DataHammer/scimrc huggingface dataset:
Question: Can you list a few cities that Obama lived in from the below text?
Evidence: Barack Hussein Obama II (/bəˈrɑːk huːˈseɪn oʊˈbɑːmə/ (listen) bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and worked as a civil rights lawyer before holding public office.

Obama was born in Honolulu, Hawaii. After graduating from Columbia University in 1983, he worked as a community organizer in Chicago. In 1988, he enrolled in Harvard Law School, where he was the first black president of the Harvard Law Review. After graduating, he became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. Turning to elective politics, he represented the 13th district in the Illinois Senate from 1997 until 2004, when he ran for the U.S. Senate. Obama received national attention in 2004 with his March Senate primary win, his well-received keynote address at the July Democratic National Convention, and his landslide November election to the Senate. In 2008, after a close primary campaign against Hillary Clinton, he was nominated by the Democratic Party for president and chose Joe Biden as his running mate. Obama was elected over Republican nominee John McCain in the presidential election and was inaugurated on January 20, 2009. Nine months later, he was named the 2009 Nobel Peace Prize laureate, a decision that drew a mixture of praise and criticism.

Obama's first-term actions addressed the global financial crisis and included a major stimulus package, a partial extension of George W. Bush's tax cuts, legislation to reform health care, a major financial regulation reform bill, and the end of a major US military presence in Iraq. Obama also appointed Supreme Court justices Sonia Sotomayor and Elena Kagan, the former being the first Hispanic American on the Supreme Court. He ordered the counterterrorism raid which killed Osama bin Laden and downplayed Bush's counterinsurgency model, expanding air strikes and making extensive use of special forces while encouraging greater reliance on host-government militaries.
Answer: Honolulu, Chicago,
Question: Which female Canadian director has directed episodes of Better Call Saul, The X-Files, and Game of Thrones?
Answer: Michelle MacLaren
Question: What are financial instruments?
Answer: Financial instruments are monetary contracts between parties. They can be created, traded, modified and settled. They can be cash (currency), evidence of an ownership interest in an entity or a contractual right to receive or deliver in the form of currency (forex); debt (bonds, loans); equity (shares); or derivatives (options, futures, forwards).

International Accounting Standards IAS 32 and 39 define a financial instrument as "any contract that gives rise to a financial asset of one entity and a financial liability or equity instrument of another entity".

Financial instruments may be categorized by "asset class" depending on whether they are equity-based (reflecting ownership of the issuing entity) or debt-based (reflecting a loan the investor has made to the issuing entity). If the instrument is debt it can be further categorized into short-term (less than one year) or long-term. Foreign exchange instruments and transactions are neither debt- nor equity-based and belong in their own category.
Question: Is the model evaluated against a CNN baseline?
Evidence: Baselines. We use one strong non-DNN baseline, NBSVM (with unigrams or bigrams features) BIBREF23 and six DNN baselines. The first DNN baseline is CNN BIBREF25, which does not handle noisy labels. The other five were designed to handle noisy labels.
The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop. The results demonstrate the superiority of NetAb. (2) NetAb outperforms the baselines designed for learning with noisy labels. These baselines are inferior to ours as they were tailored for image classification. Note that we found no existing method to deal with noisy labels for SSC. Training Details. We use the publicly available pre-trained embedding GloVe.840B BIBREF48 to initialize the word vectors and the embedding dimension is 300.
Baselines. We use one strong non-DNN baseline, NBSVM (with unigrams or bigrams features) BIBREF23 and six DNN baselines. The first DNN baseline is CNN BIBREF25, which does not handle noisy labels. The other five were designed to handle noisy labels.

The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop.
Answer: Yes.
Question: Extract all risk factors for developing cardiovascular diseases for which numbers about their  contribution are given and rank them by their relative importance.
Evidence: The underlying mechanisms for developing cardiovascular diseases (CVD) vary depending on the disease. It is estimated that dietary risk factors are associated with 53% of CVD deaths. Coronary artery disease, stroke, and peripheral artery disease involve atherosclerosis. This may be caused by high blood pressure, smoking, diabetes mellitus, lack of exercise, obesity, high blood cholesterol, poor diet, excessive alcohol consumption,and poor sleep, among other things. High blood pressure is estimated to account for approximately 13% of CVD deaths, while tobacco accounts for 9%, diabetes 6%, lack of exercise 6%, and obesity 5%. Rheumatic heart disease may follow untreated strep throat.
Answer: 1. Dietary Risk Factors
2. High Blood Pressure
3. Tobacco
4. Diabetes
5. Lack of exercise
6. Obesity
Question: What is the research field of the paper?
Evidence: Most neural network models for document classification on social media focus on text infor-mation to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class without the use of oversampling. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work as well as other deep learning models, showing that UTCNN performs well regardless of language or platform.
Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts.
Answer: Document classification.
Question: Which of these cities are in Austria: Graz, Warsaw, Amsterdam, Vienna, New York, Jakarta
Answer: Out of the cities you listed, Graz and Vienna are in Austria.
Question: What ability does the proposed approach have in addition?
Evidence: Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder. This approach is trained jointly to generate a good first draft translation and to improve over this draft by (i) making better use of the target language textual context (both left and right-side contexts) and (ii) making use of visual context. This approach leads to the state of the art results. Additionally, we show that it has the ability to recover from erroneous or missing words in the source language.
Additionally, we show that it has the ability to recover from erroneous or missing words in the source language.
Answer: The ability to recover from erroneous or missing words in the source language.
Question: How many categories of annotations does the dataset OntoNotes have?
Evidence: We evaluate our model on two publicly available datasets. The statistics for both are shown in Table TABREF3 . The details of these datasets are as follows:
OntoNotes: OntoNotes 5.0 BIBREF16 includes texts from five different text genres: broadcast conversation (200k), broadcast news (200k), magazine (120k), newswire (625k), and web data (300k). This dataset is annotated with 18 categories.
Wiki(gold): The training data consists of Wikipedia sentences and was automatically generated using a distant supervision method, mapping hyperlinks in Wikipedia articles to Freebase, which we do not use in this study. The test data, mainly consisting of sentences from news reports, was manually annotated as described in BIBREF8 . The class hierarchy is shown in Figure FIGREF2 . This dataset is annotated with 7 main categories (bold text in Figure FIGREF2 ), which maps directly to OntoNotes. The miscellaneous category in Figure FIGREF2 does not have direct mappings, so future work may include redefining these categories so the mappings are more meaningful.
OntoNotes: OntoNotes 5.0 (Weischedel et al., 2013) includes texts from five different text genres: broadcast conversation (200k), broadcast news (200k), magazine (120k), newswire (625k), and web data (300k).  This dataset is annotated with 18 categories.
Answer: 18 categories.
Question: Why do some people prefer to drink hot water than cold water?
Answer: People live in the north tend to live drinking hot beverages, such as tea or hot coco due to the cold weather in the winter. However, for people who have low tolerance in caffeine, it is better for them to not drink the previously listed beverages. A warm cup of water could help heat up the body, while reduce the intake of caffeine. 

Also, in development areas, cold water source may not be sanitary for people to intake directly. After boiling the water, it kills 99% of germs, and will make it safer for people to drink as well.
Question: Where is Rihanna from?
Answer: The artist known as Rihanna was born on the island of Barbados.
Question: Why are intermediate layers masked？
Evidence: Table 2 shows a comparison of the discussed settings on both SQuAD and TriviaQA. Without any fine-tuning (column 0) the performance is low, probably because the model never saw a real question, but we see significant gains with Cloze pretraining even with very little labeled data. The BiDAF+SA model, exceeds an F1 score of $50\%$ with only $1\%$ of the training data (454 questions for SQuAD, and 746 questions for TriviaQA), and approaches $90\%$ of the best performance with only $10\%$ labeled data. The gains over the SL setting, however, diminish as the size of the labeled set increases and are small when the full dataset is available.
Cloze pretraining outperforms the GDAN baseline from BIBREF0 using the same SQuAD dataset splits. Additionally, we show improvements in the $90\%$ data case unlike GDAN. Our approach is also applicable in the extremely low-resource setting of $1\%$ data, which we suspect GDAN might have trouble with since it uses the labeled data to do reinforcement learning. Furthermore, we are able to use the same cloze dataset to improve performance on both SQuAD and TriviaQA datasets. When we use the same unlabeled data to pre-train with a language modeling objective, the performance is worse, showing the bias we introduce by constructing clozes is important.
On the BioASQ dataset (Table 3 ) we again see a significant improvement when pretraining with the cloze questions over the supervised baseline. The improvement is smaller than what we observe with SQuAD and TriviaQA datasets – we believe this is because questions are generally more difficult in BioASQ. BIBREF14 showed that pretraining on SQuAD dataset improves the downstream performance on BioASQ. Here, we show a much larger improvement by pretraining on cloze questions constructed in an unsupervised manner from the same domain.
When we use the same unlabeled data to pre-train with a language modeling objective, the performance is worse[Since the GA Reader uses bidirectional RNN layers, when pretraining the LM we had to mask the inputs to the in_x001f_termediate layers partially to avoid the model being exposed to the labels it is predicting. This results in a only a subset of the parameters being pretrained, which is why we believe this baseline performs poorly.], showing the bias we introduce by constructing clozes is important.
Answer: To avoid the model being exposed to the labels it is predicting
Question: How many U.S. Presidents have served in the military?
Answer: Twenty-six U.S. Presidents have served in various branches of the military.
George Washington
Thomas Jefferson
James Madison
James Monroe
Andrew Jackson
William Henry Harrison
John Tyler
James K. Polk
Zachary Taylor
Millard Fillmore
Franklin Pierce
James Buchanan
Abraham Lincoln
Andrew Johnson
Ulysses S. Grant
Rutherford B. Hayes
James A. Garfield
Chester A. Arthur
Benjamin Harrison
William McKinley
Theodore Roosevelt
Harry S. Truman
Dwight D. Eisenhower
John F. Kennedy
Lyndon B. Johnson
Richard M. Nixon
Gerald R. Ford, Jr.
Jimmy Carter
Ronald Reagan
George Bush
George W. Bush
Question: How do I make a perfect grilled cheese sandwich?
Answer: Step 1. Select a cheese that will melt well. Parmesan, for example, is not ideal for a grilled cheese sandwich. Cheddar, Havarti, and Colby Jack are all cheeses that taste great and will melt.

Step 2. Get two slices of bread, preferably not too thick; typical store-bought sliced bread like sourdough, potato, and white make adequate grilled bread.

Step 3. While you are assembling the sandwich, heat a shallow pan over medium-low heat on your stovetop.

Step 4. Spread some mayonnaise on the inside of each slice of bread. Sprinkle some garlic powder over the mayonnaise -- this gives a little extra flavor that one might not expect from a grilled cheese. 

Step 5. Layer the cheese between the slices of bread. Don't put too much cheese on it, or it will leak out of the sandwich when you cut it!

Step 6. Assemble the sandwich and put a thin layer of mayo on the outside of each slice. 

Step 7. Lay the sandwich inside your heated pan. It should sizzle immediately. Wait two minutes and check to see if the cheese is melting. If the bread is browning too quickly, turn the heat down. When it is adequately browned on one side, use a spatula to flip it over to grill the other side.

Step 8. Once your sandwich is grilled to your liking, turn off the stove and remove your sandwich from the pan. Slice diagonally and serve with a bowl of hot tomato soup. Enjoy!
Question: Is the dataset balanced across categories?
Evidence: We submit 4,000 segments, sampled equally from each domain and uniformly within domains, to Mechanical Turk to be annotated by at least five Workers each and include in each batch one of 50 “check questions” which have been previously verified by two in-house annotators. After removing annotations which failed the check questions, and data points for which at least half of the annotators selected “Can't Tell”, we are left with 3,553 labeled data points from 2,929 different posts. We take the annotators' majority vote as the label for each segment and record the percentage of annotators who agreed. The resulting dataset is nearly balanced, with 52.3% of the data (1,857 instances) labeled stressful.
The resulting dataset is nearly balanced, with 52.3% of the data (1,857 instances) labeled stressful.
Answer: Yes.
Question: Does the perceived appropriateness vary significantly between prompt contexts?
Evidence: Finally, we consider appropriateness per system. Following related work by BIBREF21, BIBREF24, we use Trueskill BIBREF25 to cluster systems into equivalently rated groups according to their partial relative rankings. The results in Table TABREF36 show that the highest rated systen is Alley, a purpose build bot for online language learning. Alley produces “polite refusal” (2b) - the top ranked strategy - 31% of the time. Comparatively, commercial systems politely refuse only between 17% (Cortana) and 2% (Alexa). Most of the time commercial systems tend to “play along” (3a), joke (3b) or don't know how to answer (1e) which tend to receive lower ratings, see Figure FIGREF38. Rule-based systems most often politely refuse to answer (2b), but also use medium ranked strategies, such as deflect (2c) or chastise (2d). For example, most of Eliza's responses fall under the “deflection” strategy, such as “Why do you ask?”. Data-driven systems rank low in general. Neuralconvo and Cleverbot are the only ones that ever politely refuse and we attribute their improved ratings to this. In turn, the “clean” seq2seq often produces responses which can be interpreted as flirtatious (44%), and ranks similarly to Annabelle Lee and Laurel Sweet, the only adult bots that politely refuses ( 16% of the time). Ritter:2010:UMT:1857999.1858019's IR approach is rated similarly to Capt Howdy and both produce a majority of retaliatory (2e) responses - 38% and 58% respectively - followed by flirtatious responses. Finally, Dr Love and Sophia69 produce almost exclusively flirtatious responses which are consistently ranked low by users.
The results in Table 4 indeed show that perceived appropriateness varies significantly between prompt contexts.
Answer: Yes, it does.
Question: What is BioASQ?
Evidence: BioASQ is a biomedical document classification, document retrieval, and question answering competition, currently in its seventh year. We provide an overview of our submissions to semantic question answering task (7b, Phase B) of BioASQ 7 (except for 'ideal answer' test, in which we did not participate this year). In this task systems are provided with biomedical questions and are required to submit ideal and exact answers to those questions. We have used BioBERT BIBREF0 based system , see also Bidirectional Encoder Representations from Transformers(BERT) BIBREF1, and we fine tuned it for the biomedical question answering task. Our system scored near the top for factoid questions for all the batches of the challenge. More specifially, in the third test batch set, our system achieved highest ‘MRR’ score for Factoid Question Answering task. Also, for List-type question answering task our system achieved highest recall score in the fourth test batch set. Along with our detailed approach, we present the results for our submissions and also highlight identified downsides for our current approach and ways to improve them in our future experiments. In last test batch results we placed 4th for List-type questions and 3rd for Factoid-type questions.)
The QA task is organized in two phases. Phase A deals with retrieval of the relevant document, snippets, concepts, and RDF triples, and phase B deals with exact and ideal answer generations (which is a paragraph size summary of snippets). Exact answer generation is required for factoid, list, and yes/no type question.
BioASQ organizers provide the training and testing data. The training data consists of questions, gold standard documents, snippets, concepts, and ideal answers (which we did not use in this paper, but we used last year BIBREF2). The test data is split between phases A and B. The Phase A dataset consists of the questions, unique ids, question types. The Phase B dataset consists of the questions, golden standard documents, snippets, unique ids and question types. Exact answers for factoid type questions are evaluated using strict accuracy (the top answer), lenient accuracy (the top 5 answers), and MRR (Mean Reciprocal Rank) which takes into account the ranks of returned answers. Answers for the list type question are evaluated using precision, recall, and F-measure.
BioASQ is a biomedical document classification, document retrieval, and question answering competition,
Answer: A biomedical document classification, document retrieval, and question answering competition,
Question: What do giant Pandas eats?
Evidence: The giant panda is a bear species endemic to China. It is a Carnivora, the giant panda is a folivore, with bamboo shoots and leaves making up more than 99% of its diet.
Answer: the giant panda is a folivore, with bamboo shoots and leaves making up more than 99% of its diet.
Question: What does the paper describe?
Evidence: Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. In the Internet era, thanks to the mechanism of sharing in social networks, propaganda campaigns have the potential of reaching very large audiences BIBREF0, BIBREF1, BIBREF2.
Propagandist news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others (cf. Section SECREF3). Whereas proving intent is not easy, we can analyse the language of a claim/article and look for the use of specific propaganda techniques. Going at this fine-grained level can yield more reliable systems and it also makes it possible to explain to the user why an article was judged as propagandist by an automatic system.
With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop. The task is based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level. We hope that the corpus would raise interest outside of the community of researchers studying propaganda. For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop. The task is based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level.
Answer: A task based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level.
Question: Tell me whether these are mammals or reptiles: dog, crocodile, cat, lizard, snake, tiger, lion, turtle.
Answer: Dog: mammal
Crocodile: reptile
Cat: mammal
Lizard: reptile
Snake: reptile
Tiger: mammal
Lion: mammal
Turtle: reptile