DataHammer/scimrc huggingface dataset:
Question: On a gravel bike ride, categorize which of these items are 'useful', 'not useful', 'dangerous', or ,'neither': shovel, spare tire, clock, horn, water bottle, gps, knife, snake
Answer: shovel = not useful
spare tire = useful
clock = neither
horn = useful
water bottle = useful
gps = useful
knife = useful
snake = dangerous
Question: What simulations are performed by the authors to validate their approach?
Evidence: We evaluate our model in a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command. Each environment contains between three and five objects differentiated by their size (small, large), shape (round, square) and color (red, green, blue, yellow, pink), totalling in 20 different objects. Depending on the generated scenario, combinations of these three features are necessary to distinguish the targets from each other, allowing for tasks of varying complexity.
We evaluate our model in a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command. Each environment contains between three and five objects differentiated by their size (small, large), shape (round, square) and color (red, green, blue, yellow, pink), totalling in 20 different objects. Depending on the generated scenario, combinations of these three features are necessary to distinguish the targets from each other, allowing for tasks of varying complexity.
Answer: The answers are shown as follows:
* a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command
Question: Whether adding the convolution layer help to boost the F1-OK scores when translating from English to other languages?
Evidence: In Table TABREF21 , we show the ablation study of the features used in our model on English-German, German-English, and English-Czech. For each language pair, we show the performance of CEQE without adding the corresponding components specified in the second column respectively. The last row shows the performance of the complete CEQE with all the components. As the baseline features released in the WMT2018 QE Shared Task for English-Latvian are incomplete, we train our CEQE model without using such features. We can glean several observations from this data:
Because the number of “OK” tags is much larger than the number of “BAD” tags, the model is easily biased towards predicting the “OK” tag for each target word. The F1-OK scores are higher than the F1-BAD scores across all the language pairs.
For German-English, English Czech, and English-German (SMT), adding the baseline features can significantly improve the F1-BAD scores.
For English-Czech, English-German (SMT), and English-German (NMT), removing POS tags makes the model more biased towards predicting “OK” tags, which leads to higher F1-OK scores and lower F1-BAD scores.
Adding the convolution layer helps to boost the performance of F1-Multi, especially on English-Czech and English-Germen (SMT) tasks. Comparing the F1-OK scores of the model with and without the convolution layer, we find that adding the convolution layer help to boost the F1-OK scores when translating from English to other languages, i.e., English-Czech, English-German (SMT and NMT). We conjecture that the convolution layer can capture the local information more effectively from the aligned source words in English.
Adding the convolution layer helps to boost the performance of F1-Multi, especially on English-Czech and English-Germen (SMT) tasks. Comparing the F1-OK scores of the model with and without the convolution layer, we find that adding the convolution layer help to boost the F1-OK scores when translating from English to other languages, i.e., English-Czech, English-German (SMT and NMT).
Answer: Yes.
Question: What was revolutionary about the Barbie doll when it was first sold?
Answer: The Barbie doll was one of the first dolls for children that was an adult, rather than the traditional baby doll
Question: What does this paper develop?
Evidence: Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain. Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group. Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0. Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far. For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances. Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition. The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality. Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical.
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues. According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT. In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.
According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT), FriendsBERT and ChatBERT.
Answer: Develop two classification models, inspired by bidirectional encoder representations from transformers (BERT), FriendsBERT and ChatBERT.
Question: What model do Hasan and Ng use to model-dependent relationships to the preceding post (Hasan and Ng, 2013b)?
Evidence: In this paper we aim to use text as well as other features to see how they complement each other in a deep learning model. In the stance classification domain, previous work has showed that text features are limited, suggesting that adding extra-linguistic constraints could improve performance BIBREF6 , BIBREF7 , BIBREF8 . For example, Hasan and Ng as well as Thomas et al. require that posts written by the same author have the same stance BIBREF9 , BIBREF10 . The addition of this constraint yields accuracy improvements of 1–7% for some models and datasets. Hasan and Ng later added user-interaction constraints and ideology constraints BIBREF7 : the former models the relationship among posts in a sequence of replies and the latter models inter-topic relationships, e.g., users who oppose abortion could be conservative and thus are likely to oppose gay rights.
For work focusing on online forum text, since posts are linked through user replies, sequential labeling methods have been used to model relationships between posts. For example, Hasan and Ng use hidden Markov models (HMMs) to model dependent relationships to the preceding post BIBREF9 ; Burfoot et al. use iterative classification to repeatedly generate new estimates based on the current state of knowledge BIBREF11 ; Sridhar et al. use probabilistic soft logic (PSL) to model reply links via collaborative filtering BIBREF12 . In the Facebook dataset we study, we use comments instead of reply links. However, as the ultimate goal in this paper is predicting not comment stance but post stance, we treat comments as extra information for use in predicting post stance.
For example, Hasan and Ng use hidden Markov models (HMMs) to model dependent relationships to the preceding post (Hasan and Ng, 2013b);
Answer: Hidden Markov models (HMMs).
Question: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
Evidence: We train our models on Sentiment140 and Amazon product reviews. Both of these datasets concentrates on sentiment represented by a short text. Summary description of other datasets for validation are also as below:
We have provided baseline results for the accuracy of other models against datasets (as shown in Table 1 ) For training the softmax model, we divide the text sentiment to two kinds of emotion, positive and negative. And for training the tanh model, we convert the positive and negative emotion to [-1.0, 1.0] continuous sentiment score, while 1.0 means positive and vice versa. We also test our model on various models and calculate metrics such as accuracy, precision and recall and show the results are in Table 2 . Table 3 , Table 4 , Table 5 , Table 6 and Table 7 . Table 8 are more detail information with precisions and recall of our models against other datasets.
We train our models on Sentiment140 and Amazon product reviews. Both of these datasets concentrates on sentiment represented by a short text. 
For training the softmax model, we divide the text sentiment to two kinds of emotion, positive and negative. And for training the tanh model, we convert the positive and negative emotion to [-1.0, 1.0] continuous sentiment score, while 1.0 means positive and vice versa. 
Answer: Yes.
Question: How was the corpus annotated?
Evidence: Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states.
Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. To evaluate the quality of the annotation of dialogue acts and states, three experts were employed to manually annotate dialogue acts and states for 50 dialogues. The results show that our annotations are of high quality. Finally, each dialogue contains a structured goal, a task description, user states, system states, dialogue acts, and utterances.
Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states.

Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. To evaluate the quality of the annotation of dialogue acts and states, three experts were employed to manually annotate dialogue acts and states for 50 dialogues. The results show that our annotations are of high quality. Finally, each dialogue contains a structured goal, a task description, user states, system states, dialogue acts, and utterances.
Answer: The answers are shown as follows:
* The workers were also asked to annotate both user states and system states
* we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories
Question: If you have one of the following issues, should you consult a doctor, a lawyer, or a plumber: a leaky faucet, a persistent cough, a small claims court summons, a torn meniscus, a urinary tract infection, a broken toilet, a federal indictment, or a search warrant from the FBI.
Answer: If you have a leaky faucet or a broken toilet, you should call a plumber. If you have a persistent cough, a torn meniscus or a urinary tract infection, you should consult a physician. If you have received a small claims court summons or a federal indictment, or a search warrant from the FBI, you will probably want to lawyer up.
Question: What are the new 7 Wonders of the World:
Answer: These are the new 7 Wonders of the World:

- Great Wall of China
- Petra
- Christ the Redeemer
- Machu Picchu
- Chichén Itzá
- Colosseum
- Taj Mahal
Question: List the sweet culinary specialities of New Orleans from this provided passage
Evidence: New Orleans is known for specialties including beignets (locally pronounced like "ben-yays"), square-shaped fried dough that could be called "French doughnuts" (served with café au lait made with a blend of coffee and chicory rather than only coffee); and po' boy and Italian muffuletta sandwiches; Gulf oysters on the half-shell, fried oysters, boiled crawfish and other seafood; étouffée, jambalaya, gumbo and other Creole dishes; and the Monday favorite of red beans and rice (Louis Armstrong often signed his letters, "Red beans and ricely yours"). Another New Orleans specialty is the praline locally, a candy made with brown sugar, granulated sugar, cream, butter, and pecans. The city offers notable street food including the Asian inspired beef Yaka mein.
Answer: The sweet culinary specialities of New Orleans include:
- Beignets: Square shaped fried dough similar to donuts
- Praline: A candy made with sugar, cream and pecans
Question: Who is Mother Teresa?
Answer: Mother Teresa was was an Albanian-Indian Catholic nun and the founder of the Missionaries of Charity. She was born in 1910 and died in September of 1997.  When she was 18,  moved to Ireland and then to India, where she lived most of her life.  

The Missionaries of charity manages homes for people who are dying of HIV/AIDS, leprosy, and tuberculosis. They also run soup kitchens, dispensaries, mobile clinics, children's and family counseling programs, as well as orphanages and schools. Members of this congregation take vows of chastity, poverty, and obedience and also profess a fourth vow: to give "wholehearted free service to the poorest of the poor."  Pope Francis canonized her St. Teresa of Calcutta on September 4th, 2016 in St. Peter's Square in Vatican City.
Question: Given this paragraph, how big are the non-volley zones in pickleball and where are they located?
Evidence: The appearance of a pickleball court, and the manner of play, resembles tennis, but the court is the size of a doubles badminton court, less than a third the size of a tennis court. Court lines and rules are specific to pickleball and include two 7-foot areas (2.1 m) on either side of the net known as the non-volley zones, where the ball cannot be hit with the paddle unless the ball bounces first. The official rules specify side-out scoring, where only the serving team can score a point. All serves are made with an underhand stroke. The hard plastic ball used in pickleball produces significantly less bounce than softer flexible balls, such as a tennis ball. The minimal bounce combined with the non-volley zone and underhand serve give the game dynamic pace and strategy ranging from soft dink shots that stay within the two non-volley zones to powerful drive shots and overhead smash shots. To minimize any advantage the serving or receiving side might have at the beginning of the game, the ball must bounce once on each side of the net before either team may volley the ball, which is to hit the ball in the air before it bounces.
Answer: 7-foot areas on either side of the net known
Question: How do you live a life full of gratitude?
Answer: Each day we are faced with both positive and negative experiences. As humans we tend to focus on the negative experiences, but it is important to recognize that the challenges in life make us stronger and shape us into who we are. To have more gratitude it's important to be grateful for even the small things in life as well. Setting aside time each day to write down the things or people in your life that you're grateful for can help shift your mindset, and train your brain towards gratitude.
Question: Give me a list of the top five most followed Instagram profiles, along with their number of followers.
Answer: 1. @Instagram,  623 millions
2. @Cristiano, 567 millions
3. @leomessi, 448 millions
4. @selenagomez, 407 millions
5. @kyliejenner, 383 millions
Question: Write a facebook post responding to a question about whether public or private school is better
Answer: I really think it comes down to personal preference and the type of kid(s) you have. I don't think there is a universal one size fits all approach here. There's a lot of factors that can go into the decision, whether you live in a good school district, if your kid is special needs or struggles socially, whether you can afford private school, on and on. For us, we decided to go public because our school district is relatively good. Our school is close to our house and our kids do relatively well in school. So far, we've been happy. My one complaint is that there is not a ton of personal attention so I do think it requires a lot of parent involvement to keep kids on track. Whatever you decide, you just need to weigh what is right for your family.
Question: Tell me whether these are famous as actors or authors: Russell Crowe, Tom Hanks, Jamie Lee Curtis, Joanne Woodward, Sara Jessica Parker, Tom Cruise, Alexander Dumas, JK Rowling, Ingrid Bergman, Grace Kelly,  Judi Dench, Kate Winslet, Jodie Foster, Doris Day, William Shakespeare, Jalal al-Din Muhammad Rumi
Answer: The famous actors from this list are, Russell Crowe, Tom Hanks, Jamie Lee Curtis, Joanne Woodward, Sara Jessica Parker, Tom Cruise, Ingrid Bergman, Grace Kelly, Judi Dench, Kate Winslet, Jodie Foster and Doris Day.  The famous authors are, Alexander Dumas, JK Rowling, William Shakespeare and Jalal Al-Din Muhammad Rumi.
Question: Why are cookies delicious?
Answer: Cookies are delicious typically because they contain fat (oils usually), sugar, and salt in addition to carbohydrates from flour, many ingredients of which human taste buds enjoy. They are also baked, adding the heat element, which chemically alters them to be more palatable.
Question: Give  me a list of cities where I can stop/stay as I drive from Austin to Chicago by road
Answer: There could be multiple itineraries for a road trip from Austin to Chicago, as it's a 1000+ mile drive, but some of the prominent cities that you drive through are
Dallas / Fort Worth, Texas
Little Rock, Arkansas
Memphis, Tennessee
St Louis, Missouri
Question: What is Docker?
Answer: Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development – desktop and cloud. Docker’s comprehensive end to end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle.
Question: What is the significance of a diploma?
Evidence: A diploma is a document awarded by an educational institution (such as a college or university) testifying the recipient has graduated by successfully completing their courses of studies. Historically, it has also referred to a charter or official document of diplomacy.
Answer: A diploma testifies the recipient has graduated by successfully completing their courses of studies.
Question: Classify the following items as either cars or trucks:  Ford F150, Audi A8, BMW 318i, Nissan Titan, Tesla Model 3
Answer: Ford F150 - truck; Audi A8 - car; BMW 318i - car; Nissan Titan - truck; Tesla Model 3 - car
Question: What are the drawbacks of nuclear fusion?
Answer: List of Cons of Nuclear Fusion
1. It is extremely difficult to achieve. 
In stars, strong gravitational forces and high temperatures naturally create a fusion environment. But here on Earth, we are facing the challenge to make nuclear fuel hot and confined enough to start a self-sustaining ignition.

Imagine trying to contain the plasma (a gaseous mixture of deuterium, tritium atoms and ions, and helium the fusion product) at 100 million degrees celsius. No material can withstand that temperature. So, scientists attempt to keep the plasma (being electrically charged and having a magnetic field of its own) suspended in a magnetic field produced by superconducting magnets around the fusion chamber/vessel. This is similar to how bullet trains float on their tracks at ridiculous speeds. This process is very difficult to achieve (as compared to nuclear FISSION).

2. It produces radioactive waste.
Though nuclear power plants only emit negligible amounts of carbon dioxide into the atmosphere, its nuclear fuel chain process does produce radioactive waste.

The radioactive waste produced with fusion is not the same as with fission, and the two are often confused. With a nuclear fission reactor, the radiation is alpha particles, beta particles, and gamma rays (which can penetrate your skin and break apart the bonds in your DNA structure, giving you all kinds of cancer). In contrast, in a nuclear fusion reactor, the vessel wall is the only part that will be bombarded by the high energy neutrons, and if, in the worst case, all the protective layers surrounding the main fusion vessel fail, the neutron radiation will stop as soon as fusion reaction stops. In a fission reactor, the cancer-causing radiation still exists even in the waste materials, which means that extreme measures are needed to burry the waste to keep it as far away as possible from humans. In the case of nuclear fusion, the activated materials (i.e., the metal vessels which have been bombarded by neutrons) can be stored safely for about 100 years, after which the radiation level becomes so low that they can be reused in the fusion reactor again.