DataHammer/scimrc huggingface dataset:
Question: Of these senior crew members of the HMS Bounty, which turned out to be mutineers and which were loyalists:  William Bligh, John Fryer, Fletcher Christian, William Cole, William Peckover, William Purcell, William Elphinstone, Thomas Ledward, John Hallett, Thomas Hayward
Answer: William Bligh - Loyalist
John Fryer - Loyalist
Fletcher Christian - Mutineer
William Cole - Loyalist
William Peckover - Loyalist
William Purcell - Loyalist
William Elphinstone - Loyalist
Thomas Ledward - Loyalist
John Hallett - Loyalist
Thomas Hayward - Loyalist
Question: Of the items, tomato, broccoli, plane, sausage, pineapple, grass, honey, and milk would a human not like to consume?
Answer: A human would prefer not to eat a plane or grass.
Question: What is Lady Gaga's real name?
Evidence: Stefani Joanne Angelina Germanotta (/ˈstɛfəni ˌdʒɜːrməˈnɒtə/ (listen) STEF-ən-ee JUR-mə-NOT-ə; born March 28, 1986), known professionally as Lady Gaga, is an American singer, songwriter, and actress. She is known for her image reinventions and musical versatility. Gaga began performing as a teenager, singing at open mic nights and acting in school plays. She studied at Collaborative Arts Project 21, through the New York University Tisch School of the Arts, before dropping out to pursue a career in music.
Answer: Lady Gaga's real name is Stefani Joanne Angelina Germanotta.
Question: What is the novel aspect of the corpus in this paper?
Evidence: We create a new corpus which is, to the best of our knowledge, the largest corpus that has been annotated within the argumentation mining field to date. We choose several target domains from educational controversies, such as homeschooling, single-sex education, or mainstreaming. A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments to articles, discussion forum posts, blog posts, as well as professional newswire articles.
Since the data come from a variety of sources and no assumptions about its actual content with respect to argumentation can be drawn, we conduct two extensive annotation studies. In the first study, we tackle the problem of relatively high “noise” in the retrieved data. In particular, not all of the documents are related to the given topics in a way that makes them candidates for further deep analysis of argumentation (this study results into 990 annotated documents). In the second study, we discuss the selection of an appropriate argumentation model based on evidence in argumentation research and propose a model that is suitable for analyzing micro-level argumention in user-generated Web content. Using this model, we annotate 340 documents (approx. 90,000 tokens), reaching a substantial inter-annotator agreement. We provide a hand-analysis of all the phenomena typical to argumentation that are prevalent in our data. These findings may also serve as empirical evidence to issues that are on the spot of current argumentation research.
From the computational perspective, we experiment on the annotated data using various machine learning methods in order to extract argument structure from documents. We propose several novel feature sets and identify configurations that run best in in-domain and cross-domain scenarios. To foster research in the community, we provide the annotated data as well as all the experimental software under free license.
The rest of the article is structured as follows. First, we provide an essential background in argumentation theory in section SECREF2 . Section SECREF3 surveys related work in several areas. Then we introduce the dataset and two annotation studies in section SECREF4 . Section SECREF5 presents our experimental work and discusses the results and errors and section SECREF6 concludes this article.
A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments on articles, discussion forum posts, blog posts, as well as professional newswire articles.
Answer: A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments on articles, discussion forum posts, blog posts, as well as professional newswire articles.
Question: What are the tennis ball made of?
Evidence: Tennis balls were originally made of cloth strips stitched together with thread and stuffed with feathers. Modern tennis balls are made of hollow vulcanized rubber with a felt coating. Traditionally white, the predominant colour was gradually changed to optic yellow in the latter part of the 20th century to allow for improved visibility. Tennis balls must conform to certain criteria for size, weight, deformation, and bounce to be approved for regulation play. The International Tennis Federation (ITF) defines the official diameter as 65.41–68.58 mm (2.575–2.700 in). Balls must weigh between 56.0 and 59.4 g (1.98 and 2.10 oz).
Answer: Nowadays most tennis balls are made of hollow vulcanized rubber with a felt coating.
Question: Imagine you are the last person on Earth. Write a diary entry describing your thoughts and feelings.
Answer: Dear diary, you have been a faithful companion to me, all this while, the only true friend I have. Or, may, I say, the only witness to my thoughts - because there is no one else. Yes, I am the only person around in this world. Does this make me happy or sad, you ask? I am conflicted, to be honest. As an introvert, I always wanted solitude and peace. The ability to be myself, to not just think my own thoughts but act like no one was watching. And yet, now, when I really can do this (believe me, no one is watching me, or even the security tapes!), I long for a voice to hear and for someone to talk back to me, see me act stupid or act out my weirdest thoughts, and in general just listen to me. But, there is no one. And this scares me, as well. Why am I writing this down, when I rarely, if ever, read you back? It is, probably, with a hidden hope that someone some day will read it and understand my thoughts and ideas without judgement. If no one ever will read it, because there is no human left on planet Earth, why am I writing this even? I always read (and scoffed at) the books telling me "Man is a social animal" - I felt they did not refer or apply to me me. And now, dear friend, I have realised a little too late in life that it wasn't the books that did not recognise me. Rather, it was I who didn't understand myself. Farewell and adieu!
Question: Can you give me some background on Perez v. Campbell
Evidence: Perez v. Campbell, 402 U.S. 637 (1971), was a case in which the Supreme Court of the United States held that Arizona's law suspending a driver's license was unconstitutional due to its conflict with the federal Bankruptcy Act under the Supremacy Clause of the Constitution.

Background
The plaintiffs, Mr. Adolfo and Mrs. Emma Perez, were licensed uninsured motorists in the state of Arizona. Insurance was not required under Arizona law. Mr. Perez was involved in a traffic accident and, unable to pay the associated costs, soon filed for bankruptcy. Arizona, under its financial responsibility laws, withdrew the Perez' licenses, and the couple filed suit in district court, where their claim was denied. The Perez family's appeal to the United States Court of Appeals for the Ninth Circuit was denied, and they subsequently appealed to the United States Supreme Court, which granted certiorari.
Answer: Perez v. Campbell, 402 U.S. 637 (1971), was a case in which the Supreme Court of the United States held that Arizona's law suspending a driver's license was unconstitutional due to its conflict with the federal Bankruptcy Act under the Supremacy Clause of the Constitution.

The plaintiffs, Mr. Adolfo and Mrs. Emma Perez, were licensed uninsured motorists in the state of Arizona. Insurance was not required under Arizona law. Mr. Perez was involved in a traffic accident and, unable to pay the associated costs, soon filed for bankruptcy. Arizona, under its financial responsibility laws, withdrew the Perez' licenses, and the couple filed suit in district court, where their claim was denied. The Perez family's appeal to the United States Court of Appeals for the Ninth Circuit was denied, and they subsequently appealed to the United States Supreme Court, which granted certiorari.
Question: What did the intrinsic analysis include?
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 ).
We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section 2 ).
Answer: Contextual similarities, question types, and answer categories
Question: Write a letter from the perspective of a potential home buyer to a home owner about wanting to buy the home.
Answer: Dear home owner, 

You have a beautiful home and as a home buyer, I am very interested in purchasing this home from you. I have been on the market to buy a home for a long time, but until now, I have not found a home that fits my criteria. Your home meets everything I am looking for and I hope you will accept my offer.
Question: Change the following to past tense
Evidence: Yoo Ji-No is a South Korean football player who plays for Suwon FC.
Answer: Yoo Ji-No was a South Korean football player who played for Suwon FC.
Question: Classify each of the following as vertical or horizontal: balance beam, light pole, tree, barbell, floor, wall
Answer: Balance Beam: Horizontal
Light Pole: Vertical
Tree: Vertical
Barbell: Horizontal
Floor: Horizontal
Wall: Vertical
Question: Give me a list of actors who won an Oscar in the last 5 years.
Answer: 1. Brendan Fraser, 2022
2. Will Smith, 2021
3. Anthony Hopkins, 2020
4. Joaquin Phoenix, 2019
5. Rami Malek, 2018
Question: Are the annotators qualified for the task？
Evidence: We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above. Initially, we asked four annotators to test our guidelines on two texts. Subsequently, identified issues were discussed and resolved. Following this pilot annotation phase, we asked two different annotators to annotate two case reports according to our guidelines. The same annotators annotated an overall collection of 53 case reports.
Inter-annotator agreement is calculated based on two case reports. We reach a Cohen's kappa BIBREF6 of 0.68. Disagreements mainly appear for findings that are rather unspecific such as She no longer eats out with friends which can be seen as a finding referring to “avoidance behaviour”.
We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above.
Answer: Yes, they asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities.
Question: Classify the following ten cloud types by altitude :
Cirrus, Cumulus, Stratus, Stratocumulus, Cumulonimbus, NimboStratus, Cirrocumulus, Cirrostratus, Altostratus, Altocumulus,
Answer: These cloud types are generally classified in three different altitude groups.

The lowest hanging clouds are within 2km distance and cover the four types listed here - Stratus, Cumulus, Cumulonimbus, and Stratocumulus.

The middle altitude clouds are within 2 to 7 km distance. The three cloud types in this category are Nimbostratus, Altocumulus, and Altostratus.

Finally, the highest altitude clouds can be as high as 13 kms. These include Cirrus,  Cirrocumulus, Cirrostratus.

Each of these cloud types have further cloud species based on the formation and appearance.
Question: How accurate are future weather forecasts?
Answer: Weather forecasts are typically accurate for up to 7 days in the future. Forecasts decrease in accuracy the further away you get from the day you're looking, due to the increased variability in key indicators which comprise the models used to build the forecasts.
Question: What did the experiment show?
Evidence: In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating. The process consists of two stages. In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page. In the second stage, article–section placement, we determine the best fitting section in an entity page. Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes. This allows us to add missing sections to entity pages. We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes. For the first stage, we achieve an overall performance with P=0.93, R=0.514 and F1=0.676, outperforming our baseline competitors significantly. For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates. The overall performance across different classes is P=0.844, R=0.885 and F1=0.860.
In the future, we will enhance our work by extracting facts from the suggested news articles. Results suggest that the news content cited in entity pages comes from the first paragraphs. However, challenging task such as the canonicalization and chronological ordering of facts, still remain.
Results suggest that the news content cited in entity pages comes from the first paragraphs.
Answer: Results suggest that the news content cited in entity pages comes from the first paragraphs.
Question: I'm a vegan and traveling to new york city next month, can you suggest some vegan restaurants that i can eat at during my trip?
Answer: There are a lot of vegan restaurant options in Manhattan! Some highly rated suggestions for you:
1. Jajaja Mexicana for some good vegan mexican food
2. Planta Queen - for vegan food options
3. AbcV - vegan spot for seasonal plates and cocktails
4. Ras plant based - for plant based ethiopian cuisine
5. Bar Verde - for vegan mexican options
6. May Kaidee  - for vegan thai options
7. Lady Bird - for creative vegan small plates and cocktails
Question: Is endosymbiosis bad?
Evidence: Endosymbiosis is any symbiotic relationship in which one symbiont lives within the tissues of the other, either within the cells or extracellularly. Examples include diverse microbiomes: rhizobia, nitrogen-fixing bacteria that live in root nodules on legume roots; actinomycetes, nitrogen-fixing bacteria such as Frankia, which live in alder root nodules; single-celled algae inside reef-building corals; and bacterial endosymbionts that provide essential nutrients to about 10%–15% of insects.[citation needed]

In endosymbiosis, the host cell lacks some of the nutrients which the endosymbiont provides. As a result, the host favors endosymbiont's growth processes within itself by producing some specialized cells. These cells affect the genetic composition of the host in order to regulate the increasing population of the endosymbionts and ensure that these genetic changes are passed onto the offspring via vertical transmission (heredity).
Answer: No, endosymbiosis is a symbiotic relationship where the internal symbiont or the endosymbiont provides essential nutrients to the host. For example, a bacteria can provide necessary nutrients to an insect.
Question: Extract some information about photograph The Last Joke from given text
Evidence: The Last Joke, Bellagio, also known as The Good Joke, is a black and white photograph taken by Alfred Stieglitz during a Summer travel to Italy in 1887. He was then living and studying in Germany and decided to travel through several cities of Italy at the Summer.
Answer: 1. Alfred Stieglitz captured The Last Joke, Bellagio, also known as The Good Joke, in a black-and-white shot while on a summer trip to Italy in 1887.
2. He made the decision to visit various Italian cities during the summer when he was then residing and attending school in Germany.
Question: Given a reference text about Janne Mortil, tell me what she is most known for.
Evidence: Janne Mortil (born December 14, 1967) is a Vancouver-based Canadian-American actress probably best known for playing Madeleine Astor in Titanic (1996) and Michelle Dupont in the television series Side Effects, for which she was nominated for a Gemini, and Detective Tricia Kelsey in Street Justice (1991–1993).

Career
Janne Mortil began her career with a 1973 appearance in The Beachcombers at the age of five. She was a regular in the 1970s soap opera House of Pride. She performed on stage in the 1979 Vancouver Playhouse Theatre Company's production of The Innocents and appeared in 1980 in Huckleberry Finn and His Friends television series.

Her movie roles include Sally Moffat in Little Women (1994). In 1996, she played Madeleine Astor in the television movie Titanic, opposite Catherine Zeta-Jones. In 2003, she played Sergeant Kibble in Hitcher 2.

Her television credits include 21 Jump Street, The X-Files, Poltergeist: The Legacy and Cold Squad. In 1992 she provided narration for the CBC documentary mini-series The Valour and the Horror. She played also Detective Tricia Kelsey on Street Justice (1991-1992) and Michelle Dupont, the clinic's receptionist in Side Effects (1994-1996). For the latter role, she was nominated for a 1996 Gemini Award for Best Performance by an Actress in a Supporting Role in a Dramatic Series.
Answer: Janne Mortil is likely best known for her roles as Madeleine Astor in the movie "Titanic", Michelle Dupont in the TV show "Side Effects", and Detective Tricia Kelsey in "Street Justice".
Question: What document context was added?
Evidence: We extracted 200 sentence pairs from BIBREF3 's dataset and provided each pair with a document context consisting of a preceding and a following sentence, as in the following example.
We extracted 200 sentence pairs from BIBREF3 's dataset and provided each pair with a document context consisting of a preceding and a following sentence, as in the following example.
Answer: Preceding and following sentence of each metaphor and paraphrase are added as document context
Question: What is the size of their dataset?
Evidence: FLOAT SELECTED: Table 2: Dataset statistics.
FLOAT SELECTED: Table 2: Dataset statistics.
Answer: 10,001 utterances
Question: What is the name of William Gibson's first comic book series?
Evidence: Archangel, also written as William Gibson Archangel or William Gibson's Archangel, is a five-issue limited series comic book that was created by William Gibson and Michael St. John Smith, written by William Gibson, illustrated by Butch Guice and story-edited by Michael Benedetto. It is Gibson's first comic book series, which is set in an alternative version of 2016 in which the Vice President of America travels back in time to 1945 to secure power.

IDW Publishing first released Archangel from May 2016 to August 2017, then in October as a hardcover graphic novel. It was nominated for an Eisner Award for Best Limited Series and was adapted into an audio play that was released by Audible.
Answer: Archangel
Question: Why are the word embeddings not frozen？
Evidence: We have used Word2Vec BIBREF16, GloVe BIBREF17 and FastText BIBREF18 word vectors of 300 dimensions. These vectors were trained on the corpus obtained from Nepali National Corpus. This pre-lemmatized corpus consists of 14 million words from books, web-texts and news papers. This corpus was mixed with the texts from the dataset before training CBOW and skip-gram version of word2vec using gensim libraryBIBREF24. This trained model consists of vectors for 72782 unique words.
Light pre-processing was performed on the corpus before training it. For example, invalid characters or characters other than Devanagari were removed but punctuation and numbers were not removed. We set the window context at 10 and the rare words whose count is below 5 are dropped. These word embeddings were not frozen during the training session because fine-tuning word embedding help achieve better performance compared to frozen oneBIBREF20.
We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table TABREF25. We have trained the word embedding in such a way that the sub-word size remains between 1 and 4. We particularly chose this size because in Nepali language a single letter can also be a word, for example e, t, C, r, l, n, u and a single character (grapheme) or sub-word can be formed after mixture of dependent vowel signs with consonant letters for example, C + O + = CO, here three different consonant letters form a single sub-word.
The two-dimensional visualization of an example word npAl is shown in FIGREF14. Principal Component Analysis (PCA) technique was used to generate this visualization which helps use to analyze the nearest neighbor words of a given sample word. 84 and 104 nearest neighbors were observed using word2vec and fasttext embedding respectively on the same corpus.
These word embeddings were not frozen during the training session because fine-tuning word embedding help achieve better performance compared to frozen one(Chiu and Nichols, 2015).
Answer: Because fine-tuning word embedding help achieve better performance compared to frozen one.