DataHammer/scimrc huggingface dataset:
Question: Write do's and don'ts for handling a PR crisis. Make them directive and easy for anyone to understand and follow.
Answer: Do:

Assume "worst-case" outcomes for planning purposes.
Inform key audiences regularly and accurately from the outset.
Ensure communications are candid, accurate and timely.
Focus on concrete actions being taken to understand or address the issue.
Retain control of the story. Both positive and negative information needs to come from the company to help assure a transparent posture.
Provide information that's relevant to the public's interest rather than just the company's.
Consider seeking third-party support to help explain the crisis to various audiences.
Explain the situation directly to key audiences using the channels that are familiar to them. 
Where appropriate, contact public officials and leaders proactively. Keep them informed throughout the process.

Don't:

Be silent or make “no comment” statements — they are often interpreted as admissions of guilt and lack of preparation.
Make "off-the-record" statements — there is no such thing in a crisis situation.
Speculate on anything or provide incomplete information. Stick to the facts.
Let erroneous information or ill-informed opinions go unchallenged.
Release sensitive or proprietary information.
Try to minimize the problem, but also don’t make it a bigger deal than it is. Match the level of your response to the severity of the issue.
Rely on communications alone to fix a crisis. You have to address the issue that led to the PR crisis in the first place.
Question: What does the team utilize as regularization in their experiments?
Evidence: We evaluate Jasper across a number of datasets in various domains. In all experiments, we use dropout and weight decay as regularization. At training time, we use speed perturbation with fixed +/-10% BIBREF29 for LibriSpeech. For WSJ and Hub5'00, we use a random speed perturbation factor between [-10%, 10%] as each utterance is fed into the model. All models have been trained on NVIDIA DGX-1 in mixed precision BIBREF30 using OpenSeq2Seq BIBREF31 . Source code, training configurations, and pretrained models are available. 
In all experiments, we use dropout and weight decay as regularization.
Answer: Dropout and weight decay .
Question: What dataset is used for this study?
Evidence: For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.
For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter.
Answer: The answers are shown as follows:
* MH17 Twitter dataset
Question: What are the baselines model?
Evidence: Results of Predictive Models. For the purpose of evaluation, we report the average results after 10-fold cross-validation. Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 . Three metrics are used to compare the results of the competing models with the annotated labels: Root Mean Square Error (RMSE), Pearson's correlation coefficient ( INLINEFORM0 ), and coefficient of determination ( INLINEFORM1 ).
Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 .
Answer: The answers are shown as follows:
* (i) Uniform
* (ii) SVR+W
* (iii) SVR+O
* (iv) C4.5SSL
* (v) GLM
Question: Given a reference text about Lollapalooza, where does it take place, who started it and what is it?
Evidence: Lollapalooza /ˌlɒləpəˈluːzə/ (Lolla) is an annual American four-day music festival held in Grant Park in Chicago. It originally started as a touring event in 1991, but several years later, Chicago became its permanent location. Music genres include but are not limited to alternative rock, heavy metal, punk rock, hip hop, and electronic dance music. Lollapalooza has also featured visual arts, nonprofit organizations, and political organizations. The festival, held in Grant Park, hosts an estimated 400,000 people each July and sells out annually. Lollapalooza is one of the largest and most iconic music festivals in the world and one of the longest-running in the United States.

Lollapalooza was conceived and created in 1991 as a farewell tour by Perry Farrell, singer of the group Jane's Addiction.
Answer: Lollapalooze is an annual musical festival held in Grant Park in Chicago, Illinois. It was started in 1991 as a farewell tour by Perry Farrell, singe of the group Jane's Addiction. The festival includes an array of musical genres including alternative rock, heavy metal, punk rock, hip hop, and electronic dance music. The festivals welcomes an estimated 400,000 people each year and sells out annually. Some notable headliners include: the Red Hot Chili Peppers, Chance the Rapper, Metallica, and Lady Gage. Lollapalooza is one of the largest and most iconic festivals in the world and a staple of Chicago.
Question: How many attention heads are there in the transformer layer?
Evidence: We use a pre-trained Xnlg with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations BIBREF26. In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained with a weight of $0.5$. We train a two-language (English/Chinese) and a three-language (English/French/Chinese) Xnlg for two downstream NLG tasks, respectively. Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later steps, and the learning rate is set to $10^{-4}$. The pre-training batch size is 64, and the sequence length is set to 256. It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question generation and abstractive summarization, respectively. When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
We use a pre-trained XNLG with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations (Hendrycks and Gimpel 2016). In the first pretraining stage, we directly use the 15-language pre-trained XLM (Lample and Conneau 2019) to initialize the parameters of our encoder and decoder
Answer: 8 attention heads.
Question: Given this paragraph about Duchess Auguste of Württemberg, what happened to personal life?
Evidence: On 17 March 1773, Auguste’s spouse succeeded as the 4th Prince of Thurn and Taxis. The marriage was at that point severely deteriorated. Auguste left Karl Anselm and settled in Paris. Rumours claimed that she lived with a lover. However, she was tormented by economical problems, ran out of funds and was hounded by creditors.
Answer: Auguste’s marriage was severely deteriorated and she had debt problems.
Question: 7 factors led to nokia failure
Answer: Nokia total dependency only on hardware
Nokia lack of innovation and accept feedback
Not accepting Android on time and becoming a part of the ecosystem
Rise of the Chinese mobile market as global players
Stephen Elop as the CEO who took a series of bad decision
It could not read the new trend and pushed its arrogance on customer trust
Not incentivising the dealer
Question: Who is the first African-American to perform at the Metropolitan Opera?
Answer: Marian Anderson was the first African-American to perform at the Metropolitan Opera.
Question: What are two models' architectures in proposed solution?
Evidence: Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K\rbrace $ from a pool of candidates, and outputs a chain of selected passages.
Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages. Intuitively, when the Ranker makes more accurate passage selections, the Reasoner will work with less noisy data and thus is easier to succeed. Specifically, the Reasoner learns to extract the linking entity from chains selected by a well-trained Ranker, and it benefits the Ranker training by providing extra rewards. Taking 2-hop as an example, we train the Ranker and Reasoner alternatively as a cooperative game:
Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K\rbrace $ from a pool of candidates, and outputs a chain of selected passages.
Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages.
Answer: The answers are shown as follows:
* Reasoner model, also implemented with the MatchLSTM architecture
* Ranker model
Question: Do all corpora provide datasets/splits for answer selection?
Evidence: All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively. SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times. On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts. Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes.
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate. SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes. It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries. InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes. InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively. In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close.
Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on.
Fig. FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a. Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models.
All corpora provide datasets/splits for answer selection, whereas only (WIKIQA, SQUAD) and (WIKIQA, SELQA) provide datasets for answer extraction and answer triggering, respectively.
Answer: Yes, they do.
Question: In the United States, what type of degree is typically earned before starting graduate school?
Answer: A Bachelors degree is earned before commencing grad school
Question: What models do the authors evaluate?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
Answer: They are context-aware models that have recently been proposed in the literature on this test set.
Question: Who is considered the bachelor president of the United States?
Answer: James Buchanan, the 15th president of the United States.
Question: Classify the cities based on whether they are located in Eritrea or Poland.
Warsaw, Łódź, Assab, Keren, Kraków, Asmara
Answer: Eritrea - Asmara, Keren, Assab
Poland - Warsaw, Kraków, Łódź
Question: Which of their training domains improves performance the most?
Evidence: As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks. It is important to note that this does not include the answers and therefore does not provide supervision to our resolvers. In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. The score for each document is a weighted sum of $F_1(n)$ scores when counting overlapping n-grams: $Similarity\_Score_{document} = \frac{\sum _{n=1}^4nF_1(n)}{\sum _{n=1}^4n}$
The top 0.1% of highest ranked documents is chosen as our new training corpus. Details of the ranking is shown in Figure 2 . This procedure resulted in nearly 1,000,000 documents, with the highest ranking document having a score of $8\times 10^{-2}$ , still relatively small to a perfect score of $1.0$ . We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.
Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing. We next rank the text corpora based on ensemble performance for more reliable results. Namely, we compare the previous ensemble of 10 models against the same set of models trained on each single text corpus. This time, the original ensemble trained on a diverse set of text corpora outperforms all other single-corpus ensembles including STORIES. This highlights the important role of diversity in training data for commonsense reasoning accuracy of the final system.
In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions.
We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.
Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
Answer: The answers are shown as follows:
* documents from the CommonCrawl dataset that has the most overlapping n-grams with the question
Question: Why are dogs called dogs?
Answer: About seven centuries ago, the word hound, which came from the Old English hund, was the word for all domestic canines. Dog was just used to refer to a subgroup of hounds that includes the lovely but frequently slobbering mastiff.

Of course, the opposite is now true. We use dog to talk about all of man’s best friends, from lovable golden retrievers to panting chow chows. And hound is now used to indicate a type of dog used just for hunting. Hound especially refers to a dog with a long face and large, droopy ears. Linguists still speculate about the reversal of fortune for hound and dog. One idea suggests that the sub-breed known as dogs became so populous that dog simply became the generic term (sort of an animal equivalent of the way brand names can become so ubiquitous that they start to be used as a general term for their purpose).
Question: Give me a summary about St Paul's Cathedral
Answer: St Paul's Cathedral is an Anglican cathedral in London and is the seat of the Bishop of London. The cathedral serves as the mother church of the Diocese of London. It is on Ludgate Hill at the highest point of the City of London and is a Grade I listed building. Its dedication to Paul the Apostle dates back to the original church on this site, founded in AD 604. The present structure, dating from the late 17th century, was designed in the English Baroque style by Sir Christopher Wren. Its construction, completed in Wren's lifetime, was part of a major rebuilding programme in the city after the Great Fire of London. The earlier Gothic cathedral (Old St Paul's Cathedral), largely destroyed in the Great Fire, was a central focus for medieval and early modern London, including Paul's walk and St Paul's Churchyard, being the site of St Paul's Cross.

The cathedral is one of the most famous and recognisable sights of London. Its dome, surrounded by the spires of Wren's City churches, has dominated the skyline for over 300 years. At 365 ft (111 m) high, it was the tallest building in London from 1710 to 1963. The dome is still one of the highest in the world. St Paul's is the second-largest church building in area in the United Kingdom, after Liverpool Cathedral.
Question: What are 5 totally distinct, creative ways to use a glass of water?
Answer: 1. Pour half the water out and set the glass on a bedside table as a gentle reminder to look at life as "half full"
2. Dampen t-shirt so others perceive the water as sweat and assume you are working out super hard
3. Break the glass and use the shards to cut fabric similar to a knife or pair of scissors
4. Balance on top of head in order to improve focus and balance while risking getting wet
5. Quickly invert on a table and attempt to prevent water from escaping as a real-time test of liquid physics
Question: How are tornadoes formed?
Answer: Tornadoes are rotating columns of air that is in contact with the ground. The catalyst that lead to tornadoes are temperature and humidity instability. Warm moist air near the ground, with cooler dry air at higher altitudes combined with wind shear, a change in wind speed and/or direction with height. This unstable airmass promotes the development of strong updrafts, while wind shear will further increase the strength of the updraft, and promotes the  rotation from which tornadoes are produced. Tornado intensities are measured using the Enhanced Fujita (EF) scale
Question: How did you prepare for your business case competition?
Answer: To start off, we started getting background information on the topic before proceeding to construct our argument. Next, we finished a rough draft and checked the California FBLA website to make sure we were following the formatting requirements. By February break, we were completely finished with our prejudged report. After figuring out we were prejudged, we created a separate document for our speech. This was because our speech had additional requirements compared to the report; the speech also had to include our personal recommendation on the topic.
Question: How can I help to improve my son's tennis skills?
Answer: There are several options, depends on your son's current age, level. 
If he is below 10, and he just starts, the most important thing for him is keeping interests in tennis. So you can find some local tennis group classes for him, or even semi-private lessons with qualified coaches. 
If he is over 10, and he has some basic level skills already, he could attend some tournament (USTA junior circuit, L7 or even L6), or he could join one USTA jtt team, to play for the team with his friends. Also at this age and level, it is also good to have one private coach for him.
If he has already shown some competitive skills in this sport, you could also find some "big" tournaments, for example, Little Mo Tournament which is national wide tournament for young kids. You can even bring him to watch some live professional player's match.