DataHammer/scimrc huggingface dataset:
Question: How do they evaluate the sentence representations?
Evidence: Unsupervised Evaluation
The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 .
The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset.
Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 .
Unsupervised Evaluation
The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 .

The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset.

Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 .
Answer: The answers are shown as follows:
* The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 .

The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset.
* Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 .
Question: Which six languages are experimented with?
Evidence: In this section we report on the experiments performed using the system and data described above. First we will present the English results for the three ABSA editions as well as a comparison with previous work. After that we will do the same for 5 additional languages included in the ABSA 2016 edition: Dutch, French, Russian, Spanish and Turkish. The local and clustering features, as described in Section SECREF11 , are the same for every language and evaluation setting. The only change is the clustering lexicons used for the different languages. As stated in section SECREF11 , the best cluster combination is chosen via 5-fold cross validation (CV) on the training data. We first try every permutation with the Clark and Word2vec clusters. Once the best combination is obtained, we then try with the Brown clusters obtaining thus the final model for each language and dataset.
In this section we report on the experiments performed using the system and data described above. First we will present the English results for the three ABSA editions as well as a comparison with previous work. After that we will do the same for 5 additional languages included in the ABSA 2016 edition: Dutch, French, Russian, Spanish and Turkish. The local and clustering features, as described in Section SECREF11 , are the same for every language and evaluation setting. The only change is the clustering lexicons used for the different languages. As stated in section SECREF11 , the best cluster combination is chosen via 5-fold cross validation (CV) on the training data. We first try every permutation with the Clark and Word2vec clusters. Once the best combination is obtained, we then try with the Brown clusters obtaining thus the final model for each language and dataset.
Answer: The answers are shown as follows:
* Dutch
* French
* Russian
* Spanish 
* Turkish
* English
Question: What sport is Ruud Gullit known for
Answer: Ruud Gullit is best known for his career in professional football, most notably for his time with Dutch club Ajax.
Question: Is a Handball a 'Direct' or 'Indirect' Kick?
Answer: As a rule, a direct kick occurs following a contact foul or hand ball. Most other football infringements are indirect.

If a defending team commits a contact foul or hand ball within their own penalty area (a marked out box at each end of the field) - the result is a penalty kick.
Question: Who was Singhari Shyamasundar Kar
Evidence: Desaraja Adiguru Singhari Shyamasundar Kar (Odia: ସିଂହାରୀ ଶ୍ୟାମସୁନ୍ଦର କର, romanized: Singhāri Syāmasundara Kara; 4 March 1908 – 16 March 1975) was a renowned Odissi musician, Guru, singer, scholar and composer. Born to a sebayata (servitor) family of the Jagannatha Temple, he was groomed under veteran masters of the temple tradition and soon rose to be one of the towering Gurus of Odissi classical music in the 20th century. He was most known for his powerful voice and intricate style, his command over the Mardala as well as his contribution towards the academic institutionalization of Odissi music education. Most of the performers of Odissi music, Mardala and Odissi dance of his period admit to having come under the commanding influence and knowledge of Singhari. He died on 16 March 1975.
Answer: Desaraja Adiguru Singhari Shyamasundar Kar (4 March 1908 – 16 March 1975) was a renowned Odissi musician, Guru, singer, scholar and composer. Born to a sebayata (servitor) family of the Jagannatha Temple, he was groomed under veteran masters of the temple tradition and soon rose to be one of the towering Gurus of Odissi classical music in the 20th century. 

He was most known for his powerful voice and intricate style, his command over the Mardala as well as his contribution towards the academic institutionalization of Odissi music education. Most of the performers of Odissi music, Mardala and Odissi dance of his period admit to having come under the commanding influence and knowledge of Singhari. He died on 16 March 1975.
Question: Write a story about somebody, anybody, nobody and everybody
Answer: One day, a mysterious stranger arrived in the village. He was a man of many faces, and he had a story to tell. He told them of his journeys and his adventures, and his tales of the world beyond. The villagers were entranced by his stories and his tales of the world beyond.

And so, from that day on, the villagers and the stranger became best friends. They laughed and talked and explored the world together. They were one big family, and they shared their stories with each other.

The stranger's tales inspired the villagers and gave them hope and courage. They could see the world beyond their village and they could make their own stories.

The stranger taught them to dream and to follow their hearts. He showed them that anything was possible, and he taught them to be brave and to never give up.

And so, the villagers and the stranger became the best of friends. They were no longer strangers, but they were family. And from that day on, the village was a place of beauty and peace.
Question: If you were to compete in the Olympics, which sport would you pick?
Answer: If I were to compete in the Olympics, I would compete in water polo. Water polo is a really fun sport to play, that is dynamic and fast paced to both watch and play. I don't like long breaks, so I like how fast water polo is. I also love swimming, and I like that water polo is rooted in California culture, which is where I am from.
Question: Is Star Wars Science Fiction or Fantasy?
Answer: While Star Wars is considered science fiction by the general public, most sci-fi and fantasy fans would classify Star Wars in the fantasy genre. The themes of the story rely on mythology and the hero's journey as opposed to exploration of scientific and philosophical concepts.
Question: Give me a list of songs to listen to with the artist for each song.
Answer: Here Comes the Sun by the Beatles, Ready to Run by the Chicks, Love Story by Taylor Swift, Call Me Home by The Dreggs and The Internet by Jon Bellion
Question: Can domain adaptation be applied to sarcasm detection to leverage patterns in out-of-domain training data through their validation?
Evidence: Sarcasm, a creative device used to communicate an intended meaning that is actually the opposite of its literal meaning, is notoriously difficult to convey and interpret through text, in part because doing so relies heavily upon shared contextual understandings that can be marked more easily by altered prosody (e.g., emphasis upon certain words) or non-verbal signals (e.g., rolling one's eyes). It is a complex process even for humans, and in fact an inability to detect sarcasm has been linked with a number of neurocognitive disorders, including dementia BIBREF0 . It is similarly a challenging open task in natural language processing, and has direct implications to a number of other critical application areas, such as sentiment analysis.
Most research on automatic sarcasm detection to date has focused on the Twitter domain, which boasts an ample source of publicly-available data, some of which is already self-labeled by users for the presence of sarcasm (e.g., with #sarcasm). However, Twitter is highly informal, space-restricted, and subject to frequent topic fluctuations from one post to the next due to the ebb and flow of current events—in short, it is not broadly representative of most text domains. Thus, sarcasm detectors trained using features designed for maximum Twitter performance are not necessarily transferable to other domains. Despite this, it is desirable to develop approaches that can harness the more generalizable information present in the abundance of Twitter data.
In this work, we develop a set of domain-independent features for sarcasm detection and show that the features generally perform well across text domains. Further, we validate that domain adaptation can be applied to sarcasm detection to leverage patterns in out-of-domain training data, even when results from training only on that source domain data are extremely bad (far below baseline results), to improve over training on only the target data or over training on the simply combined dataset. Finally, we make a new dataset of sarcastic and non-sarcastic tweets available online as a resource to other researchers.
Further, we validate that domain adaptation can be applied to sarcasm detection to leverage patterns in out-of-domain training data, even when results from training only on that source domain data are extremely bad (far below baseline results), to improve over training on only the target data or over training on the simply combined dataset.
Answer: Yes.
Question: Why do people like to drink coffee in the morning?
Answer: Coffee is very popular to drink in the morning. It contains caffeine and caffeine wakes you up. In addition drinking coffee is a social thing and makes people feel more connected. Also some people think coffee is healthy. You can become addicted to coffee.
Question: What are the currently available datasets for EAC?
Evidence: Nowadays, most of chatbots technologies were built by using neural-based approach. Emotional Chatting Machine (ECM) BIBREF15 was the first works which exploiting deep learning approach in building a large-scale emotionally-aware conversational bot. Then several studies were proposed to deal with this research area by introducing emotion embedding representation BIBREF24 , BIBREF25 , BIBREF26 or modeling as reinforcement learning problem BIBREF27 , BIBREF28 . Most of these studies used encoder-decoder architecture, specifically sequence to sequence (seq2seq) learning. Some works also tried to introduce a new dataset in order to have a better gold standard and improve system performance. BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. Their dataset was used to develop a chatbot which captures human emotional states and elicits positive emotion during the conversation.
BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. 
Answer: The answers are shown as follows:
* EMPATHETICDIALOGUES dataset
* a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries
* SEMAINE corpus BIBREF30
Question: Tell me different stages in data lifecycle.
Answer: 1. Data Ingestion: In this stage, data from producers is ingested and collected for later processing.
2. Data Processing: Once the data is ingested and collected, mostly in raw format, it goes through various processing steps. It is one of the core responsibilities of data pipelines. This stage can further divide into sub-stages.
3. Data Storage: All the collected and processing is stored in various formats. Based on specific needs, there can be one or more tools or paradigm employed.
4. Data Analysis or Visualisation: In this layer, processed data is analysed to derive meaningful insights and mostly present in some visual form.
Question: What does the acronym IMET stand for?
Evidence: International Military Education and Training (IMET) is the title of a United States security assistance program, a type of student exchange program.
Answer: International Military Education and Training
Question: What is America's oldest brewery?
Answer: D.G. Yuengling and Son is the oldest recognized brewery in America.
Question: What steps can I take to avoid getting hit by a falling window from a skyscraper while walking about in downtown during high winds?
Answer: Here are some steps you can take to avoid getting hit by falling windows detached from tall building by strong winds from cyclones or tornadoes: 
1. The space area close to a doorway or near windows is most likely to be affected by falling items, and it is best to stay away from buildings, trees and tall electric or telephone poles. Find an open space, far away from these objects and stay low to the ground to reduce your chances of being hit by debris.
2. One of the safest places to avoid getting hit by falling objects is underground in a basement with no windows. Try to find underground spaces in downtown like the lower levels of buildings and malls, or the subway. 
3. If you absolutely need to be outside and close to tall buildings, try to stay alert and keep an eye out for falling debris. 
4. You might think that being inside a car would be a safe option, but vehicles are notoriously dangerous places to be in during high winds because of their susceptibility to being blown over or blown away or for their windows to explode.
Question: Give me a bulleted list of the five most recent Italian prime ministers.
Answer: . Giorgia Meloni
. Mario Draghi
. Giuseppe Conte
. Paolo Gentiloni
. Matteo Renzi
Question: From the passage provided, extract where the first session of Indian National Congress was held
Evidence: The Indian National Congress conducted its first session in Bombay from 28 to 31 December 1885 at the initiative of retired Civil Service officer Allan Octavian Hume, known for his pro-Indian activities. In 1883, Hume had outlined his idea for a body representing Indian interests in an open letter to graduates of the University of Calcutta. It aimed to obtain a greater share in government for educated Indians and to create a platform for civic and political dialogue between them and the British Raj. Hume took the initiative, and in March 1885 a notice convening the first meeting of the Indian National Union to be held in Poona the following December was issued. However due to a cholera outbreak there, it was moved to Bombay.
Answer: The Indian National Congress conducted its first session in Bombay from 28 to 31 December 1885.
Question: Why should we ban artificial sweeteners like aspartame?
Answer: Artificial sweeteners like aspartame are incredibly dangerous to our health. There have been countless studies showing the damaging effects it can wreak on a person's health. One of those is your gut health. As we know more and more about gut health, your gut is now being referred to as your "second brain". One could argue without a healthy gut, you cannot have good health. We know that artificial sweeteners like aspartame are damaging to our gut and our microbiome.
Question: List the types of cured pork that can be used to make carbonara. Separate them with a comma.
Evidence: Carbonara (Italian: [karboˈnaːra]) is a Roman pasta dish made with eggs, hard cheese, cured pork and black pepper. The dish took its modern form and name in the middle of the 20th century.

The cheese is usually Pecorino Romano, Parmigiano-Reggiano, or a combination of the two. Spaghetti is the most common pasta, but fettuccine, rigatoni, linguine, or bucatini are also used. Normally guanciale or pancetta are used for the meat component, but lardons of smoked bacon are a common substitute outside Italy.
Answer: Guanciale, pancetta, lardons, smoked bacon
Question: What are the main cat personality types?
Answer: Similar to humans, all cats have their unique quirks that make them special, but research has shown that they fall into 5 main personality types:
1 - The human cat likes to be around people, is a social butterfly. They prefer activities that they can do with their human overlords as opposed to being independent.
2 - The hunter cat, like its wild ancestors, likes to hunt for food, prey. They like to roam and explore and have a desire to be outdoors.
3 - The cat's cat likes to cuddle with other cats. This type of personality typically comes from cats who are raised with other cats.
4 - The cantankerous cat likes to be left alone. They tend to be grumpy when their habits are disrupted. In short, they have sensitive needs.
5 - The inquisitive cat is always curious and getting into mischief. They like to flex their investigate skills by looking in boxes, exploring new areas, and playing with puzzles. These cats enjoy meeting new friends and don't necessarily like to be left alone.
Overall, your cat's personality type will depend on a mix of factors including their upbringing, development, and genetics.
Question: Give me a bulleted list of the 5 top-grossing movies of all times.
Answer: 1 - Avatar
2 - Avengers: Endgame
3 - Avatar: The Way of Water
4 - Titanic
5 - Star Wars: Episode VII - The Force Awakens
Question: What is the focus of the method of Ebrahimi et al.?
Evidence: Several papers address adversarial attacks on NLP systems. Changes to text, whether word- or character-level, are all perceptible, raising some questions about what should rightly be considered an adversarial example BIBREF8 , BIBREF9 . BIBREF10 address the reading comprehension task, showing that by appending distractor sentences to the end of stories from the SQuAD dataset BIBREF11 , they could cause models to output incorrect answers. Inspired by this work, BIBREF12 demonstrate an attack that breaks entailment systems by replacing a single word with either a synonym or its hypernym. Recently, BIBREF13 investigated the problem of producing natural-seeming adversarial examples, noting that adversarial examples in NLP are often ungrammatical BIBREF14 .
In related work on character-level attacks, BIBREF8 , BIBREF15 explored gradient-based methods to generate string edits to fool classification and translation systems, respectively. While their focus is on efficient methods for generating adversaries, ours is on improving the worst case adversarial performance. Similarly, BIBREF9 studied how synthetic and natural noise affects character-level machine translation. They considered structure invariant representations and adversarial training as defenses against such noise. Here, we show that an auxiliary word recognition model, which can be trained on unlabeled data, provides a strong defense.
Spelling correction BIBREF16 is often viewed as a sub-task of grammatical error correction BIBREF17 , BIBREF18 . Classic methods rely on a source language model and a noisy channel model to find the most likely correction for a given word BIBREF19 , BIBREF20 . Recently, neural techniques have been applied to the task BIBREF7 , BIBREF21 , which model the context and orthography of the input together. Our work extends the ScRNN model of BIBREF7 .
In related work on character-level attacks, Ebrahimi et al. (2018b,a) explored gradient-based methods to generate string edits to fool classification and translation systems, respectively. While their focus is on efficient methods for generating adversaries, ours is on improving the worst case adversarial performance.
Answer: Efficient methods for generating adversaries.
Question: Identify which instrument is string or percussion: Ghatam, Monochord
Answer: Monochord is string, Ghatam is percussion.
Question: Identify which car manufacturer is British or American: Mini, RAM
Answer: RAM is American, Mini is British
Question: Name some of the bravolebrities from Summer house
Answer: Amanda Batula, Andrea Denver, Carl Radke, Ciara Miller, Danielle Olivera, Kyle Cooke, Lindsay Hubbard, Luke Gulbranson, Mya Allen and Paige DeSorbo
Question: What was Meghan Markle's royal title?
Answer: Meghan Markle's royal title is Duchess of Sussex.
Question: What is efficient market hypothesis?
Answer: Efficient market hypothesis postulates that markets converge on a fair price with all available information being generally available to the market.  Thus it is not logical that any one person can beat the market, if all information is generally available.
Question: What is Central Park?
Evidence: Central Park is an urban park in New York City located between the Upper West and Upper East Sides of Manhattan. It is the fifth-largest park in the city, covering 843 acres (341 ha). It is the most visited urban park in the United States, with an estimated 42 million visitors annually as of 2016, and is the most filmed location in the world.
Answer: Central Park is an urban park located in the borough of Manhattan, New York City, NY.  It is located between the Upper West Side and the Upper East Side.  It is 843 acres and is actually the 5th largest park in the city.  It is the most visited urban park in the United States, with an estimated 42 million visitors annually.