DataHammer/scimrc huggingface dataset:
Question: In what aspect does the team's work superior to earlier research in predicting discourse relations?
Evidence: Since the PDTB BIBREF7 corpus was created, a surge of studies BIBREF8 , BIBREF3 , BIBREF9 , BIBREF10 have been conducted for predicting discourse relations, primarily focusing on the challenging task of implicit discourse relation classification when no explicit discourse connective phrase was presented. Early studies BIBREF11 , BIBREF3 , BIBREF2 , BIBREF12 focused on extracting linguistic and semantic features from two discourse units. Recent research BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 tried to model compositional meanings of two discourse units by exploiting interactions between words in two units with more and more complicated neural network models, including the ones using neural tensor BIBREF5 , BIBREF17 , BIBREF18 and attention mechanisms BIBREF6 , BIBREF19 , BIBREF20 . Another trend is to alleviate the shortage of annotated data by leveraging related external data, such as explicit discourse relations in PDTB BIBREF9 , BIBREF19 , BIBREF21 and unlabeled data obtained elsewhere BIBREF12 , BIBREF19 , often in a multi-task joint learning framework.
However, nearly all the previous works assume that a pair of discourse units is independent from its wider paragraph-level contexts and build their discourse relation prediction models based on only two relevant discourse units. In contrast, we model inter-dependencies of discourse units in a paragraph when building discourse unit representations; in addition, we model global continuity and patterns in a sequence of discourse relations, including both implicit and explicit relations.
Hierarchical neural network models BIBREF22 , BIBREF23 have been applied to RST-style discourse parsing BIBREF24 mainly for the purpose of generating text-level hierarchical discourse structures. In contrast, we use hierarchical neural network models to build context-aware sentence representations in order to improve implicit discourse relation prediction.
In contrast, we model inter-dependencies of discourse units in a paragraph when building discourse unit representations; in addition, we model global continuity and patterns in a sequence of discourse relations, including both implicit and explicit relations.
Answer: They model inter-dependencies of discourse units in a paragraph when building discourse unit representations; in addition, they model global continuity and patterns in a sequence of discourse relations, including both implicit and explicit relations.
Question: Finish the sentence from the book 1984: Big ____ is watching you!
Answer: The complete sentence is: Big Brother is watching you!
Question: Who are the members of the band Phish.
Answer: The band consists of guitarist Trey Anastasio, bassist Mike Gordon, drummer Jon Fishman, and keyboardist Page McConnell
Question: What film is recognized as the first film in the world with an entirely black cast?
Evidence: William D. Foster, sometimes referred to as Bill Foster (1884 – 15 April 1940), was a pioneering African-American film producer who was an influential figure in the Black film industry in the early 20th century, along with others such as Oscar Micheaux. He was the first African American to found a film production company, establishing the Foster Photoplay Company in Chicago in 1910. Foster had a vision for the African-American community to portray themselves as they wanted to be seen, not as someone else depicted them. He was influenced by the black theater community and wanted to break the racial stereotyping of blacks in film. He was an actor and writer under the stage name Juli Jones, as well as an agent for numerous vaudeville stars. His film The Railroad Porter, released in 1912, is credited as being the world's first film with an entirely black cast and director. The film is also credited with being the first black newsreel, featuring images of a YMCA parade. Foster's company produced four films that were silent shorts.
Answer: The Railroad Porter, produced by Bill Foster in 1912, is recognized as the first film in the world with a fully black cast and director.
Question: Given a reference text about multiple myeloma, tell me the risk factors and the cause of the disease.
Evidence: Multiple myeloma (MM), also known as plasma cell myeloma and simply myeloma, is a cancer of plasma cells, a type of white blood cell that normally produces antibodies. Often, no symptoms are noticed initially. As it progresses, bone pain, anemia, kidney dysfunction, and infections may occur. Complications may include amyloidosis.

The cause of multiple myeloma is unknown. Risk factors include obesity, radiation exposure, family history, and certain chemicals. There is an increased risk of multiple myeloma in certain occupations. This is due to the occupational exposure to aromatic hydrocarbon solvents having a role in causation of multiple myeloma. Multiple myeloma may develop from monoclonal gammopathy of undetermined significance that progresses to smoldering myeloma. The abnormal plasma cells produce abnormal antibodies, which can cause kidney problems and overly thick blood. The plasma cells can also form a mass in the bone marrow or soft tissue. When one tumor is present, it is called a plasmacytoma; more than one is called multiple myeloma. Multiple myeloma is diagnosed based on blood or urine tests finding abnormal antibodies, bone marrow biopsy finding cancerous plasma cells, and medical imaging finding bone lesions. Another common finding is high blood calcium levels.
Answer: Although the cause of multiple myeloma is not known, risk factors for developing the disease are obesity, radiation exposure, family history, and exposure to certain chemicals.
Question: How many members are there in Shanghai Cooperation Organisation (SCO)
Answer: Shanghai Cooperation Organisation (SCO) has 8 members
Question: Why is professional wrestling entertaining?
Answer: Professional wrestling is one of the best forms of entertainment.  First, it's an incredible display of physical talent.  Professional wrestling and acrobats are showcasing their physical attributes in a partially choregraphed dance.  There is an element of improv in professional wrestling.  Wrestlers often are calling the match as the action happens.  The next set of moves can be greatly influenced from the crowd's reaction.  The "pop" of the crowd, if you will.  Finally, the best wrestlers are ones that are amplified versions of their true selves.  The audience can detect an inauthentic speech on the mic.  Those wrestlers who combine their charisma to their true gimmick are the ones that turn the normal fan to a mark.
Question: Write about something you hate.
Answer: I hate motorized scooters. I live in London and over the last few years, they have become extremely popular for people getting around the city. Most people are okay on them, but there are far too many who will go too fast, and ride on the pavement. I've been crashed into a few times and I have read news reports of people being seriously injured and killed by them. 

The rental scooters can often be left abandoned on the pavement or in parking spots, creating an eyesore and a hazard. They should be regulated better!
Question: What datasets did they use?
Evidence: The method we propose to measure the controversy equates in accuracy the one developed by Garimella et al.BIBREF23 and improves considerably computing time and robustness wrt the amount of data needed to effectively apply it. Our method is also based on a graph approach but it has its main focus on the vocabulary. We first train an NLP classifier that estimates opinion polarity of main users, then we run label-propagation BIBREF31 on the endorsement graph to get polarity of the whole network. Finally we compute the controversy score through a computation inspired in Dipole Moment, a measure used in physics to estimate electric polarity on a system. In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4).
In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.
In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4).
We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.
Answer: The answers are shown as follows:
* BIBREF32, BIBREF23, BIBREF33
* discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia.
Question: Is Lake Tahoe a beautiful place for vacation/cabin rentals to immerse into nature?
Answer: Lake Tahoe is one of the largest Alpine lake in United States. The meaning of 'Alpine' is nestled high in the Mountains (Sierra Mountains here) surrounded with thousands of miles of forest, Wildlife and Mountain air. This unique fresh-water lake is also one of the most beautiful places to visit in the country, and every view of the lake is nothing less than breathtaking. Sitting directly on the California-Nevada border, Lake Tahoe is best known for its world-class ski resorts and casinos, but also offers a multitude of exciting, fun and relaxing things to experience, and unlimited places to explore during any time of the year.
This is one of the sought out places to visit year round.
Question: What are the limitations of existing recursive neural networks?
Evidence: Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags. Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM. With this, we can control the composition function of the existing word-level tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM. In extensive experiments, we show that models built upon the proposed architecture obtain superior or competitive performance on several sentence-level tasks such as sentiment analysis and natural language inference when compared against previous tree-structured models and other sophisticated neural models.
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.
Answer: Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.
Question: What is the assisting language in all experiments?
Evidence: Consider the task of translating from an extremely low-resource language (source) to a target language. The parallel corpus between the two languages if available may be too small to train a NMT model. Similar to existing works BIBREF3 , BIBREF10 , BIBREF12 , we use transfer learning to overcome data sparsity and train a NMT model between the source and the target languages. Specifically, the NMT model (parent model) is trained on the assisting language and target language pairs. We choose English as the assisting language in all our experiments. In our resource-scarce scenario, we have no parallel corpus for the child task. Hence, at test time, the source language sentence is translated using the parent model after performing a word-by-word translation into the assisting language.
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the contextual representations generated by the encoder for the two languages. In this paper, we propose to pre-order English sentences (assisting language sentences) to match the word-order of the source language and train the parent model on this pre-ordered corpus. In our experiments, we look at scenarios where the assisting language has SVO word order and the source language has SOV word order.
For instance, consider the English sentence Anurag will meet Thakur. One of the pre-ordering rule swaps the position of the noun phrase followed by a transitive verb with the transitive verb. The original and the resulting re-ordered parse tree will be as shown in the Table TABREF5 . Applying this reordering rule to the above sentence Anurag will meet Thakur will yield the reordered sentence Anurag Thakur will meet. Additionally, the Table TABREF5 shows the parse trees for the above sentence with and without pre-ordering.
Pre-ordering should also be beneficial for other word order divergence scenarios (e.g., SOV to SVO), but we leave verification of these additional scenarios for future work.
We choose English as the assisting language in all our experiments.
Answer: English.
Question: What category do these items belong to:
- Apple
- Pear
- Banana
- Orange
- Pineapple
- Guava

The possible categories are: Fruits, Vegetables, Minerals.
Answer: These items are all fruits. All of the items listed develop from flowers after they wilt and contain seeds. They are not vegetables because they are not stems, roots or tubers. They are not minerals because they are living things.
Question: How was the universe created?
Evidence: The Big Bang event is a physical theory that describes how the universe expanded from an initial state of high density and temperature. Various cosmological models of the Big Bang explain the evolution of the observable universe from the earliest known periods through its subsequent large-scale form. These models offer a comprehensive explanation for a broad range of observed phenomena, including the abundance of light elements, the cosmic microwave background (CMB) radiation, and large-scale structure. The overall uniformity of the Universe, known as the flatness problem, is explained through cosmic inflation: a sudden and very rapid expansion of space during the earliest moments. However, physics currently lacks a widely accepted theory of quantum gravity that can successfully model the earliest conditions of the Big Bang.
Answer: We can't know for sure but a popular theory called the Big Bang suggests that the universe was created from an infinitesimally small point of infinite energy some four and a half billion years ago.
Question: What example of a cancer could cause blood in the urine?
Evidence: Kidney cancer, also known as renal cancer, is a group of cancers that starts in the kidney. Symptoms may include blood in the urine, lump in the abdomen, or back pain. Fever, weight loss, and tiredness may also occur. Complications can include spread to the lungs or brain.
Answer: Kidney cancer, also known as renal cancer, is a group of cancers that starts in the kidney. Symptoms may include blood in the urine, lump in the abdomen, or back pain. Fever, weight loss, and tiredness may also occur. Complications can include spread to the lungs or brain.

The main types of kidney cancer are renal cell cancer (RCC), transitional cell cancer (TCC), and Wilms' tumor. RCC makes up approximately 80% of kidney cancers, and TCC accounts for most of the rest. Risk factors for RCC and TCC include smoking, certain pain medications, previous bladder cancer, being overweight, high blood pressure, certain chemicals, and a family history. Risk factors for Wilms' tumor include a family history and certain genetic disorders such as WAGR syndrome. Diagnosis may be suspected based on symptoms, urine testing, and medical imaging. It is confirmed by tissue biopsy.

Treatment may include surgery, radiation therapy, chemotherapy, immunotherapy, and targeted therapy. Kidney cancer newly affected about 403,300 people and resulted in 175,000 deaths globally in 2018. Onset is usually after the age of 45. Males are affected more often than females. The overall five-year survival rate is 75% in the United States, 71% in Canada, 70% in China, and 60% in Europe. For cancers that are confined to the kidney, the five-year survival rate is 93%, if it has spread to the surrounding lymph nodes it is 70%, and if it has spread widely, it is 12%. Kidney cancer has been identified as the 13th most common form of cancer, and is responsible for 2% of the world's cancer cases and deaths. The incidence of kidney cancer has continued to increase since 1930. Renal cancer is more commonly found in populations of urban areas than rural areas.
Question: Who are the  most referred to Devas in the Rigveda as per the passage?
Evidence: In Vedic literature, Devas and Devis represent the forces of nature and some represent moral values (such as the Adityas, Varuna, and Mitra), each symbolizing the epitome of a specialized knowledge, creative energy, exalted and magical powers (Siddhis).
Vedic era deities evolved over time. Rudra (left) is represented in Vedic literature, is shown as Shiva-Rudra 2nd-century sculpture (middle), and as Shiva (meaning kind) in 13th-century art work (right). The iconography evolved, retaining some symbolic elements such as trident, axe or antelope.The most referred to Devas in the Rigveda are Indra, Agni (fire) and Soma, with "fire deity" called the friend of all humanity, it and Soma being the two celebrated in a yajna fire ritual that marks major Hindu ceremonies. Savitr, Vishnu, Rudra (later given the exclusive epithet of Shiva), and Prajapati (later Brahma) are gods and hence Devas.The Vedas describes a number of significant Devis such as Ushas (dawn), Prithvi (earth), Aditi (cosmic moral order), Saraswati (river, knowledge), Vāc (sound), Nirṛti (destruction), Ratri (night), Aranyani (forest), and bounty goddesses such as Dinsana, Raka, Puramdhi, Parendi, Bharati, Mahi among others are mentioned in the Rigveda. Sri, also called Lakshmi, appears in late Vedic texts dated to be pre-Buddhist, but verses dedicated to her do not suggest that her characteristics were fully developed in the Vedic era. All gods and goddesses are distinguished in the Vedic times, but in the post-Vedic texts (~500 BCE to 200 CE), and particularly in the early medieval era literature, they are ultimately seen as aspects or manifestations of one Brahman, the Supreme power.
Answer: Indra, Agni (fire) and Soma
Question: What is the size of the labelled dataset?
Evidence: We collected a total of 96,566 tweets from TwiNL, which we filtered in a number of ways. First, retweets were removed, as we wanted to focus on unique messages. This led to a removal of 31% of the messages. Second, we filtered out messages that contain a URL. Such messages often share a news headline and include a URL to refer to the complete news message. As a news headline does not reflect the stance of the person who posted the tweet, we decided to apply this filtering step. It is likely that part of the messages with a URL do include a message composed by the sender itself, but this step helps to clean many unwanted messages. Third, we removed messages that include a word related to animals and traveling (`dier’, animal; `landbouw’, agriculture; and `teek’, tick), as we strictly focus on messages that refer to vaccination that is part of the governmental vaccination program. 27,534 messages were left after filtering. This is the data set that is used for experimentation.
27,534 messages were left after filtering. This is the data set that is used for experimentation.
Answer: The answers are shown as follows:
* 27,534 messages
Question: What is the TSA church?
Evidence: The Salvation Army (TSA) is a Protestant Christian church and an international charitable organization headquartered in London, England that is aligned with the Wesleyan-Holiness movement. The organisation reports a worldwide membership of over 1.7 million, comprising soldiers, officers and adherents collectively known as Salvationists. Its founders sought to bring salvation to the poor, destitute, and hungry by meeting both their "physical and spiritual needs". It is present in 133 countries, running charity shops, operating shelters for the homeless and disaster relief, and humanitarian aid to developing countries.
Answer: The TSA church typically stands for The Salvation Army (TSA) and it is a Protestant Christian church, as well as an international charitable organization with head quarters in England.
Question: Create a dialogue between two characters discussing the impact of social media on their lives. Your dialogue should explore both the positive and negative effects of social media on society.
Answer: Glen: "Man, I've been feeling terrible lately. Just the worst anxiety. I've been spending too much time on social media."

Rashid: "I hear ya. It can really mess with your mood. I find it helps if I participate less and just, well, consume content. Curate it, too. I take some time to make sure I don't get all the political garbage or downer stuff."

Glen: "I can't do that. I get fired up too easily, man. I'm just reading, like, an article on cats and then someone in the comments goes off about how cat owners are stupid, and I reply, and then my night's ruined."

Rashid: "Don't ever read the comments!" <laughs>

Glen: "I know, man; I know! But I'm not talking about just that. I get on Twitter and I just see all this, I don't know, anger and trolling and negativity. And it gets to me. I start to think everything's worse than it is."

Rashid: "That's why you really need to use the tools the sites provide to aggressively curate what you see. Block people and brands. Get rid of annoying friends. Choose and refine categories for subjects you participate in. You have to tell the algorithm what you don't want. It's not perfect, but these days, I really only see family stuff, cats, and exotic car stuff on Facebook. Same for Pinterest and Instagram."

Glen: "Even then, it feels like the internet just wants to trick me all the time. Ads, appeals for money, grifts of all sorts, email scams. Like this is some seriously extractive capitalism nonsense, and that makes me tired, too. But I can't seem to stop engaging with it!"

Rashid: "Then limit your time to what you can  actually manage. Set timers, if you have to. Focus on the apps you have more control over, more ability to curate effectively. You gotta make the internet work for you now. Find communities but also be skeptical and suss them out before you dive into participating. And just be streetwise. If you think something's a scam, it probably is.  Walk away."

Glen: "I thought you'd tell me to go outside!"

Rashid: "Well, I was gonna get to that..."
Question: How many maharatna companies are there in India?
Answer: 11(eleven), the 11th is  Power Finance Corporation