DataHammer/scimrc huggingface dataset:
Question: Where did Sly and the Family Stone come from?
Evidence: Sly and the Family Stone was an American funk rock band originated from San Francisco, California. Active from 1966 to 1983, they were pivotal in the development of funk, soul, rock, and psychedelic music. Their core line-up was led by singer-songwriter, record producer, and multi-instrumentalist Sly Stone, and included Stone's brother and singer/guitarist Freddie Stone, sister and singer/keyboardist Rose Stone, trumpeter Cynthia Robinson, drummer Greg Errico, saxophonist Jerry Martini, and bassist Larry Graham. The band was the first major American rock group to have a racially integrated, male and female lineup.
Answer: Sly and the Family Stone originated from San Francisco, California.
Question: Classify each of these are a television show or a movie: Die Hard, The Wire, Greatest American Hero, Better Call Saul, Raiders of the Lost Ark, E.T., and Family Ties.
Answer: The television shows in this list include The Wire, Greatest American Hero, Better Call Saul, and Family Ties. The movies in this list include Die Hard, Raiders of the Lost Ark, and E.T.
Question: Write a shopping list for me and group the ingredients by type. I want to make stir-fried teriyaki tofu for dinner and I want enough ingredients to make a sauce, because I don't want to buy a bottle of sauce. This meal will serve four people.
Answer: Produce:
1lb broccoli florets
One yellow onion
One red bell pepper
One green bell pepper
One small knob ginger
One bulb garlic

Protein:
1lb extra firm tofu 

Misc:
1 can corn starch
8oz bottle soy sauce
1 bag of dark brown sugar
Red pepper flakes
Question: What are the interesting grammatical properties of Mapudungun?
Evidence: Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina BIBREF0. It is an isolate language and is classified as threatened by Ethnologue, hence the critical importance of all documentary efforts. Although the morphology of nouns is relatively simple, Mapudungun verb morphology is highly agglutinative and complex. Some analyses provide as many as 36 verb suffix slots BIBREF1. A typical complex verb form occurring in our corpus of spoken Mapudungun consists of five or six morphemes.
Mapudungun has several interesting grammatical properties. It is a polysynthetic language in the sense of BIBREF2; see BIBREF3 for explicit argumentation. As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages BIBREF4. One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself. One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role BIBREF5.
Mapudungun has several interesting grammatical properties. It is a polysynthetic language in the sense of (Baker, 1996); see (Loncon Antileo, 2011) for explicit argumentation. As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages (Baker et al., 2005). One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself. One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role (Arnold, 1996).
Answer: Firstly, it is a unique polysynthetic language insofar as the Noun appears to the right of the Verb, instead of to the left. Secondly, the distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms. One further remarkable property of Mapudungun is its inverse voice system of agreement.
Question: What country has the most active valcanoes in the world?
Answer: Indonesia has the most volcanoes in the world.  They currently have more than 140 located in an area call the Pacific Ring of Fire, around 76 of them are active volcanoes.  Active volcanoes can be very dangerous because they can erupt at any time, and predicting when they will erupt is an on-going science that scientists hope to be able to achieve with increased latency in order to save lives.
Question: Who were King Aenys I Targaryen's children?
Answer: Aegon, Viserys, Jaehaerys, Rhaena, Alysanne, Vaella
Question: Are Schindler elevators safe
Evidence: Schindler Holding Ltd. is a Swiss multinational company which manufactures escalators, moving walkways, and elevators worldwide, founded in Switzerland in 1874. Schindler produces, installs, maintains and modernizes lifts and escalators in many types of buildings including residential, commercial and high-rise buildings.

The company is present in over 140 countries and employs more than 66,000 people worldwide. The production facilities are located in Brazil, China, Slovakia, Spain, Switzerland, India and the United States. All operations and subsidiaries of Schindler Group are organised into Schindler Holding Ltd. (German: Schindler Holding AG, French: Schindler Holding S.A.), which is publicly traded on SIX Swiss Exchange.
Answer: Schindler Holding Ltd. is a Swiss multinational company which manufactures escalators, moving walkways, and elevators worldwide, founded in Switzerland in 1874. Schindler produces, installs, maintains and modernizes lifts and escalators in many types of buildings including residential, commercial and high-rise buildings.

The company is present in over 140 countries and employs more than 66,000 people worldwide. The production facilities are located in Brazil, China, Slovakia, Spain, Switzerland, India and the United States. All operations and subsidiaries of Schindler Group are organised into Schindler Holding Ltd., which is publicly traded on SIX Swiss Exchange.
Question: Given this paragraph about the NFL, in what year was the Super Bowl first held?
Evidence: Following an agreement to merge the NFL with the rival American Football League (AFL), the Super Bowl was first held in 1967 to determine a champion between the best teams from the two leagues and has remained as the final game of each NFL season since the merger was completed in 1970
Answer: 1967
Question: Is the manatee an endangered species?
Answer: All three species of manatee (Amazonian manatee, West Indian manatee, and the West African manatee) are listed by the World Conservation Union as vulnerable to extinction. However, The U.S. Fish and Wildlife Service (FWS) does not consider the West Indian manatee to be "endangered" anymore, having downgraded its status to "threatened" as of March 2017. They cite improvements to habitat conditions, population growth and reductions of threats as reasoning for the change.
Question: Where did they get training data?
Evidence: FLOAT SELECTED: Table 3: Precision (P), Recall (R), and Relative Coverage (RC) results on ConciergeQA.
FLOAT SELECTED: Table 4: Precision (P), Recall (R), and Relative Coverage (RC) results on AmazonQA dataset.
FLOAT SELECTED: Table 1: Various types of training instances.
FLOAT SELECTED: Table 3: Precision (P), Recall (R), and Relative Coverage (RC) results on ConciergeQA.
FLOAT SELECTED: Table 4: Precision (P), Recall (R), and Relative Coverage (RC) results on AmazonQA dataset.
FLOAT SELECTED: Table 1: Various types of training instances.
Answer: AmazonQA and ConciergeQA datasets
Question: How do they model a city description using embeddings?
Evidence: While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.
While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.
Answer: The answers are shown as follows:
* We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.
Question: How are different languages identified？
Evidence: Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
 INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example，the string ‘real' is pronounced differently in English，German，Spanish，and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets.
Answer: They prepend each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets.
Question: Tell if these are sports done in water, on land or both: triathlon, biathlon, water polo, sailing, crew, beach volleyball, basketball
Answer: Triathlon is done on both land and water, biathlon is done on land, water polo is done in water, sailing is done in water, crew is done in water, beach volleyball is done on land and basketball is done on land
Question: What is the task of slot filling?
Evidence: Slot Filling is an information extraction task which has become popular in the last years BIBREF3 . It is a shared task organized by the Text Analysis Conference (TAC). The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents. An example is “Steve Jobs” for the slot “X founded Apple”. Thinking of a text passage like “Steve Jobs was an American businessman. In 1976, he co-founded Apple”, it is clear that coreference resolution can play an important role for finding the correct slot filler value.
Slot Filling is an information extraction task which has become popular in the last years BIBREF3 .
The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents.
Answer: The answers are shown as follows:
* The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents.
Question: In which part of Germany is the town Kirchenlamitz located?
Evidence: Kirchenlamitz is a town in the district of Wunsiedel, in Bavaria, Germany. It is situated in the Fichtelgebirge, 13 km northwest of Wunsiedel and 19 km south of Hof.
Answer: Bavaria
Question: What type of products does this company produce?
Evidence: Sweet Tree Holdings is a Vermont, United States, company that produces maple products from a sugarbush and processing facility in Island Pond.
Answer: maple products
Question: What is a DBU?
Answer: dBu and dBV are decibel units specifically for measuring voltage. Unlike the dB, they are actually units because they can be converted to an actual voltage value. dBu is dB relative to 0.775 volts; such that 0dBu = 0.775 volts.
Question: Which two schemes are used?
Evidence: In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling. We have also proposed concrete architectures based on LSTMs to tackle the problem in both cases.
In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling.
Answer: The answers are shown as follows:
* sequence classification
* sequence labeling
Question: Why Audi Q5 sportsback is best over its rival
Answer: Audi Q5 is a great car. It's rival Mercedes GLC and BMW X3. Compare to this Audi q5 sportsback is good looking. the Car has enough power to beat these 2 on Hill road because of it's Quattro
Question: Give me a list of the top 10 types of clouds
Answer: Cumulus, Stratus, Stratocumulus, Cirrus, Cirrocumulus, Cirrostratus, Cumulonimbus, Altocumulus, Nimbostratus, Altostratus,
Question: Are all the trained models publicly available？
Evidence: We prepared precomputed ELMo contextual embeddings for seven languages: Croatian, Estonian, Finnish, Latvian, Lithuanian, Slovenian, and Swedish. We present the necessary background on embeddings and contextual embeddings, the details of training the embedding models, and their evaluation. We show that the size of used training sets importantly affects the quality of produced embeddings, and therefore the existing publicly available ELMo embeddings for the processed languages are inadequate. We trained new ELMo embeddings on larger training sets and analysed their properties on the analogy task and on the NER task. The results show that the newly produced contextual embeddings produce substantially better results compared to the non-contextual fastText baseline. In future work, we plan to use the produced contextual embeddings on the problems of news media industry. The pretrained ELMo models will be deposited to the CLARIN repository by the time of the final version of this paper.
The pretrained EMBEDDIA ELMo models are publicly available on the CLARIN repository14.
Answer: Yes.
Question: What kind of species are the Triplophysa longipectoralis
Answer: Triplophysa longipectoralis is a cave-living species of stone loach with vestigial eyes. The fish lives in clear water at temperatures below 20 °C. The holotype was caught in Xunle town, Huanjiang Maonan Autonomous County in the Liu River basin, Guangxi, China and was described by Zheng et al. in 2009.
Question: Why does my back hurt?
Answer: There are several reason why your back might hurt.  Generally, the problem starts not in your back but in a connected muscle group. To assess the problem, start with your feet and work your way up to your shoulders and neck.   If your shoulders and/or neck are tight, that will affect your back muscles.  Also, consider your shoes, your weight, the strength of your knees, and your core strength.
Question: Did they experiment with the corpus?
Evidence: The corpus creation process involved a small number of people that have voluntarily joined the initiative, with the authors of this paper directing the work. Initially, we searched for NER resources in Romanian, and found none. Then we looked at English resources and read the in-depth ACE guide, out of which a 16-class draft evolved. We then identified a copy-right free text from which we hand-picked sentences to maximize the amount of entities while maintaining style balance. The annotation process was a trial-and-error, with cycles composed of annotation, discussing confusing entities, updating the annotation guide schematic and going through the corpus section again to correct entities following guide changes. The annotation process was done online, in BRAT. The actual annotation involved 4 people, has taken about 6 months (as work was volunteer-based, we could not have reached for 100% time commitment from the people involved), and followed the steps:
The annotation process was a trial-and-error, with cycles composed of annotation, discussing confusing entities, updating the annotation guide schematic and going through the corpus section again to correct entities following guide changes.
Answer: Yes.
Question: what was the early uses of computers?
Answer: Early computers were meant to be used only for calculations. Simple manual instruments like the abacus have aided people in doing calculations since ancient times. Early in the Industrial Revolution, some mechanical devices were built to automate long, tedious tasks, such as guiding patterns for looms.
Question: What dataset is used for training the autocomplete system?
Evidence: We evaluate our approach by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews BIBREF6 (see Appendix for details). We quantify the efficiency of a communication scheme $(q_{\alpha },p_{\beta })$ by the retention rate of tokens, which is measured as the fraction of tokens that are kept in the keywords. The accuracy of a scheme is measured as the fraction of sentences generated by greedily decoding the model that exactly matches the target sentence.
We evaluate our approach by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews [38] (see Appendix C for details).
Answer: A dataset that contains sampled sentences fromYelp reviews.
Question: What tool is used in all of their experiments?
Evidence:  Training Data. We choose WIT3's TED corpus BIBREF12 as the basis of our experiments since it might be the only high-quality parallel data of many low-resourced language pairs. TED is also multilingual in a sense that it includes numbers of talks which are commonly translated into many languages. In addition, we use a much larger corpus provided freely by WMT organizers when we evaluate the impact of our approach in a real machine translation campaign. It includes the paralled corpus extracted from the digital corpus of European Parliament (EPPS), the News Commentary (NC) and the web-crawled parallel data (CommonCrawl). While the number of sentences in popular TED corpora varies from 13 thousands to 17 thousands, the total number of sentences in those larger corpus is approximately 3 million sentences.
Neural Machine Translation Setup. All experiments have been conducted using NMT framework Nematus, Following the work of Sennrich2016a, subword segmentation is handled in the prepocessing phase using Byte-Pair Encoding (BPE). Excepts stated clearly in some experiments, we set the number of BPE merging operations at 39500 on the joint of source and target data. When training all NMT systems, we take out the sentence pairs exceeding 50-word length and shuffle them inside every minibatch. Our short-list vocabularies contain 40,000 most frequent words while the others are considered as rare words and applied the subword translation. We use an 1024-cell GRU layer and 1000-dimensional embeddings with dropout at every layer with the probability of 0.2 in the embedding and hidden layers and 0.1 in the input and ourput layers. We trained our systems using gradient descent optimization with Adadelta BIBREF13 on minibatches of size 80 and the gradient is rescaled whenever its norm exceed 1.0. All the trainings last approximately seven days if the early-stopping condition could not be reached. At a certain time, an external evaluation script on BLEU BIBREF14 is conducted on a development set to decide the early-stopping condition. This evaluation script has also being used to choose the model archiving the best BLEU on the development set instead of the maximal loglikelihood between the translations and target sentences while training. In translation, the framework produces INLINEFORM0 -best candidates and we then use a beam search with the beam size of 12 to get the best translation.
All experiments have been conducted using NMT framework Nematus6 , Following the work of Sennrich et al. (2016b), subword segmentation is handled in the prepocessing phase using Byte-Pair Encoding (BPE).
Answer: NMT framework Nematus.
Question: Based on the above information, provide the real names of the cast in the American television sitcom Friends. Separate the names using a comma. Also provide the date when the series finale was aired. Provide the date in MM/DD/YYYY format.
Evidence: Friends is an American television sitcom created by David Crane and Marta Kauffman, which aired on NBC from September 22, 1994, to May 6, 2004, lasting ten seasons. With an ensemble cast starring Jennifer Aniston, Courteney Cox, Lisa Kudrow, Matt LeBlanc, Matthew Perry and David Schwimmer, the show revolves around six friends in their 20s and 30s who live in Manhattan, New York City. The series was produced by Bright/Kauffman/Crane Productions, in association with Warner Bros. Television. The original executive producers were Kevin S. Bright, Kauffman, and Crane.

Kauffman and Crane began developing Friends under the working title Insomnia Cafe between November and December 1993. They presented the idea to Bright, and together they pitched a seven-page treatment of the show to NBC. After several script rewrites and changes, including title changes to Six of One and Friends Like Us, the series was finally named Friends.

Filming took place at Warner Bros. Studios in Burbank, California. The show ranked within the top ten of the final television season ratings; it ultimately reached the number-one spot in its eighth season. The series finale aired on May 6, 2004, and was watched by around 52.5 million American viewers, making it the fifth-most-watched series finale in television history and the most-watched television episode of the 2000s.
Answer: As per the above passage, the cast of Friends include Jennifer Aniston, Courteney Cox, Lisa Kudrow, Matt LeBlanc, Matthew Perry, David Schwimmer. The series finale was aired on 05/06/2004.