DataHammer/scimrc huggingface dataset:
Question: What's the ability of rephrasing?
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.
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.
Answer: It could help when the system does know the concept but does not know the correct wording.
Question: What are the comparable alternative architectures?
Evidence: We compared our proposed approaches to a standard CNN that exploits a single set of word embeddings BIBREF3 . We also compared to a baseline of simply concatenating embeddings for each word to form long vector inputs. We refer to this as Concatenation-CNN C-CNN. For all multiple embedding approaches (C-CNN, MG-CNN and MGNC-CNN), we explored two combined sets of embedding: word2vec+Glove, and word2vec+syntactic, and one three sets of embedding: word2vec+Glove+syntactic. For all models, we tuned the l2 norm constraint INLINEFORM0 over the range INLINEFORM1 on a validation set. For instantiations of MGNC-CNN in which we exploited two embeddings, we tuned both INLINEFORM2 , and INLINEFORM3 ; where we used three embedding sets, we tuned INLINEFORM4 and INLINEFORM5 .
More similar to our work, Yin and Schütze yin-schutze:2015:CoNLL proposed MVCNN for sentence classification. This CNN-based architecture accepts multiple word embeddings as inputs. These are then treated as separate `channels', analogous to RGB channels in images. Filters consider all channels simultaneously. MVCNN achieved state-of-the-art performance on multiple sentence classification tasks. However, this model has practical drawbacks. (i) MVCNN requires that input word embeddings have the same dimensionality. Thus to incorporate a second set of word vectors trained on a corpus (or using a model) of interest, one needs to either find embeddings that happen to have a set number of dimensions or to estimate embeddings from scratch. (ii) The model is complex, both in terms of implementation and run-time. Indeed, this model requires pre-training and mutual-learning and requires days of training time, whereas the simple architecture we propose requires on the order of an hour (and is easy to implement).
FLOAT SELECTED: Table 1: Results mean (min, max) achieved with each method. w2v:word2vec. Glv:GloVe. Syn: Syntactic embedding. Note that we experiment with using two and three sets of embeddings jointly, e.g., w2v+Syn+Glv indicates that we use all three of these.
We compared our proposed approaches to a standard CNN that exploits a single set of word embeddings BIBREF3 . We also compared to a baseline of simply concatenating embeddings for each word to form long vector inputs. We refer to this as Concatenation-CNN C-CNN. For all multiple embedding approaches (C-CNN, MG-CNN and MGNC-CNN), we explored two combined sets of embedding: word2vec+Glove, and word2vec+syntactic, and one three sets of embedding: word2vec+Glove+syntactic. 
More similar to our work, Yin and Schütze yin-schutze:2015:CoNLL proposed MVCNN for sentence classification. This CNN-based architecture accepts multiple word embeddings as inputs. 
FLOAT SELECTED: Table 1: Results mean (min, max) achieved with each method. w2v:word2vec. Glv:GloVe. Syn: Syntactic embedding. Note that we experiment with using two and three sets of embeddings jointly, e.g., w2v+Syn+Glv indicates that we use all three of these.
Answer: The answers are shown as follows:
* standard CNN
* C-CNN
* MVCNN
Question: If you were going to learn how to play basketball, how would you do it?
Answer: 1. Learn the basic rules. Start by understanding how the game is played, either by watching local pickup games or watching professional basketball online.

2. Practice on your own. Find a basketball and go to a local basketball court. Practice taking various shots including layups, free throws, jump shots, and three-pointers. Find videos online to help improve your shooting form. This will help prepare you for playing basketball games with others.

3. Play pickup games. Find a group to play basketball with at a local gym or at an outdoor court. If you don't have enough people to play with, you can play 1-1 or half-court 3 on 3. This will help you learn facets of the game that cannot be learned when practicing on your own such as passing and defense.
Question: Was this experiment done in a lab?
Evidence: We recruited 102 pairs of participants from Amazon Mechanical Turk and randomly assigned speaker and listener roles. After we removed 7 games that disconnected part-way through and 12 additional games according to our pre-registered exclusion criteria (due to being non-native English speakers, reporting confusion about the instructions, or clearly violating the instructions), we were left with a sample of 83 full games.
We recruited 102 pairs of participants from Amazon Mechanical Turk and randomly assigned speaker and listener roles
Answer: No.
Question: Which of the following cities are the capital of a province of the Netherlands: Emmen, Almere, Assen, Den Helder, Enschede, Terneuzen, Lelystad, Leeuwarden, Arnhem, Groningen, Maastricht, 's-Hertogenbosch, Haarlem, Zwolle, Bussum, Alkmaar, Hilversum, The Hague, Utrecht, Amsterdam, Rotterdam, Middelburg, Amstelveen, Terneuzen
Answer: The following cities are the capital of a province of the Netherlands: Assen, Lelystad, Leeuwarden, Arnhem, Groningen, Maastricht, 's-Hertogenbosch, Haarlem, Zwolle, The Hague, Utrecht, Middelburg
Question: Identify the bird from the list: Cockroach, Hawfinch, Humpback whale
Answer: Hawfinch
Question: Which of these would I find on a pirate ship: oars, cannons, gym shoes, air conditioning, swords, frisbee golf set
Answer: On a pirate ship you’d probably find oars, cannons, and swords. You wouldn’t find gym shoes, air conditioning, or a frisbee golf set
Question: What conclusion does the author draw from this paper?
Evidence: We have shown that DNN models can be used for cyberbullying detection on various topics across multiple SMPs using three datasets and four DNN models. These models coupled with transfer learning beat state of the art results for all three datasets. These models can be further improved with extra data such as information about the profile and social graph of users. Most of the current datasets do not provide any information about the severity of bullying. If such fine-grained information is made available, then cyberbullying detection models can be further improved to take a variety of actions depending on the perceived seriousness of the posts.
We have shown that DNN models can be used for cyberbullying detection on various topics across multiple SMPs using three datasets and four DNN models. These models coupled with transfer learning beat state of the art results for all three datasets. These models can be further improved with extra data such as information about the profile and social graph of users.
Answer: They have shown that DNN models can be used for cyberbullying detection on various topics across multiple SMPs using three datasets and four DNN models. These models coupled with transfer learning beat state of the art results for all three datasets. These models can be further improved with extra data such as information about the profile and social graph of users.
Question: List down few details about CURE algorithm from given text
Evidence: To avoid the problems with non-uniform sized or shaped clusters, CURE employs a hierarchical clustering algorithm that adopts a middle ground between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α. The scattered points after shrinking are used as representatives of the cluster. The clusters with the closest pair of representatives are the clusters that are merged at each step of CURE's hierarchical clustering algorithm. This enables CURE to correctly identify the clusters and makes it less sensitive to outliers.
Answer: 1. CURE uses a hierarchical clustering method that selects a middle ground between the centroid based and all point extremes in order to avoid the issues with non-uniformly sized or formed clusters.
2. In CURE, a fixed number c of evenly spaced-out points from a cluster are selected, and they are shrunk by a fraction in the direction of the cluster centroid.
3. The scattered points that have shrunk are used as cluster representatives.
4. At each stage of the hierarchical clustering algorithm used by CURE, the clusters with the closest pair of representatives are the clusters that are combined.
5. This makes CURE less sensitive to outliers and allows it to appropriately detect the clusters.
Question: What metric or criteria do they use to evaluate their models?
Evidence: We developed several linguistic, temporal and combined temporal-linguistic models for our task. These models take as input a user, $u$ , from one of the sites (i.e., Twitter or Facebook) and a list of $N$ users from the other service, where one of the $N$ users, $u\prime $ , is the same as $u$ . The models then provide a ranking among candidate matches between $u$ and each of the $N$ users. We used two criteria to evaluate our models:
A baseline random choice ranker would have an accuracy of $1/N$ , and an average rank of $N/2$ (since $u\prime $ may appear anywhere in the list of $N$ items).
We used two criteria to evaluate our models:– Accuracy: percentage of cases when a model’s top ranked candidate is u0. – Average Rank: the average rank of u within the ranked list of candidates generated by a model.
Answer: Accuracy and Average Rank.
Question: Give me an idea for a country to travel to that is safe, inexpensive and does not fall into the well trodden tourist path?
Answer: One such place is Mongolia. Situated between China and Russia, it is an ideal destination to explore national parks, deserts, and history. Mongolia is the historic home to one of the largest empires to ever conquered the earth. The Mongol empire covered most of Eurasia and had a fierce nomadic culture and modern weaponry. 
As a traveler to Mongolia, you can spend time with today's nomadic tribes, hike through the Khstain Nuruu National Park park while sleeping in luxury tents, marvel at the beauty of Ugil Lake, or Camel ride through the dunes of the Gobi desert. 
You will enjoy Mongolia while feeling safe and awed without breaking the bank.
Question: What is process mining?
Evidence: Process mining is a family of techniques relating the fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes.Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the executional path taken by operational processes and address their performance and compliance problems.

Process mining starts from event data. Input for process mining is an event log. An event log views a process from a particular angle. Each event in the log should contain (1) a unique identifier for a particular process instance (called case id), (2) an activity (description of the event that is occurring), and (3) a timestamp. There may be additional event attributes referring to resources, costs, etc., but these are optional. With some effort, such data can be extracted from any information system supporting operational processes. Process mining uses these event data to answer a variety of process-related questions.
Answer: Process mining brings together data mining, data science and process management to find process efficiencies.
Question: When did the fist episode of Family Guy air?
Answer: January 31, 1999
Question: What is the relationship between interest rates and the economy?
Answer: Central banks typically adjust interest rates as a way to influence inflation and economic growth. When a country is experiencing high rates of inflation, its central bank will typically increase the interest rate which will increase the cost of credit and therefore reduce economic demand and consumption. Reduced demand will lead to reduced prices and less inflation. When a country is experiencing deflation, the central bank will typically decrease interest rates to reduce cost of credit and increase consumption. Central banks must be careful with interest rates. High interest rates for a sustained period of time can lead to a recession. Whereas, low interest rates for a sustained period of time can lead to hyperinflation and an overheated economy.
Question: What are some contributions of women in computing?
Answer: The first algorithm intended to be executed by a computer was designed by Ada Lovelace who was a pioneer in the field. Grace Hopper was the first person to design a compiler for a programming language. Throughout the 19th and early 20th century, and up to World War II, programming was predominantly done by women; significant examples include the Harvard Computers, codebreaking at Bletchley Park and engineering at NASA.
Question: Tell me whether the following are public or private universities in the United States: Harvard University, University of Florida, University of Massachusetts Amherst, Cornell University, Amherst College, Colby College, and the University of Michigan Ann Arbor.
Answer: Harvard University, Cornell University, Amherst College, and Colby College are private universities. The rest of the schools you mentioned are public universities.
Question: Is Moore's law still viable?
Evidence: Moore's law is the observation that the number of transistors in an integrated circuit (IC) doubles about every two years. Moore's law is an observation and projection of a historical trend. Rather than a law of physics, it is an empirical relationship linked to gains from experience in production. The observation is named after Gordon Moore, the co-founder of Fairchild Semiconductor and Intel (and former CEO of the latter), who in 1965 posited a doubling every year in the number of components per integrated circuit, and projected this rate of growth would continue for at least another decade. In 1975, looking forward to the next decade, he revised the forecast to doubling every two years, a compound annual growth rate (CAGR) of 41%. While Moore did not use empirical evidence in forecasting that the historical trend would continue, his prediction held since 1975 and has since become known as a "law". Moore's prediction has been used in the semiconductor industry to guide long-term planning and to set targets for research and development, thus functioning to some extent as a self-fulfilling prophecy. Advancements in digital electronics, such as the reduction in quality-adjusted microprocessor prices, the increase in memory capacity (RAM and flash), the improvement of sensors, and even the number and size of pixels in digital cameras, are strongly linked to Moore's law. These ongoing changes in digital electronics have been a driving force of technological and social change, productivity, and economic growth. Industry experts have not reached a consensus on exactly when Moore's law will cease to apply. Microprocessor architects report that semiconductor advancement has slowed industry-wide since around 2010, slightly below the pace predicted by Moore's law. In September 2022 Nvidia CEO Jensen Huang considered Moore's law dead, while Intel CEO Pat Gelsinger was of the opposite view.
Answer: Moore's law is the observation that the number of transistors in a circuit doubles about every two years. Through advancements in technology, Moore's law is still viable since 1975 with some experts believing the observation still holds and some seeing a slowing of Moore's observation.
Question: What is KBQA？
Evidence: Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.
Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA).
Answer: KBQA is short for "Knowledge Base Question Answering".
Question: Tell me whether these golfers are right-handed or left-handed: Phil Mickelson, Sergio Garcia, Bubba Watson, Kevin Na, Carlos Ortiz, Brooks Koepka
Answer: Phil Mickelson: Left-handed
Sergio Garcia: Right-handed
Bubba Watson: Left-handed
Kevin Na: Right-handed
Carlos Ortiz: Right-handed
Brooks Koepka: Right-handed
Question: What is the first name of the man Evershed crater was named after?
Evidence: Evershed is a lunar impact crater on the far side of the Moon, named after the English solar astronomer John Evershed. It is located to the northeast of the larger crater Cockcroft, and to the north of the smaller Van den Bergh.
Answer: John
Question: Do the images have multilingual annotations or monolingual ones?
Evidence: We experiment using a dataset derived from Google Images search results. The dataset consists of queries and the corresponding image search results. For example, one (query, image) pair might be “cat with big ears” and an image of a cat. Each (query, image) pair also has a weight corresponding to a relevance score of the image for the query. The dataset includes 3 billion (query, image, weight) triples, with 900 million unique images and 220 million unique queries. The data was prepared by first taking the query-image set, filtering to remove any personally identifiable information and adult content, and tokenizing the remaining queries by replacing special characters with spaces and trimming extraneous whitespace. Rare tokens (those that do not appear in queries at least six times) are filtered out. Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images. For example, if the query “back pain” is made by a user with English as her home language, then the query is stored as “en:back en:pain”. The dataset includes queries in about 130 languages.
The dataset consists of queries and the corresponding image search results.
Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images.
Answer: monolingual
Question: From the passage note down the years when global recession happened. List the results in comma separated format.
Evidence: The International Monetary Fund defines a global recession as "a decline in annual per‑capita real World GDP (purchasing power parity weighted), backed up by a decline or worsening for one or more of the seven other global macroeconomic indicators: Industrial production, trade, capital flows, oil consumption, unemployment rate, per‑capita investment, and per‑capita consumption".According to this definition, since World War II there were only four global recessions (in 1975, 1982, 1991 and 2009), all of them only lasting a year (although the 1991 recession would have lasted until 1993 if the IMF had used normal exchange rate weighted per‑capita real World GDP rather than the purchasing power parity weighted per‑capita real World GDP). The 2009 global recession, also known as the Great Recession, was by far the worst of the four postwar recessions, both in terms of the number of countries affected and the decline in real World GDP per capita.
Answer: 1975, 1982, 1991, 2009
Question: Give me some Mother's Day gift ideas.
Answer: The choice of Mother's Day gift depends on who the gift-giver is. An adult child or spouse might opt flowers or jewelry. A young child may opt for a hand-made card or a coupon to do a household chore (e.g. clean the dishes). A collective gift from multiple family members could be breakfast in bed or a gift certificate for a spa treatment.