DataHammer/scimrc huggingface dataset:
Question: What are the results?
Evidence: FLOAT SELECTED: Table 3: Performance of various models on the ACP test set.
FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data.
As for ${\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.
We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\mathcal {L}_{\rm AL}$, $\mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$, $\mathcal {L}_{\rm ACP}$, and $\mathcal {L}_{\rm ACP} + \mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$.
FLOAT SELECTED: Table 3: Performance of various models on the ACP test set.
FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data.
As for ${\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. 
We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\mathcal {L}_{\rm AL}$, $\mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$, $\mathcal {L}_{\rm ACP}$, and $\mathcal {L}_{\rm ACP} + \mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$.
Answer: Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. 
Using a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO.
Question: In which years did the Women's Australian cricket team win the world cup
Answer: 1978
1982
 1988
 1997
 2005
 2013
2022
Question: What is a dig in baseball?
Answer: A "dig" or "digging one out" is an expression to describe a defensive player accepting a baseball with a motion that captures the ball very near the ground.  This kind of expression is often, though not always, used to describe a first baseman catching a ball thrown by another player that bounces before it reaches them as they hold one foot on first base.  This expression can also be used to describe another fielder, typically an infielder, to get a ball into their glove or hand that bounced near them or immediately before reaching them.
Question: Describe some different ways you can make a phone call
Answer: There are many ways you can make a phone call. If you have a mobile phone or landline then the easiest way to make a phone call would be to use one of those devices. If you do not then you could look for a telephone box in your local area. Alternatively you can use a computer to access software phone services such as Twilio that provide a bridge between the internet and international telephony networks.
Question: What is an Angul Thermal Power Station
Answer: Angul Thermal Power Station is a coal based thermal power project located at Derang village in Angul district in Indian state of Odisha. The power plant is one of the coal based power plants of Jindal India Thermal Power Limited.

Coal for the power plant is sourced from Mahanadi Coalfields Limited and water is sourced from Samal Barrage on Brahmani River which is 14 km away.

Bharat Heavy Electricals is the EPC contractor for this project.
Question: Maria Magdelana Von Losch Beyyer know as who
Answer: Marlene Dietrich
Question: Did the bigram language model perform worse than the trigram language model?
Evidence: In this section we present the results from our development stage (Table 2), the evaluation stage (Table 3), and two post-evaluation results (Table 3). Since we implemented both bigram and trigam language models during the development stage but only results from trigram language models were submitted to the task, we evaluated bigram language models in the post-evaluation stage. Note that the accuracy and distance measurements listed in Table 2 and Table 3 are defined by the task organizers BIBREF6 .
Table 2 shows results from the development stage. These results show that for the tweet data the best setting is to keep the # and @, omit sentence boundaries, be case sensitive, and ignore tokenization. While using these settings the trigram language model performed better on Subtask B (.887) and the bigram language model performed better on Subtask A (.548). We decided to rely on trigram language models for the task evaluation since the advantage of bigrams on Subtask A was very slight (.548 versus .543). For the news data, we found that the best setting was to perform tokenization, omit sentence boundaries, and to be case sensitive. Given that trigrams performed most effectively in the development stage, we decided to use those during the evaluation.
Table 3 shows the results of our system during the task evaluation. We submitted two runs, one with a trigram language model trained on the tweet data, and another with a trigram language model trained on the news data. In addition, after the evaluation was concluded we also decided to run the bigram language models as well. Contrary to what we observed in the development data, the bigram language model actually performed somewhat better than the trigram language model. In addition, and also contrary to what we observed with the development data, the news data proved generally more effective in the post–evaluation runs than the tweet data.
In addition, after the evaluation was concluded we also decided to run the bigram language models as well. Contrary to what we observed in the development data, the bigram language model actually performed somewhat better than the trigram language model.
Answer: No, it performed somewhat better than the trigram language model.
Question: When did Porsche stop producing air-cooled engines in the 911?
Evidence: The Porsche 911 (pronounced Nine Eleven or in German: Neunelfer) is a two-door 2+2 high performance rear-engined sports car introduced in September 1964 by Porsche AG of Stuttgart, Germany. It has a rear-mounted flat-six engine and originally a torsion bar suspension. The car has been continuously enhanced through the years but the basic concept has remained unchanged. The engines were air-cooled until the introduction of the 996 series in 1998.
Answer: Porsche stopped producing air-cooled engines in the 911 in 1998 with the 996 series.
Question: Is the dataset multimodal?
Evidence: The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function. The last single neuron with no activation function was added at the end to predict the hate speech detection score.
The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function.
Answer: Yes.
Question: Why should people drive cars with manual transmissions?
Answer: People should drive cars with manual transmissions because it forces drivers to be more engaged while driving and lowers the incidence of distracted driving, making it the safest choice overall.

Environmental concerns about fossil fuels aside, one of the biggest issues with driving today is distracted driving. With improvements in cruise control technology and other driver assistance functions like lane keep assist, cars are taking over more of the driving process than ever before. While the intent is to make it safer for drivers, it also has the unintended consequence of making easier for drivers to be distracted. 

Cars with manual transmissions not only force greater engagement and focus, but they are also either incompatible with, or only compatible with lesser versions of, certain driver assistance features like adaptive cruise control because lowering the speed while not being able to change the gear (which is manual) could cause the car to stall. 

Paradoxically, by increasing the required inputs and decreasing the number of driver assistance features, driving a manual transmission car is safest.
Question: What black effectiveness does this paper clarify?
Evidence: Pre-trained language models such as BERT BIBREF0 have significantly improved the accuracy of various language processing tasks. However, we cannot apply BERT to language generation tasks as is because its model structure is not suitable for language generation. Several pre-trained seq-to-seq models for language generation BIBREF1, BIBREF2 based on an encoder-decoder Transformer model, which is a standard model for language generation, have recently been proposed. These models have achieved blackstate-of-the-art results in various language generation tasks, including abstractive summarization.
However, when generating a summary, it is essential to correctly predict which part of the source text should be included in the summary. Some previous studies without pre-training have examined combining extractive summarization with abstractive summarization BIBREF3, BIBREF4. Although pre-trained seq-to-seq models have achieved higher accuracy compared to previous models, it is not clear whether modeling “Which part of the source text is important?” can be learned through pre-training.
blackThe purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task. Our main contributions are as follows:
We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder.
We proposed a new combination model, the conditional summarization model with important tokens (CIT), in which a token sequence extracted by a saliency model is explicitly given to a seq-to-seq model as an additional input text.
We evaluated the combination models on the CNN/DM BIBREF5 and XSum BIBREF6 datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets.
The purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task.
Answer: It clarifies the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task.
Question: Were any other word similar metrics, besides Jaccard metric, tested?
Evidence: FLOAT SELECTED: TABLE II: A comparison of classification AVCs using word-pairs extracted by different feature selection methods
FLOAT SELECTED: TABLE II: A comparison of classification AVCs using word-pairs extracted by different feature selection methods
Answer: Yes.
Question: How do you jump start a car?
Answer: 1. Make sure both batteries have the same voltage (they probably do unless one car is old). Don’t jump start a frozen battery (it may explode), or a battery with cracks in the casing
Keep metal objects away from the battery. If they cause a spark it could be dangerous.

2. Park the two cars facing each other, close but with enough space to walk in between.

3. Remove the keys from the ignitions, turn off lights/blinkers.

4. Disconnect all accessories (voltage spikes can damage them).

5. Clamp one of the red (positive) clamp to the positive terminal of the dead battery. If there is corrosion on the terminal, clean off or scrape the residue, and/or twist the jaws of the clamp until it cuts through the corrosion and into the metal. Make sure the black clamp of the same end isn’t touching any metal.

6. Connect the other red clamp to the positive terminal of the good battery.

7. Connect the black (negative) clamp to the negative terminal of the good battery.

8. Attach the other black clamp to an unpainted metal surface (e.g. bolt, bracket) on the engine of the car with the dead battery, at least a few inches away from the battery. If a labeled remote negative terminal is available, use it.

9. Confirm that the jumper cables are not near any moving engine parts.

10. Start the booster car, and let it idle for 5 minutes to build up a charge in the dead battery. This will take longer if the dead battery is really empty.

11. Start the car with the dead battery and let the two vehicles idle for a few minutes. If the car doesn’t start, wait a few more minutes and try again. In some instances, slightly revving the engine of the running car while charging the dead battery may help. If it still doesn’t start, it probably won’t.

12. Disconnect the jumper cables reversing the connection order, making sure no disconnected clamps touch any metal or each other

13. Drive the jump-started car for at least 20-30 minutes so it can recharge the battery
Try to keep the jumper cables with you; you may need to jump again the next time you start the car.
Question: What is APR?
Answer: The term annual percentage rate of charge (APR), corresponding sometimes to a nominal APR and sometimes to an effective APR (EAPR), is the interest rate for a whole year (annualized), rather than just a monthly fee/rate, as applied on a loan, mortgage loan, credit card, etc. It is a finance charge expressed as an annual rate.

Those terms have formal, legal definitions in some countries or legal jurisdictions, but in the United States:
(1) The nominal APR is the simple-interest rate (for a year).
(2) The effective APR is the fee+compound interest rate (calculated across a year).
Question: What languages are generally spoken in Macau?
Answer: The official languages are Chinese Mandarin and Portuguese. Other spoken languages include Chinese Cantonese.
Question: Which episode of The X-Files did Dana Scully get diagnosed with cancer?
Answer: Memento Mori, episode 14 of season 4.
Question: Give me a bulleted list of who received the last best player awards at the World Cup.
Answer: The Best Player award at the World Cup, also known as the Golden Ball for best player, was first awarded in 1982. Here are the last seven recipients of the Golden Ball award:
- Brazil Ronaldo (1998 France),
- Oliver Kahn (2002 South Korea/Japan),
- Zinedine Zidane (2006 Germany),
- Diego Forlán (2010 South Africa),
- Lionel Messi  (2014 Brazil),
- Luka Modrić (2018 Russia),
- Lionel Messi (2022 Qatar).
Question: How did the Australian Shepard get its name?
Answer: Despite its name, the Australian Shepard is not actually from Australia but its origins trace back to the United States (California to be specific), where the breed was first developed. Australian Shepards were bred from a variety herding breeds, including shepherds from the Asturias region of Spain, and at one point were known as Spanish Shepherds.
Question: Distinguish between manmade and natural structures from the following:
Statue of Liberty
Suez canal
Niagara Falls
Vatical City
Valley of Death
Himalayas
Angorwat Temple
Taj Mahal
River Rhine
The strait of Malacca
Brooklyn bridge
Answer: Manmade:
Brooklyn bridge
Taj Mahal
Vatical City
Angorwat Temple
Statue of Liberty
Suez canal

Natural structures:
Niagara Falls
Valley of Death
The strait of Malacca
River Rhine
Himalayas
Question: Please classify from which sci-fi universe are following movies, or tv series: The return of the Jedi, Deep Space 9, Avengers
Answer: Deep Space 9 is a tv series from Star Trek universe, The return of a jedi is a movie from Star Wars saga, and Avengers is a tv series from Marvel Universe
Question: Is the sun a star or a planet?
Answer: The sun is not a planet but a star. It is not only the nearest and sole star in our solar system, it also happens to be its center.
Question: What are the words of House Swygert?
Answer: "Truth Conquers"
Question: Which episodes of season four of Game of Thrones did Michelle MacLaren direct?
Answer: She directed "Oathkeeper" and "First of His Name" the fourth and fifth episodes of season four, respectively.
Question: How old was Dominik Volek when he made his Czech Extraliga debut?
Evidence: Dominik Volek (born January 12, 1994) is a Czech professional ice hockey player. He is currently playing for HC Sparta Praha of the Czech Extraliga.

Volek made his Czech Extraliga debut playing with HC Sparta Praha during the 2014–15 Czech Extraliga season. Volek is the son of former New York Islanders forward David Volek.
Answer: Dominik Volek was 20 years old. He debuted in 2014 and was born in 1994, so 2014-1994 = 20.
Question: How do they make sure the languages have never been seen before when verifying whether multi-BERT largely counts on a language-independent strategy?
Evidence: It has been shown that extractive QA tasks like SQuAD may be tackled by some language independent strategies, for example, matching words in questions and context BIBREF20. Is zero-shot learning feasible because the model simply learns this kind of language independent strategies on one language and apply to the other?
To verify whether multi-BERT largely counts on a language independent strategy, we test the model on the languages unseen during pre-training. To make sure the languages have never been seen before, we artificially make unseen languages by permuting the whole vocabulary of existing languages. That is, all the words in the sentences of a specific language are replaced by other words in the same language to form the sentences in the created unseen language. It is assumed that if multi-BERT used to find answers by language independent strategy, then multi-BERT should also do well on unseen languages. Table TABREF14 shows that the performance of multi-BERT drops drastically on the dataset. It implies that multi-BERT might not totally rely on pattern matching when finding answers.
To verify whether multi-BERT largely counts on a language independent strategy, we test the model on the languages unseen during pretraining. To make sure the languages have never been seen before, we artificially make unseen languages by permuting the whole vocabulary of existing languages. That is, all the words in the sentences of a specific language are replaced by other words in the same language to form the sentences in the created unseen language.
Answer: Invisible sentences are artificially created by replacing all words in a sentence in a particular language with other words in the same language.
Question: Which NFL teams have gone to the Super Bowl at least once but have never won a Super Bowl title?
Answer: Buffalo Bills, Minnesota Vikings, Cincinnati Bengals, Atlanta Falcons, Carolina Panthers, Houston/Tennessee/Oilers/Titans, San Diego/Los Angeles Chargers, St. Louis/Phoenix/Arizona Cardinals
Question: How do you wheelie a motorcycle?
Answer: While moving at constant speed, have your right foot covering the brake, compress the motorcycles suspension by pushing down equally on the handle bars and foot pegs, as the suspension starts to rebound upward, blip the throttle. This will lift the front wheel off the ground. If the wheel goes too high use the rear brake to bring it back down.
Question: Why the selection-based QA remains important?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
Recently, various QA tasks that are not selection-based have been proposed (Reddy and Bandyopadhyay, 2006; Hosseini et al., 2014; Jauhar et al., 2016; Sachan et al., 2016); however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT START).
Answer: selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT START).
Question: Why is NFL football called football when players use their hands mainly?
Answer: Sports historians have no clear answer. The current form of football evolved from the 1800s based on rugby and the rules were heavy on a lot of running. My opinion is that influenced the sport and the football word came into origin.
Question: Give me examples of German auto manufacturers still operating.
Answer: BMW, Volkswagen, Mercedes-Benz, Audi, Porsche, Opel.
Question: Write a professional email to my landlord asking whether I can make my rent payment 15 days late next month
Answer: Mr./Mrs. Landlord, I would like to ask that you allow Brian to turn his rent payment in 15 days late next month.  Please note that he has never had a late payment before and actually has a history of turning his payment in early.  As a matter of fact, if you review all of the previous months and added up all of the days he turned his rent in early, you would find it's a total of 30 days early.  So with that in mind, and considering everything going on in his life right now, he is simply asking to delay his payment for 15 days this upcoming month and then 15 days later you will be receiving the following months payment.  Thanks in advance and appreciate your flexibility and patience in this matter.