a tweet is used as one of the vector elements to describe that tweet. We note that this modelling choice provides us with a big advantage, because the model is independent of the language used for posting the message.
Furthermore, we choose to model the input tweets in the form of vectors using wordbased frequency vectorization. That is, the words in the corpus are indexed based on their frequency of appearance in the corpus, and the index value of each word in a tweet is used as one of the vector elements to describe that tweet. We note that this modelling choice provides us with a big advantage, because the model is independent of the language used for posting the message.
Answer: In the form of vectors using wordbased frequency vectorization. That is, the words in the corpus are indexed based on their frequency of appearance in the corpus, and the index value of each word in a tweet is used as one of the vector elements to describe that tweet.
Question: What data is used to build the task-specific embeddings?
Evidence: The system starts with a claim to verify. First, we automatically convert the claim into a query, which we execute against a search engine in order to obtain a list of potentially relevant documents. Then, we take both the snippets and the most relevant sentences in the full text of these Web documents, and we compare them to the claim. The features we use are dense representations of the claim, of the snippets and of related sentences from the Web pages, which we automatically train for the task using Long Short-Term Memory networks (LSTMs). We also use the final hidden layer of the neural network as a task-specific embedding of the claim, together with the Web evidence. We feed all these representations as features, together with pairwise similarities, into a Support Vector Machine (SVM) classifier using an RBF kernel to classify the claim as True or False.
 We also use the final hidden layer of the neural network as a task-specific embedding of the claim, together with the Web evidence. 
Answer: The answers are shown as follows:
* embedding of the claim
* Web evidence
Question: Why did the Allies invade Normandy during world war 2?
Answer: The Allies invaded Normandy to open a 2nd front against the Axis and to liberate Europe from the Germans. The invasion spot was chosen because it was weakly defended and gave great access to the interior of France.  A diversion called Operation Fortitude diverted German resources to Calais, as they believed that to be the primary landing zone for invasion, enabling the Allies to score a decisive victory.
Question: Is morpheme the most basic linguistic unit in Vietnamese?
Evidence: Vietnamese, like many languages in continental East Asia, is an isolating language and one branch of Mon-Khmer language group. The most basic linguistic unit in Vietnamese is morpheme, similar with syllable or token in English and “hình vị” (phoneme) or “tiếng” (syllable) in Vietnamese. According to the structured rule of its, Vietnamese can have about 20,000 different syllables (tokens). However, there are about 8,000 syllables used the Vietnamese dictionaries. There are three methods to identify morphemes in Vietnamese text BIBREF10 .
Morpheme is the smallest meaningful unit of Vietnamese.
Morpheme is the basic unit of Vietnamese.
Morpheme is the smallest meaningful unit and is not used independently in the syntax factor.
In computational linguistics, morpheme is the basic unit of languages as Leonard Bloomfield mentioned for English BIBREF11 . In our research for Vietnamese, we consider the morpheme as syllable, called “tiếng” in Vietnamese (as Nguyen’s definition BIBREF12 ).
The next concept in linguistics is word which has fully grammar and meaning function in sentences. For Vietnamese, word is a single morpheme or a group of morphemes, which are fixed and have full meaning BIBREF12 . According to Nguyen, Vietnamese words are able classified into two types, (1) 1- syllable words with fully meaning and (2) n-syllables words whereas these group of tokens are fixed. Vietnamese syllable is not fully meaningful. However, it is also explained in the meaning and structure characteristics. For example, the token “kỳ” in “quốc kỳ” whereas “quốc” means national, “kỳ” means flag. Therefore, “quốc kỳ” means national flag.
Consider dictionary used for evaluating the corpus, extracting features for models, and evaluating the systems, there are many Vietnamese dictionaries, however we recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary. This dictionary has been built by a group of linguistical scientists at the Linguistic Institute, Vietnam. It was firstly published in 1988, reprinted and extended in 2000, 2005 and 2010. The dictionary currently has 45,757 word items with 15,901 Sino-Vietnamese word items (accounting for 34.75%) BIBREF13 .
The most basic linguistic unit in Vietnamese is morpheme, similar with syllable or token in English and “hinh vj” (phoneme) or “tieng^ (syllable) in Vietnamese.
Answer: Yes.
Question: Make a bullet list of three different types of meat I could get in a taco
Answer: - Pollo Asado
- Carne asada
- Al Pastor
Question: What is a crossbody bag
Answer: A crossbody bag is a purse or sport bag that straddles over one shoulder and crosses over the front and back of the torso, so the bag can hang by the hip.
Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
Evidence: Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0
where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 .
Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 .
Answer: The answers are shown as follows:
* monte-carlo
* sequential sampling
Question: Prove pythagoras theorem.
Answer: There is a geometrical proof that if we square the hypotenuse, it equals the sum of the squares of the sides of a triangle. The sum of the areas of the two squares on the legs (a and b) equals the area of the square on the hypotenuse (c).

Symbolic statement	 a^{2}+b^{2}=c^{2}
Question: Was Dracula a real person.
Answer: No. Dracula is fictitious character. The character first appeared in a book written by Bram Stoker and was inspired by a real life person called Vlad Dracul. Vlad Dracul lived in the 15th century. He was a Transylvanian prince known for their cruelty in punishing enemies and wrong doers.
Question: We are getting a new puppy today, I am writing this messages to you our beloved children that we need to keep the Cats away from the new puppy for at least the first 2 weeks.
Answer: The reason is that the Cat will feel intimated and attack the new puppy if we don't separate them and let them get used to each other for a period of time.
Question: How do I keep my plants alive?
Answer: To keep your plants alive you will first need to understand what type of nutrition and environment is best suited for them. For environment, some plants prefer full sun, whereas some prefer partial or full shade. There's also humidity and temperature to consider. For nutrition, determine what types or food and how much water to provide at what frequency. You can usually find this information by consulting plant expert resources online or at your local library. Once you understand the ideal climate and nutrition for your plant, adjust the climate to best suit it, while following the ideal schedule for feeding and watering. Also, once the plant outgrows the pot it is in, it's time to move it to a bigger pot. This should keep your plant happy and healthy.
Question: Identify which car manufacturer is German or American: Lotec, Aurica Motors
Answer: Lotec is German, Aurica Motors is American
Question: What are some good techniques for cooking a steak?
Answer: Most would agree that the most important element of cooking a steak to get optimal flavor is to be able to achieve very high heat on the cooking surface.  High heat would be 500 degrees or more.  

Where opinion varies is when to put the steak onto that high heat surface.  

The more traditional technique calls for searing the steak on both sides first, and then cooking it until you hit your target temperature.  

But more and more, people seem to prefer what many call a "reverse sear" technique, in which you would slow roast the steak until it is near done, and then placing the steak on the high heat surface to achieve a very dark, even sear all over the steak's surface.
Question: What metrics are used as evaluation metrics?
Evidence: The articles from the newspaper were fed in spaCy library into the proper format for training. Different parameters were tested, in order to get the optimal result. The dataset was shuffled, using the same seed for all the experiments and was split into a train set (70%), a test set (20%) and a validation set (10%). Information was passed through the training algorithm in batches with an increasing batch size from 4 to 32 and a step of 1.001. Additionally, a dropout rate was configured in every batch, initialized to 0.6 which dropped during the training process to 0.4. Most of the experiments were trained using 30 epochs.
The main area of study for the experiments focuses on three important components. At first, we investigate the difference in results between part of speech taggers that classify morphological features and taggers that detect only the part of speech. Moreover, we explore the significance of pretrained vectors used from a model and their effect on the extraction of better results. Most importantly, the usage of subwords of tokens from a tagger as embeddings is issued. For the experiments, precision, recall and f1 score are used as evaluation metrics.
For the experiments, precision, recall and f1 score are used as evaluation metrics.
Answer: Precision, recall and f1 score.
Question: What do the results show?
Evidence: We approach understanding NMT by investigating the word importance via a gradient-based method, which bridges the gap between word importance and translation performance. Empirical results show that the gradient-based method is superior to several black-box methods in estimating the word importance. Further analyses show that important words are of distinct syntactic categories on different language pairs, which might support the viewpoint that essential inductive bias should be introduced into the model design BIBREF28. Our study also suggests the possibility of detecting the notorious under-translation problem via the gradient-based method.
This paper is an initiating step towards the general understanding of NMT models, which may bring some potential improvements, such as
Interactive MT and Constraint Decoding BIBREF29, BIBREF26: The model pays more attention to the detected unimportant words, which are possibly under-translated;
Adaptive Input Embedding BIBREF30: We can extend the adaptive softmax BIBREF31 to the input embedding of variable capacity – more important words are assigned with more capacity;
NMT Architecture Design: The language-specific inductive bias (e.g., different behaviors on POS) should be incorporated into the model design.
We can also explore other applications of word importance to improve NMT models, such as more tailored training methods. In general, model interpretability can build trust in model predictions, help error diagnosis and facilitate model refinement. We expect our work could shed light on the NMT model understanding and benefit the model improvement.
There are many possible ways to implement the general idea of exploiting gradients for model interpretation. The aim of this paper is not to explore this whole space but simply to show that some fairly straightforward implementations work well. Our approach can benefit from advanced exploitation of the gradients or other useful intermediate information, which we leave to the future work.
Empirical results show that the gradient-based method is superior to several black-box methods in estimating the word importance.
Answer: Empirical results show that the gradient-based method is superior to several black-box methods in estimating the word importance.
Question: Give me a list of some of the most popular song from 70s Japanese Pop singer, Mariya Takeuchi
Answer: 1. Plastic Love
2. Stay with Me
3. September
4. Miracle Love
5. Yume No Tsuzuki
Question: Why is merino wool a great material for clothing?
Answer: While traditional wool may be known as itchy and rough, merino wool is a soft fabric that offers many benefits over alternative materials such as cotton. Merino wool has moisture wicking abilities that work to keep you dry and sweat free. It may also have antibacterial properties that can help reduce body odor. These characteristics make merino wool a great choice for clothing, and an especially great choice for socks.
Question: Explain why Key West is a great location for a vacation.
Answer: Key West is a great place to go for various reasons. It provides a lot of the same allure that the Caribbean offers, however, still remains within the US. Therefore, you do not have to worry about a passport, customs, or anything that deals with traveling internationally. It sits as an island off the coast of mainland Florida and has some of the clearest blue water. While it is an island and has several beaches, it also has a great downtown. Duvall street specifically is filled with restaurants, bars, shops and other attractions for tourists. It is a ton of fun and has something for everyone. For some people who just want to sit near the water and relax, its great for that - for someone who likes to be active and enjoy going out at night it has that as well. With so many options, it's a great destination for couples as well as groups of people depending on the occasion. Between everything mentioned and not having to worry about going through customs, forgetting your passport (or anything else that can happen when traveling internationally), etc - Key West serves as a relatively lower maintenance excursion, while reaping the same benefits as going somewhere tropical.
Question: How to prepare without baking dessert recipe -Banana Pudding?
Answer: Take a casserole mold. Layer the mold's base with cookies of your choice. Slice up 1½ - 2 bananas and layer them above the cookies. The pudding mixture can be made by following the instructions on the box. Mix the tub of Cool Whip well before pouring it above the pudding mixture. Pour half of the pudding mix on the first layer of bananas and cookies. Add more bananas and cookies, followed by more pudding mixture. Repeat the process to add more layers. Use cookie crumbs and sprinkle on top. Refrigerate until serving.
Question: Given this paragraph about modern triathlon, what year did the two founders form mission bay triathlon.
Evidence: The first modern swim/bike/run event was held at Mission Bay, San Diego, California on September 25, 1974. The race was conceived and directed by two members of the San Diego Track Club, Jack Johnstone and Don Shanahan. Johnstone recalls that he was a part of the 70s jogging craze in America and that after entering a few races he was not regaining his "mediocre fitness" despite having been a member of the 1957 Collegiate and AAU All-American swim teams. Then in 1973, Johnstone learned of the Dave Pain Birthday Biathlon, a 7.2 km (4.5 mi) run followed by what was billed as a quarter-mile (0.4 km) swim (the actual distance was only between 200 and 300 yards). The following year, after competing in the event for the second time and placing in the top ten, Johnstone desired more of this style of race and with equal emphasis on the swim. So he petitioned the chairman of the San Diego Track Club who told him he would add a race to the club calendar. But the rest of the race was up to Johnstone to organise, and at the same time he was to contact Don Shanahan so there wouldn't be too many "weird" races on the club schedule. Shanahan told Johnstone that he wanted to include a biking leg to the race; whilst hesitant Johnstone agreed to the addition. When naming the event the pair used the unofficially agreed naming system for multisport events, already used for pentathlon, heptathlon, and decathlon. So they used the Greek prefix tri (three) for the number of events, followed by the already familiar athlon, hence naming the event the Mission Bay Triathlon. It is worthy of note that neither founder had heard of the French events; both believed their race was a unique idea.
Answer: Modern day triathlon is believed to have occurred in 1974 when founded by two members of the San Diego track club, Don Shanahan and Jack Johnstone. Although the pair reportedly founded the sport, it's important to understand not everything is created and owned by americans. The sport can be traced back to france in 1901, originally called "Le Trois Sports", which included Bike, Run and Canoe disciplines. Other forms of multisport had occurred before 1974 including biathlon which included a run of 7.2km followed by a swim of 0.4km) but Nonetheless, on the 25th September 1974, triathlon mission bay in San Diego, California was born.