DataHammer/scimrc huggingface dataset:
Question: How is correctness of automatic derivation proved?
Evidence: In this section, we empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) in ROOT. We show that AD can drastically improve accuracy and performance of derivative evaluation, compared to ND.
In this section, we empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) in ROOT. We show that AD can drastically improve accuracy and performance of derivative evaluation, compared to ND.
Answer: The answers are shown as follows:
* empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method)
Question: write a short essay on day spent in central park
Answer: Central Park is one of the most iconic landmarks in New York City, and a popular destination for locals and tourists alike. Spending a day in Central Park is an unforgettable experience, filled with beauty, excitement, and relaxation.

The park offers a wide range of activities and attractions, from boating on the lake and exploring the many walking paths, to visiting the Central Park Zoo and the famous Alice in Wonderland statue. There are also numerous opportunities for outdoor sports and recreation, including baseball fields, basketball courts, and running trails.

One of the most popular activities in Central Park is picnicking. Visitors can spread out a blanket on the grass and enjoy a meal with friends or family while taking in the stunning views of the park's natural beauty. There are also plenty of food vendors and cafes throughout the park, offering everything from hot dogs and ice cream to gourmet sandwiches and salads.

Central Park is also home to many cultural events and performances throughout the year. From concerts and plays to art exhibits and film screenings, there is always something exciting happening in the park. Visitors can check the park's schedule to find out about upcoming events and plan their visit accordingly.

Whether you're looking for a relaxing day in nature or an action-packed adventure, a day spent in Central Park has something for everyone. The park's beauty and charm make it a must-visit destination for anyone visiting New York City, and a favorite spot for locals to escape the hustle and bustle of city life.
Question: Can you tell me what of these are capital cities of their respective countries? Manchester, Madrid, Paris, Milan, London and Munich
Answer: A capital city is the city where the government of the country resides. From the previous lists only Madrid, Paris and London are capital cities. Manchester, Milan and Munich are large and important cities in Europe but they are not their country's capital. Some cities like Milan might be consider their country's economic capital as they are the main economic hub of Italy but they are still not their capital city which is Rome.
Question: What are the baselines?
Evidence: With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
As baselines, we compare with feature spaces comprising of char n-grams [6], TF-IDF vectors, and Bag of Words vectors (BoWV).
Answer: Char n-grams, TF-IDF vectors, and Bag of Words vectors (BoWV).
Question: What data do their experiments base on?
Evidence: We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times. Any other multilingual data collection would work as well, but with the selected corpus we have the advantage that we cover the same genre and roughly the same coverage for each language involved. It is also easy to divide the data into training and test sets by using Bible verse numbers, which allows us to control for semantic similarity between languages in a way that would have been difficult in a corpus that is not multi-parallel. Altogether we have 1,303 translations in 990 languages that we can use for our purposes. These were chosen so that the model alphabet size is below 1000 symbols, which was satisfied by choosing only translations in Latin, Cyrillic or Greek script.
Certainly, there are disadvantages as well, such as the limited size (roughly 500 million tokens in total, with most languages having only one translation of the New Testament each, with roughly 200 thousand tokens), the narrow domain and the high overlap of named entities. The latter can lead to some unexpected effects when using nonsensical language vectors, as the model will then generate a sequence of random names.
The corpus deviates in some ways from an ideal multi-parallel corpus. Most translations are of the complete New Testament, whereas around 300 also contain the Old Testament (thus several times longer), and around ten contain only portions of the New Testament. Additionally, several languages have multiple translations, which are then concatenated. These translations may vary in age and style, but historical versions of languages (with their own ISO 639-3 code) are treated as distinct languages. During training we enforce a uniform distribution between languages when selecting training examples.
We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times.
Answer: A large collection of Bible translations crawled from the web.
Question: Name some celebrities who have been diagnosed with multiple sclerosis.
Answer: Jack Osbourne, Selma Blair, Jason DaSilva, Richard Pryor
Question: How do you subtract two numbers?
Answer: Subtraction is an operation that identifies the total count of objects in a collection of items after a specific count of items are removed from the starting total.

You can subtract two numbers by starting with an initial count, which represents the number to subtract from, and a second count which represents the number to subtract.  Then remove one from the starting count repeating this operation for the number of items in the subtract value.  

Subtraction is not associative, which means that the order in which subtraction is performed matters.  For instance, seven minus five (7 - 3) equals four (= 4) but four minus seven (4 - 7) equals minus three (= -3).
Question: What are some ways of traveling from Washington D.C to San Francisco?
Answer: - take a direct flight
- drive cross country on paved roads with a car
- drive cross country on dirt roads with a car
- ride cross country on dirt roads with a motorcycle
- ride cross country with a bicycle
- take a ship through the panama canal
- walk cross country
- take a bus
- take a train
- hitchhike 
- combine any of the following methods
Question: What data is the language model pretrained on?
Evidence: To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.
Answer: The answers are shown as follows:
* Chinese general corpus
Question: What's the difference between facts and truth?
Answer: Facts are pieces of information that are verifiable and based on empirical evidence whereas truth, although a related concept is related to a subjective perception or belief of what is a fact and does not always require evidence. An example would be a question about whether aliens exists. Many have a personal belief that aliens do exists, but since no empirical evidence exists upon which society can reach a consensus that they do truly exist. Someone may say that they are speaking truth when they say that aliens exists, however this is more likely just based on their belief which is unsupported by evidence (assuming in this example that the person making this statement has no empirical evidence). In this example someone who says they believe aliens do exist, believes they are speaking the truth but that does not make it a fact.
Question: Tell me whether each of these wines is red or white: Merlot, Pinot Noir, Pinot Gris, Malbec, Syrah, Riesling, Chardonnay
Answer: Merlot: Red
Pinot Noir: Red
Pinot Gris: White
Malbec: Red
Syrah: Red
Riesling: White
Chardonnay: White
Question: What corpus is used to conduct experiments?
Evidence: We conduct experiments on a simplified Chinese emotion cause corpus BIBREF31 , the only publicly available dataset on this task to the best of our knowledge. The corpus contains 2,105 documents from SINA city news. Each document has only one emotion word and one or more emotion causes. The documents are segmented into clauses manually. The main task is to identify which clause contains the emotion cause.

[id=lq]Details of the corpus are shown in Table 1. The metrics we used in evaluation follows lee2010text. It is commonly accepted so that we can compare our results with others. If a proposed emotion cause clause covers the annotated answer, the word sequence is considered correct. The precision, recall, and F-measure are defined by INLINEFORM0 
In the experiments, we randomly select 90% of the dataset as training data and 10% as testing data. In order to obtain statistically credible results, we evaluate our method and baseline methods 25 times with different train/test splits.
We conduct experiments on a simplified Chinese emotion cause corpus (Gui et al., 2016) ∗ , the only publicly available dataset on this task to the best of our knowledge.
Answer: A simplified Chinese emotion cause corpus.
Question: What subset of GLUE tasks is used?
Evidence: We use the following subset of GLUE tasks BIBREF4 for fine-tuning:
MRPC: the Microsoft Research Paraphrase Corpus BIBREF13
STS-B: the Semantic Textual Similarity Benchmark BIBREF14
SST-2: the Stanford Sentiment Treebank, two-way classification BIBREF15
QQP: the Quora Question Pairs dataset
RTE: the Recognizing Textual Entailment datasets
QNLI: Question-answering NLI based on the Stanford Question Answering Dataset BIBREF3
MNLI: the Multi-Genre Natural Language Inference Corpus, matched section BIBREF16
We use the following subset of GLUE tasks BIBREF4 for fine-tuning:

MRPC: the Microsoft Research Paraphrase Corpus BIBREF13

STS-B: the Semantic Textual Similarity Benchmark BIBREF14

SST-2: the Stanford Sentiment Treebank, two-way classification BIBREF15

QQP: the Quora Question Pairs dataset

RTE: the Recognizing Textual Entailment datasets

QNLI: Question-answering NLI based on the Stanford Question Answering Dataset BIBREF3

MNLI: the Multi-Genre Natural Language Inference Corpus, matched section BIBREF16
Answer: The answers are shown as follows:
* MRPC
* STS-B
* SST-2
* QQP
* RTE
* QNLI
* MNLI
Question: Tell me whether these Pokemon are fairy or poison type: Clefable, Jigglypuff, Rapidash, Arbok, Nidorino, Zubat
Answer: Clefable: Fairy
Jigglypuff: Fairy
Rapidash: Fairy
Arbok: Poison
Nidorino: Poison
Zubat: Poison
Question: Why are convolutional LMs preferred than n-gram LMs？
Evidence: The convolutional language model (LM) is the GCNN-14B from BIBREF0 , which achieved competitive results on several language modeling benchmarks. The network contains 14 convolutional residual blocks BIBREF21 with a growing number of channels, and uses gated linear units as activation function.
The language model is used to score candidate transcriptions in addition to the acoustic model in the beam search decoder described in the next section. Compared to n-gram LMs, convolutional LMs allow for much larger context sizes. Our detailed experiments study the effect of context size on the final speech recognition performance.
Compared to n-gram LMs, convolutional LMs allow the decoder to look at longer context with better perplexity.
Answer: Because convolutional LMs allow the decoder to look at longer context with better perplexity.
Question: What is an apartment building?
Evidence: Apartment buildings are multi-story buildings where three or more residences are contained within one structure. Such a building may be called an apartment building, apartment complex, flat complex, block of flats, tower block, high-rise or, occasionally, mansion block (in British English), especially if it consists of many apartments for rent. A high-rise apartment building is commonly referred to as a residential tower, apartment tower, or block of flats in Australia.
Answer: An apartment building is a multi-story building with at least three residences. Each residential unit within the building has many different names depending on the country or region; flat is commonly used in British English, while the term apartment is most popular in North America.
Question: What is the most known picture from Dorothea Lange
Evidence: Resettlement Administration

Lange's iconic 1936 photograph of Florence Owens Thompson, Migrant Mother

"Broke, baby sick, and car trouble!" (1937)
Lange and Dixon divorced on October 28, 1935 and on December 6 she married economist Paul Schuster Taylor, professor of economics at the University of California, Berkeley. For the next five years they traveled through the California coast and the midwest. Throughout their travels they documented rural poverty, in particular the exploitation of sharecroppers and migrant laborers. Taylor interviewed subjects and gathered economic data while Lange produced photographs and accompanying data. They lived and worked from Berkeley for the rest of her life.

Working for the Resettlement Administration and Farm Security Administration, Lange's images brought to public attention the plight of the poor and forgotten—particularly sharecroppers, displaced farm families, and migrant workers. Lange's work was distributed to newspapers across the country, and the poignant images became icons of the era.

One of Lange's most recognized works is Migrant Mother, published in 1936. The woman in the photograph is Florence Owens Thompson. In 1960, Lange spoke about her experience taking the photograph:

"I saw and approached the hungry and desperate mother, as if drawn by a magnet. I do not remember how I explained my presence or my camera to her, but I do remember she asked me no questions. I made five exposures, working closer and closer from the same direction. I did not ask her name or her history. She told me her age, that she was thirty-two. She said that they had been living on frozen vegetables from the surrounding fields, and birds that the children killed. She had just sold the tires from her car to buy food. There she sat in that lean-to tent with her children huddled around her, and seemed to know that my pictures might help her, and so she helped me. There was a sort of equality about it."

Lange reported the conditions at the camp to the editor of a San Francisco newspaper, showing him her photographs. The editor informed federal authorities and published an article that included some of the images. In response, the government rushed aid to the camp to prevent starvation.

According to Thompson's son, while Lange got some details of the story wrong, the impact of the photograph came from an image that projected both the strengths and needs of migrant workers. Twenty-two of Lange's photographs produced for the FSA were included in John Steinbeck's The Harvest Gypsies when it was first published in 1936 in The San Francisco News. According to an essay by photographer Martha Rosler, Migrant Mother became the most reproduced photograph in the world.
Answer: The most famous picture taken by Dorothea Lange is the "migrant mother" where a women is shown with her child during the great depression. The image symbolises the suffering of the american population at the time following years of bad crop that led to the economic crisis of 1929 and mid 30's depression.
Question: what is the shortcoming of the model of Neelakantan et al., 2015?
Evidence: To the best of our knowledge, we are not aware of any knowledge learning system that can learn new knowledge in the conversation process. This section thus discusses other related work.
Among existing KB completion approaches, BIBREF20 extended the vector space model for zero-shot KB inference. However, the model cannot handle unknown entities and can only work on fixed set of unknown relations with known embeddings. Recently, BIBREF22 proposed a method using external text corpus to perform inference on unknown entities. However, the method cannot handle unknown relations. Thus, these methods are not suitable for our open-world setting. None of the existing KB inference methods perform interactive knowledge learning like LiLi. NELL BIBREF23 continuously updates its KB using facts extracted from the Web. Our task is very different as we do not do Web fact extraction (which is also useful). We focus on user interactions in this paper. Our work is related to interactive language learning (ILL) BIBREF24 , BIBREF25 , but these are not about KB completion. The work in BIBREF26 allows a learner to ask questions in dialogue. However, this work used RL to learn about whether to ask the user or not. The “what to ask aspect" was manually designed by modeling synthetic tasks. LiLi formulates query-specific inference strategies which embed interaction behaviors. Also, no existing dialogue systems BIBREF4 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 employ lifelong learning to train prediction models by using information/knowledge retained in the past.
Our work is related to general lifelong learning in BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 . However, they learn only one type of tasks, e.g., supervised, topic modeling or reinforcement learning (RL) tasks. None of them is suitable for our setting, which involves interleaving of RL, supervised and interactive learning. More details about lifelong learning can be found in the book BIBREF31 .
Among existing KB completion approaches, (Neelakantan et al., 2015) extended the vector space model for zero-shot KB inference. However, the model cannot handle unknown entities and can only work on fixed set of unknown relations with known embeddings.
Answer: It cannot handle unknown entities and can only work on fixed set of unknown relations with known embeddings.
Question: What is the difference between a day on Earth and Mars?
Answer: A day on mars is called a Sol whose name is borrowed from the Latin word for Sun. Compared to a day on Earth, a sol is slightly longer at 24 hours, 39 minutes, and 35 seconds which is equivalent to 1.0275 Earth days. Spacecraft that have landed on Mars conventionally use a 24 hour Mars clock where the hours, minutes, and seconds are 2.75% longer than the standard Earth durations.
Question: Extract which animals Waddell played with and which ones he fought with from the text. Separate them with a comma, putting the ones he played with first.
Evidence: Waddell's career wound through a number of teams. He was notably unpredictable; early in his career, he once left in the middle of a game to go fishing. He also had a longstanding fascination with fire trucks and ran off the field to chase after them during games on multiple occasions. He would disappear for months at a time during the offseason, and it was not known where he went until it was discovered that he was wrestling alligators in a circus. He was easily distracted by opposing fans who held up puppies, which caused him to run over to play with them, and shiny objects, which seemed to put him in a trance. An alcoholic for much of his short life, he reportedly spent his entire first signing bonus on a drinking binge; as a pun of the baseball term "southpaw" denoting a left-handed pitcher, the Sporting News dubbed him a "sousepaw". His eccentric behavior led to constant battles with his managers and scuffles with bad-tempered teammates.
Answer: puppies, alligators
Question: Who created the first chocolate bar?
Answer: Joseph Fry created the first chocolate bar in 1847. Soon after he created the Fry's Chocolate Cream bar in 1866. The first chocolate bar was made of bittersweet chocolate.