DataHammer/scimrc huggingface dataset:
Question: What does their system consist of?
Evidence: The architecture of our convolutional neural net- work for sentiment classification is shown on Fig. 1. Our network is composed of a single convolutional layer followed by a non-linearity, max pooling, Dropout, fully connected layer and a soft-max classification layer. Here we describe this architecture:
The architecture of our convolutional neural net- work for sentiment classification is shown on Fig. 1. Our network is composed of a single convolutional layer followed by a non-linearity, max pooling, Dropout, fully connected layer and a soft-max classification layer.
Answer: A single convolutional layer followed by a non-linearity, max pooling, Dropout, fully connected layer and a softmax on top.
Question: Of this list of countries, which of these countries shares a border with at least one other country, and which do not? New Zealand, Canada, France, Australia, Germany, Finland, Turkey and Japan.
Answer: Canada, France, Germany, Finland, and Turkey all share a border with other countries. New Zealand, Australia and Japan do not share a border with any other country.
Question: Describe how skateboarding teaches you about life and to be resilient.
Answer: Skateboarding has been through quite an evolution since its first inception, when it came from the waves to the street. As a sport, skateboarding has always had a special attitude towards rules and pushed boundaries. Think of where one is supposed to skate (in the park) and where the best spots can actually be found across town (particularly where it's forbidden). In contrast to the waves in front of the coast, the asphalt one usually lands on when falling is pretty hard and rough - a circumstance felt by bones and skin. Skateboard riders usually fall quite a bit and despite all, the important thing is to get up and try again, and again, and again. It is just like in life where one grows with their challenges and needs to get up again until the goals are achieved. As humbling and painful as falling onto plain asphalt can be, it can also stimulate resilience and a relieving patience when the goal is in sight. Just try again.
Question: Which is a bird or fish: Red-throated diver, Redlip blenny
Answer: Redlip blenny is a fish, Red-throated diver is a bird
Question: How can I help soothe a teething baby?
Answer: Babies often begin teething anywhere from 3 months onwards with most babies displaying their first tooth around 5 months of age.  Teething can be very painful and cause great discomfort for your baby.  Some things you can do to help soothe a teething baby include letting baby chew on a pacifier, teething ring (cold or room temperature), putting a wet washcloth in the freezer and letting baby suck on that, Tylenol for infants (be sure to check with your pediatrician first before giving baby any medication under 6 months of age).
Question: Give me a two sentence summary of the following text: "A Venn diagram illustrates the relationships between two or more data sets. Venn diagrams are especially useful for highlighting similarities and differences and are commonly used to compare and contrast the characteristics of different data sets.

In a Venn diagram, circles are used to represent each data set. Inside each circle is a list of characteristics that define that data. Characteristics shared between two or more data sets are listed in the area where the circles overlap. Simple Venn diagrams consist of two overlapping circles, but complex Venn diagrams may compare up to five or more data sets using up to five or more circles.

Venn diagrams are commonly used in business and education settings to visualize and explore how things relate. In a business context, Venn diagrams are commonly used in product development, marketing, management, and more."
Answer: A Venn diagram is a commonly used type of diagram in business and education to illustrate similarities and differences between (at least) two datasets. In Venn diagrams, there are normally two slightly overlapping circles which represent two different datasets and the intersection of these circles contain characteristics of the similarities between the datasets and the symmetric difference of these circles contains the differences between these two datasets.
Question: What is the best northeast mountain for skiing?
Answer: Hunter mountain has the most diversity in trails and is an easy trip from New York.
Question: What components does the metric have?
Evidence: Following the setting of titovcrosslingual, we evaluate only on the arguments that were correctly identified, as the incorrectly identified arguments do not have any gold semantic labels. Evaluation is done using the metric proposed by lang2011unsupervised, which has 3 components: (i) Purity (PU) measures how well an induced cluster corresponds to a single gold role, (ii) Collocation (CO) measures how well a gold role corresponds to a single induced cluster, and (iii) F1 is the harmonic mean of PU and CO. For each predicate, let INLINEFORM0 denote the total number of argument instances, INLINEFORM1 the instances in the induced cluster INLINEFORM2 , and INLINEFORM3 the instances having label INLINEFORM4 in gold annotations. INLINEFORM5 , INLINEFORM6 , and INLINEFORM7 . The score for each predicate is weighted by the number of its argument instances, and a weighted average is computed over all the predicates.
(i) Purity (PU) measures how well an induced cluster corresponds to a single gold role, (ii) Collocation (CO) measures how well a gold role corresponds to a single induced cluster, and (iii) F1 is the harmonic mean of PU and CO. For each predicate, let N denote the total number of argument instances, Ci the instances in the induced cluster i, and Gj the instances having label j in gold annotations. P
Answer: Purity, collocation, and F1.
Question: What are the top largest economies in the world?
Answer: The United States
China
Japan
Germany
Question: Did they exclude retweets?
Evidence: Change is a universal property of language. For example, English has changed so much that Renaissance-era texts like The Canterbury Tales must now be read in translation. Even contemporary American English continues to change and diversify at a rapid pace—to such an extent that some geographical dialect differences pose serious challenges for comprehensibility BIBREF0 . Understanding language change is therefore crucial to understanding language itself, and has implications for the design of more robust natural language processing systems BIBREF1 .
Language change is a fundamentally social phenomenon BIBREF2 . For a new linguistic form to succeed, at least two things must happen: first, speakers (and writers) must come into contact with the new form; second, they must decide to use it. The first condition implies that language change is related to the structure of social networks. If a significant number of speakers are isolated from a potential change, then they are unlikely to adopt it BIBREF3 . But mere exposure is not sufficient—we are all exposed to language varieties that are different from our own, yet we nonetheless do not adopt them in our own speech and writing. For example, in the United States, many African American speakers maintain a distinct dialect, despite being immersed in a linguistic environment that differs in many important respects BIBREF4 , BIBREF5 . Researchers have made a similar argument for socioeconomic language differences in Britain BIBREF6 . In at least some cases, these differences reflect questions of identity: because language is a key constituent in the social construction of group identity, individuals must make strategic choices when deciding whether to adopt new linguistic forms BIBREF7 , BIBREF8 , BIBREF9 . By analyzing patterns of language change, we can learn more about the latent structure of social organization: to whom people talk, and how they see themselves.
But, while the basic outline of the interaction between language change and social structure is understood, the fine details are still missing: What types of social network connections are most important for language change? To what extent do considerations of identity affect linguistic differences, particularly in an online context? Traditional sociolinguistic approaches lack the data and the methods for asking such detailed questions about language variation and change.
In this paper, we show that large-scale social media data can shed new light on how language changes propagate through social networks. We use a data set of Twitter users that contains all public messages for several million accounts, augmented with social network and geolocation metadata. This data set makes it possible to track, and potentially explain, every usage of a linguistic variable as it spreads through social media. Overall, we make the following contributions:
We excluded retweets, which are explicitly marked with metadata, and focused on messages that were posted in English from within the United States.
Answer: Yes.
Question: What is used to train the ML model?
Evidence: To illustrate that semantically different yet related applications may have significant potential for reuse of features, let us consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.
Towards that, we analysed published works on three different types of events in different domains as described next:
Objective of this study is to design a ML model for identifying if there exist mentions of one of the nine types of bio-molecular interactions in (publicly available) Biomedical data. To train SVM based classifier, authors use GENETAG database, which is a tagged corpus for gene/protein named entity recognition. BioNLP 2009 shared task test-set was used to estimate performance of the system. Further details can be found at BIBREF22.
Objective of the study was to design ML model for enabling automated detection of specific financial events in the news text. Ten diﬀerent types of ﬁnancial events were considered including announcements regarding CEOs, presidents, products, competitors, partners, subsidiaries, share values, revenues, proﬁts, and losses. To train and test SVM and CRF based ML models, authors used data set consisting of 200 news messages extracted from the Yahoo! Business and Technology newsfeeds, having financial events and relations manually annotated by 3 domain experts. Further details can be found at BIBREF23.
Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data. 38 different types of events were considered for designing the system. To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets. Further details can be found at BIBREF24.
Table TABREF21 below depicts classes of features selected by authors of these works (as described in the corresponding references above) to highlight the point that despite domain differences, these applications share similar sets of features. Since authors of these works did not cite each other, it is possible that that these features might have been identified independently. This, in turn, supports the hypothesis that if adequate details of any one or two of these applications are fed to a system described in this work, which is designed to estimate semantic similarities across applications, system can automatically suggest potential features for consideration for the remaining applications to start with without requiring manual knowledge of the semantically related applications.
To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets.
Answer: To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets.
Question: What makes for a good photograph?
Answer: There are three key elements to a good picture - light, subject and composition. Great photographs are in focus, sharp, have good exposure (light) and composition.  In order to take the perfect photograph you must be at the correct focal length, set the appropriate aperture for the subject(s) in your frame, make sure the shutter speed is set for the action you are capturing (higher shutter speed for sports, lower shutter speed for portraits) and make sure the ISO is correctly set to work with both the shutter speed and aperture.  Understanding how the ISO, Aperture and shutter speed work together is key to taking the perfect picture.
Question: Classify the below substances based on their natural physical state (Solid or Gas).
Methane, Gold, Silver, Platinum
Answer: Solid - old, Silver, Platinum
Gas - Methane
Question: Who was responsible for creating Skynet?
Answer: Skynet is a fictional automated defense network in the Terminator movies. It's main creator is James Cameron, as he wrote and directed the first Terminator movie where Skynet was introduced.
Question: Provide 5 factors to consider when deciding where to live
Answer: Here are 5 factors to consider when deciding where to live:
1. Opportunities for employment
2. Cost of living
3. Weather
4. Proximity to family and friends
5. Things to do in the area
Question: What embeddings do they use?
Evidence: Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks.
Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks.
Answer: The answers are shown as follows:
* GloVe
Question: Extract names of all previous US presidents in the paragraph below:
Evidence: Muskets with interchangeable locks caught the attention of Thomas Jefferson through the efforts of Honoré Blanc when Jefferson was Ambassador to France in 1785. Jefferson tried to persuade Blanc to move to America, but was not successful, so he wrote to the American Secretary of War with the idea, and when he returned to the USA he worked to fund its development. President George Washington approved of the idea, and by 1798 a contract was issued to Eli Whitney for 12,000 muskets built under the new system.
Answer: The following are names of US presidents in the paragraph above: Thomas Jefferson (or Jefferson), George Washington.
Question: Name five different cuts of beef that I could ask for from a butcher
Answer: There are many popular cuts of beef. Three that you could ask your butcher to prepare are brisket, rib and sirloin.
Question: How many publicly available multilingual pretrained models are there to date?
Evidence: Pre-trained models BIBREF0, BIBREF1 have received much of attention recently thanks to their impressive results in many down stream NLP tasks. Additionally, multilingual pre-trained models enable many NLP applications for other languages via zero-short cross-lingual transfer. Zero-shot cross-lingual transfer has shown promising results for rapidly building applications for low resource languages. BIBREF2 show the potential of multilingual-BERT BIBREF0 in zero-shot transfer for a large number of languages from different language families on five NLP tasks, namely, natural language inference, document classification, named entity recognition, part-of-speech tagging, and dependency parsing.
Although multilingual models are an important ingredient for enhancing language technology in many languages, recent research on improving pre-trained models puts much emphasis on English BIBREF3, BIBREF4, BIBREF5. The current state of affairs makes it difficult to translate advancements in pre-training from English to non-English languages. To our best knowledge, there are only three available multilingual pre-trained models to date: (1) the multilingual-BERT (mBERT) that supports 104 languages, (2) cross-lingual language model BIBREF6 that supports 100 languages, and (3) Language Agnostic SEntence Representations BIBREF7 that supports 93 languages. Among the three models, LASER is based on neural machine translation approach and strictly requires parallel data to train.
Do multilingual models always need to be trained from scratch? Can we transfer linguistic knowledge learned by English pre-trained models to other languages? In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer. Our main contributions in this work are:
We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
We evaluate our bilingual LMs for six languages on two zero-shot cross-lingual transfer tasks, namely natural language inference BIBREF9 and universal dependency parsing. We show that our models offer competitive performance or even better that mBERT.
We illustrate that our bilingual LMs can serve as an excellent feature extractor in supervised dependency parsing task.
To our best knowledge, there are only three publicly available multilingual pretrained models to date:
Answer: Only three.
Question: What test do the participants take part in to assess their vocabulary and language proficiency?
Evidence: As a linguistic assessment, the vocabulary and language proficiency of the participants was tested with the LexTALE test (Lexical Test for Advanced Learners of English, lemhofer2012introducing). This is an unspeeded lexical decision task designed for intermediate to highly proficient language users. The average LexTALE score over all participants was 88.54%. Moreover, we also report the scores the participants achieved with their answers to the reading comprehension control questions and their relation annotations. The detailed scores for all participants are also presented in Table TABREF4.
As a linguistic assessment, the vocabulary and language proficiency of the participants was tested with the LexTALE test (Lexical Test for Advanced Learners of English, Lemhofer and Broersma (2012)). This is an unspeeded lexical decision task designed for intermediate to highly proficient language users.
Answer: LexTALE test.
Question: What approach performs better in experiments global latent or sequence of fine-grained latent variables?
Evidence: Compare to baseline models, the GVT achieves relatively lower reconstruction PPL, which suggests that the global latent variable contains rich latent information (e.g., topic) for response generation. Meanwhile, the sequential latent variables of the SVT encode fine-grained latent information and further improve the reconstruction PPL.
FLOAT SELECTED: Table 1: Results of Variational Transformer compared to baselines on automatic and human evaluations.
Compare to baseline models, the GVT achieves relatively lower reconstruction PPL, which suggests that the global latent variable contains rich latent information (e.g., topic) for response generation. Meanwhile, the sequential latent variables of the SVT encode fine-grained latent information and further improve the reconstruction PPL.
FLOAT SELECTED: Table 1: Results of Variational Transformer compared to baselines on automatic and human evaluations.
Answer: PPL: SVT
Diversity: GVT
Embeddings Similarity: SVT
Human Evaluation: SVT
Question: Where were the sunspots first observed?
Answer: Galileo Galilei, Father of observational astronomy
Question: Extract the non-meat ingredients of the sandwich in the passage provided. Separate them with a pipe symbol.
Evidence: A typical Vietnamese roll or sandwich is a fusion of meats and vegetables from native Vietnamese cuisine such as chả lụa (pork sausage), coriander leaf (cilantro), cucumber, pickled carrots, and pickled daikon combined with condiments from French cuisine such as pâté, along with red chili and buttery mayonnaise. However, a wide variety of popular fillings are used, from xíu mại (a Chinese cuisine) to even ice cream. In Vietnam, bread rolls and sandwiches are typically eaten for breakfast or as a snack.
Answer: cilantro | cucumber | picked carrots | pickled daikon | red chili | mayonnaise
Question: Which team has drafted Nkem-Nkechukwu Ojougboh?
Evidence: Nkem-Nkechukwu Ojougboh (born August 11, 1987) is Nigerian professional basketball player. He was drafted in the second round (ninth pick) at the 2010 NBA Development League Draft by the Utah Flash, an affiliate of the Utah Jazz and Atlanta Hawks. Collegiately, Ojougboh played for the Northeastern University Huskies men's basketball team from 2007-2010 and for the University of Texas San Antonio in 2005. He played for the NBA Development League team Utah Flash in the 2010/11 NBA D-League season, and for the Tulsa 66ers in 2011/12. Ojougboh was named to the CAA All-Academic First Team from 2005-2008. Ojougboh is the son of Cairo Ojougboh and Grace Ojougboh. He has two brothers Omam Ojougboh, Orieka Ojougboh and a sister Rimma Ojougboh. Out of high school, Ojougboh committed to the University of Texas at San Antonio, after also being recruited by Boise State, Cornell University, Harvard University, Arizona State University and Washington State. He transferred to Northeastern after his Freshman campaign at University of Texas San Antonio.
Answer: Nkem-Nkechukwu Ojougboh was drafted by the Utah Flash in 2010.
Question: Summarize Roosevelt's accomplishments described in this paragraph.
Evidence: Beginning in 1925, Roosevelt spent most of his time in the Southern United States, at first on his houseboat, the Larooco. Intrigued by the potential benefits of hydrotherapy, he established a rehabilitation center at Warm Springs, Georgia, in 1926. To create the rehabilitation center, he assembled a staff of physical therapists and used most of his inheritance to purchase the Merriweather Inn. In 1938, he founded the National Foundation for Infantile Paralysis, leading to the development of polio vaccines.
Answer: Roosevelt established a rehabilitation center in Georgia and founded the National Foundation for Infantile Paralysis. The latter accomplishment led to the development of polio vaccines.
Question: How much improvement does jointly learning QA and QG give, compared to only training QA?
Evidence: Evaluation results are provided in Table 1 . We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points. Performance of q-gen worsens after joint training, but the decrease is relatively small. Furthermore, as pointed out by earlier studies, automatic metrics often do not correlate well with the generation quality assessed by humans BIBREF9 . We thus consider the overall outcome to be positive.
We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points.
Answer: The answers are shown as follows:
* We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points.