DataHammer/scimrc huggingface dataset:
Question: Please give me a short bulleted list of the key events that happened in Kobe Bryant’s early life in time order, from earliest to the latest.
Evidence: Bryant was born on August 23, 1978, in Philadelphia, the youngest of three children and the only son of Pamela Cox Bryant and former NBA player Joe Bryant. He was also the maternal nephew of NBA player John "Chubby" Cox. His parents named him after the famous beef of Kobe, Japan, which they saw on a restaurant menu. His middle name, Bean, was derived from his father's nickname "Jellybean". Bryant's family was Catholic and he was brought up with this faith.

Bryant started playing basketball when he was three, and the Lakers were his favorite team when he was growing up. When Bryant was six, his father retired from the NBA and moved his family to Rieti in Italy to continue playing professional basketball.After two years, they moved first to Reggio Calabria, then to Pistoia and Reggio Emilia. Kobe became accustomed to his new lifestyle and learned to speak fluent Italian. He was especially fond of Reggio Emilia, which he considered a loving place and where some of his best childhood memories were made.  Bryant began to play basketball seriously while living in Reggio Emilia. Bryant's grandfather would mail him videos of NBA games for Bryant to study. Another source of inspiration was animated European films about sports, from which he learned more about basketball. From 1987 to 1989, his father played for Olimpia Basket Pistoia where he paired with former Detroit Pistons Leon Douglas. Kobe would work at the games as a ball and mop boy and would practice shooting at halftime, with Douglas sharing, "At every one of our games at halftime, it was the Kobe show. He'd get out there and get his shot up. We'd come out of the locker room at halftime and have to chase him off the court".

Bryant also learned to play soccer, and his favorite soccer team was A.C. Milan. During summers, Bryant would come back to the United States to play in a basketball summer league. When Bryant was 13, he and his family moved back to Philadelphia, where he enrolled in eighth grade at Bala Cynwyd Middle School.
Answer: - In August 1978, Kobe Bryant was born in Philadelphia. The name Kobe comes from the famous Japanese beef, Kobe.
- When Kobe was three years old, he started playing basketball.
- When Kobe was six, he moved to Italy with his family as his father started to play professional basketball there. 
- Two years later, Kobe moved to Reggio Calabria, and Pistoia and Reggio Emilia later.
- Kobe returned to the US when he was 13 with his family. He studied in Bala Cynwyd Middle School.
Question: Is Britain the only country that drives on the left-hand side of the road?
Answer: Britain is not the only country that drives on the left-hand side of the road. Approximately a third of the global population drives on the left.
Question: Is the LSTM bidirectional?
Evidence: We use the LSTM network as follows. The $x$ vector is fed through the LSTM network which outputs a vector $\overrightarrow{h_i}$ for each time step $i$ from 0 to $n-1$. This is the forward LSTM. As we have access to the complete vector $x$, we can process a backward LSTM as well. This is done by computing a vector $\overleftarrow{h_i}$ for each time step $i$ from $n-1$ to 0. Finally, we concatenate the backward LSTM with the forward LSTM:
Both $\overrightarrow{h_i}$ and $\overleftarrow{h_i}$ have a dimension of $l$, which is an optimized hyperparameter. The BiLSTM output $h$ thus has dimension $2l\times n$.
We use the LSTM network as follows. The $x$ vector is fed through the LSTM network which outputs a vector $\overrightarrow{h_i}$ for each time step $i$ from 0 to $n-1$. This is the forward LSTM. As we have access to the complete vector $x$, we can process a backward LSTM as well. This is done by computing a vector $\overleftarrow{h_i}$ for each time step $i$ from $n-1$ to 0. Finally, we concatenate the backward LSTM with the forward LSTM:

Both $\overrightarrow{h_i}$ and $\overleftarrow{h_i}$ have a dimension of $l$, which is an optimized hyperparameter. The BiLSTM output $h$ thus has dimension $2l\times n$.
Answer: Yes.
Question: What does the data cleaning and filtering process consist of?
Evidence: First, many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content, e.g. “New York (CNN) –”, “Jones Smith, May 10th, 2018:”. We therefore apply simple regular expressions to remove these prefixes.
Second, to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total. In this way, we filter out i) articles with excessively long content to reduce memory consumption; ii) very short leading sentences with little information which are unlikely to be a good summary. To encourage the model to generate abstrative summaries, we also remove articles where any of the top three sentences is exactly repeated in the rest of the article.
Third, we try to remove articles whose top three sentences may not form a relevant summary. For this purpose, we utilize a simple metric: overlapping words. We compute the portion of non-stopping words in the top three sentences that are also in the rest of an article. A higher portion implies that the summary is representative and has a higher chance of being inferred by the model using the rest of the article. To verify, we compute the overlapping ratio of non-stopping words between human-edited summary and the article in CNN/DailyMail dataset, which has a median value of 0.87. Therefore, in pretraining, we keep articles with an overlapping word ratio higher than 0.65.
First, many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content, e.g. “New York (CNN) –”, “Jones Smith, May 10th, 2018:”. We therefore apply simple regular expressions to remove these prefixes.

Second, to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total. In this way, we filter out i) articles with excessively long content to reduce memory consumption; ii) very short leading sentences with little information which are unlikely to be a good summary. To encourage the model to generate abstrative summaries, we also remove articles where any of the top three sentences is exactly repeated in the rest of the article.

Third, we try to remove articles whose top three sentences may not form a relevant summary.
Answer: The answers are shown as follows:
* many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content
* to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total
* we try to remove articles whose top three sentences may not form a relevant summary
Question: Given this paragraph about books, what is the Poison Book Project?
Evidence: The Poison Book Project is a project of the Winterthur Museum, Garden and Library and the University of Delaware to identify and catalog books known to contain poisonous substances, particularly arsenic in Paris green pigments. It was started in 2019 when Winterthur staff members Melissa Tedone and Rosie Grayburn identified a book containing Paris green in the institution's collection. The project has since confirmed at least 100 other books from libraries across the world that contain Paris green, allowing librarians to take measures to minimize the risk to those handling the books.
Answer: The Poison Book Project is a project of the Winterthur Museum, Garden and Library and the University of Delaware to identify and catalog books known to contain poisonous substances, particularly arsenic in Paris green pigments.
Question: Extract why Jerry Rice is the greatest wide receiver of all time.
Evidence: Nicknamed "World" because of his superb catching ability, his accomplishments and numerous records, Rice is widely regarded as the greatest wide receiver in NFL history and one of the greatest players of all time. His biography on the official Pro Football Hall of Fame website names him: "the most prolific wide receiver in NFL history with staggering career totals". In 1999, The Sporting News listed Rice second behind Jim Brown on its list of "Football's 100 Greatest Players". In 2010, he was chosen by NFL Network's NFL Films production The Top 100: NFL's Greatest Players as the greatest player in NFL history.
Answer: Jerry Rice holds numerous NFL receiving records. His 197 career touchdown receptions are 41 scores more than the second place of 156 touchdown receptions by Randy Moss; his 208 total touchdowns (197 receiving, ten rushing, and one fumble recovery) are 33 scores ahead of Emmitt Smith's second-place total of 175. His 22,895 career receiving yards are 5,403 yards ahead of the second-place Larry Fitzgerald. His 1,256 career points scored make him the highest-scoring non-kicker in NFL history.  Many of these records are considered by sports analysts to be unbreakable. During a career spanning two decades, Rice averaged 75.6 receiving yards per game.
Question: When is the World Teacher's Day celebrated?
Answer: World Teacher's Day is celebrated on 5th October
Question: Which pre-trained English NER model do they use?
Evidence: Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation. The hidden states of the character language model (CharLM) are used to create contextualized word embeddings. The final embedding INLINEFORM0 is concatenated by the CharLM embedding INLINEFORM1 and GLOVE embedding INLINEFORM2 BIBREF8 . A standard BiLSTM-CRF named entity recognition model BIBREF0 takes INLINEFORM3 to address the NER task.
We implement the basic BiLSTM-CRF model using PyTorch framework. FASTTEXT embeddings are used for generating word embeddings. Translation models are trained on United Nation Parallel Corpus. For pre-trained English NER system, we use the default NER model of Flair.
Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation. 
For pre-trained English NER system, we use the default NER model of Flair.
Answer: Bidirectional LSTM based NER model of Flair
Question: Write down some important aspects of short story Reason from the given text
Evidence: "Reason" is a science fiction short story by American writer Isaac Asimov, first published in the April 1941 issue of Astounding Science Fiction and collected in I, Robot (1950), The Complete Robot (1982), and Robot Visions (1990). It is part of Asimov's Robot series, and was the second of Asimov's positronic robot stories to see publication.
Answer: 1. Isaac Asimov, an American author of science fiction, first published "Reason" in the April 1941 edition of Astounding Science Fiction. 
2. It was later collected in Robot (1950), The Complete Robot (1982), and Robot Visions. (1990).
3. I was the second of Asimov's positronic robot stories to be published, it is a part of the Robot series.
Question: What is ice hockey?
Evidence: Ice hockey (or simply hockey) is a team sport played on ice skates, usually on an ice skating rink with lines and markings specific to the sport. It belongs to a family of sports called hockey. In ice hockey, two opposing teams use ice hockey sticks to control, advance, and shoot a closed, vulcanized, rubber disc called a "puck" into the other team's goal. Each goal is worth one point. The team which scores the most goals is declared the winner. In a formal game, each team has six skaters on the ice at a time, barring any penalties, one of whom is the goaltender. Ice hockey is a full contact sport, and is considered to be one of the more physically demanding sports.
Answer: Ice hockey is a sport played on ice skates and each player has a hockey stick that they use to advance, pass, and shoot a puck attempting to score goals. In an ice hockey game, two teams play against each other and the team that scores the most goals win.
Question: How to get the correct answer for questions that the system does not know?
Evidence: From the point of view of dialog systems providing general information from a knowledge base, the most limiting factor is that a large portion of the questions is understood poorly.
Current approaches BIBREF3 , BIBREF4 can only achieve around 50% accuracy on some question answering datasets. Therefore, we think that there is a room for improvements which can be achieved by interactively asking for additional information in conversational dialogs with users. This extra information can be used for improving policies of dialog systems. We call this approach the interactive learning from dialogs.
We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording. This area is extensively studied for the purposes of information retrieval BIBREF5 , BIBREF6 .
The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it.
Answer: Try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it.
Question: What type of diet does Dr. Michael Greger advocate for?
Answer: Dr. Michael Greger advocates for a whole foods plant based diet.
Question: What are state-of-the-art baselines?
Evidence: E2ECM BIBREF11: In dialogue policy maker, it adopts a classic classification for skeletal sentence template. In our implement, we construct multiple binary classifications for each act to search the sentence template according to the work proposed by BIBREF11.
CDM BIBREF10: This approach designs a group of classifications (two multi-class classifications and some binary classifications) to model the dialogue policy.
E2ECM BIBREF11: In dialogue policy maker, it adopts a classic classification for skeletal sentence template. In our implement, we construct multiple binary classifications for each act to search the sentence template according to the work proposed by BIBREF11.

CDM BIBREF10: This approach designs a group of classifications (two multi-class classifications and some binary classifications) to model the dialogue policy.
Answer: The answers are shown as follows:
* E2ECM
* CDM
Question: What does the dataset consist of in this paper?
Evidence: Student reflections are comments provided by students in response to a set of instructor prompts. The prompts are directed towards gathering students' feedback on course material. Student reflections are collected directly following each of a set of classroom lectures over a semester. In this paper, the set of reflections for each prompt in each lecture is considered a student reflection document. The objective of our work is to provide a comprehensive and meaningful abstractive summary of each student reflection document. Our dataset consists of documents and summaries from four course instantiations: ENGR (Introduction to Materials Science and Engineering), Stat2015 and Stat2016 (Statistics for Industrial Engineers, taught in 2015 and 2016, respectively), and CS (Data Structures in Computer Science). All reflections were collected in response to two pedagogically-motivated prompts BIBREF16: “Point of Interest (POI): Describe what you found most interesting in today's class” and “Muddiest Point (MP): Describe what was confusing or needed more detail.”
For each reflection document, at least one human (either a TA or domain expert) created summaries. Table TABREF4 shows example reference summary produced by one annotator for the CS course. Table TABREF5 summarizes the dataset in terms of number of lectures, number of prompts per lecture, average number of reflections per prompt, and number of abstractive reference summaries for each set of reflections.
Our dataset consists of documents and summaries from four course instantiations: ENGR1 (Introduction to Materials Sci ence and Engineering), Stat2015 and Stat20162 (Statistics for Industrial Engineers, taught in 2015 and 2016, respectively), and CS3 (Data Structures in Computer Science).
Answer: The dataset consists of documents and summaries from four course instantiations: ENGR1 (Introduction to Materials Science and Engineering), Stat2015 and Stat20162 (Statistics for Industrial Engineers, taught in 2015 and 2016, respectively), and CS3 (Data Structures in Computer Science).
Question: What has been achieved by KG2Vec?
Evidence: Recently, the number of public datasets in the Linked Data cloud has significantly grown to almost 10 thousands. At the time of writing, at least four of these datasets contain more than one billion triples each. This huge amount of available data has become a fertile ground for Machine Learning and Data Mining algorithms. Today, applications of machine-learning techniques comprise a broad variety of research areas related to Linked Data, such as Link Discovery, Named Entity Recognition, and Structured Question Answering. The field of Knowledge Graph Embedding (KGE) has emerged in the Machine Learning community during the last five years. The underlying concept of KGE is that in a knowledge base, each entity and relation can be regarded as a vector in a continuous space. The generated vector representations can be used by algorithms employing machine learning, deep learning, or statistical relational learning to accomplish a given task. Several KGE approaches have already shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Moreover, Distributional Semantics techniques (e.g., Word2Vec or Doc2Vec) are relatively new in the Semantic Web community. The RDF2Vec approaches BIBREF4 , BIBREF5 are examples of pioneering research and to date, they represent the only option for learning embeddings on a large knowledge graph without the need for state-of-the-art hardware. To this end, we devise the KG2Vec approach, which comprises skip-gram techniques for creating embeddings on large knowledge graphs in a feasible time but still maintaining the quality of state-of-the-art embeddings. Our evaluation shows that KG2Vec achieves a vector quality comparable to the most scalable approaches and can process more than 250 million triples in less than 7 hours on a machine with suboptimal performances.
Our evaluation shows that KG2Vec achieves a vector quality comparable to the most scalable approaches and can process more than 250 million triples in less than 7 hours on a machine with suboptimal performances.
Answer: KG2Vec achieves a vector quality comparable to the most scalable approaches and can process more than 250 million triples in less than 7 hours on a machine with suboptimal performances.
Question: What datasets did the author use to evaluate ARAML?
Evidence: We evaluated ARAML on three datasets: COCO image caption dataset BIBREF28 , EMNLP2017 WMT dataset and WeiboDial single-turn dialogue dataset BIBREF29 . COCO and EMNLP2017 WMT are the common benchmarks with no input to evaluate the performance of discrete GANs, and we followed the existing works to preprocess these datasets BIBREF12 , BIBREF11 . WeiboDial, as a dialogue dataset, was applied to test the performance of our model with input trigger. We simply removed post-response pairs containing low-frequency words and randomly selected a subset for our training/test set. The statistics of three datasets are presented in Table TABREF28 .
We evaluated ARAML on three datasets: COCO image caption dataset (Chen et al., 2015), EMNLP2017 WMT dataset1 and WeiboDial single-turn dialogue dataset (Qian et al.,2018).
Answer: They evaluated ARAML on three datasets: COCO image caption dataset, EMNLP2017 WMT dataset1 and WeiboDial single-turn dialogue dataset.
Question: Imagine you are a java expert and explain what OOPS means
Answer: As the name suggests, Object-Oriented Programming or OOPs refers to languages that use objects in programming, they use objects as a primary source to implement what is to happen in the code. Objects are seen by the viewer or user, performing tasks assigned by you. Object-oriented programming aims to implement real-world entities like inheritance, hiding, polymorphism etc. in programming. The main aim of OOP is to bind together the data and the functions that operate on them so that no other part of the code can access this data except that function.
Question: What are the pros and cons of denormalizing data sets?
Answer: The pro for denormalizing data is to optimize for scan related queries of data and data serving. The con is that denormalization increases data latency due to the re-building of the materialized view.
Question: Conference of Parties (COP) is the supreme body of?
Answer: UN framework Convention on Climate Change