DataHammer/scimrc huggingface dataset:
Question: How does the paper explore the cross-lingual ability of multi-BERT?
Evidence: Reading Comprehension (RC) has become a central task in natural language processing, with great practical value in various industries. In recent years, many large-scale RC datasets in English BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 have nourished the development of numerous powerful and diverse RC models BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11. The state-of-the-art model BIBREF12 on SQuAD, one of the most widely used RC benchmarks, even surpasses human-level performance. Nonetheless, RC on languages other than English has been limited due to the absence of sufficient training data. Although some efforts have been made to create RC datasets for Chinese BIBREF13, BIBREF14 and Korean BIBREF15, it is not feasible to collect RC datasets for every language since annotation efforts to collect a new RC dataset are often far from trivial. Therefore, the setup of transfer learning, especially zero-shot learning, is of extraordinary importance.
Existing methods BIBREF16 of cross-lingual transfer learning on RC datasets often count on machine translation (MT) to translate data from source language into target language, or vice versa. These methods may not require a well-annotated RC dataset for the target language, whereas a high-quality MT model is needed as a trade-off, which might not be available when it comes to low-resource languages.
In this paper, we leverage pre-trained multilingual language representation, for example, BERT learned from multilingual un-annotated sentences (multi-BERT), in cross-lingual zero-shot RC. We fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair to explore the cross-lingual ability of multi-BERT. Surprisingly, we find that the models have the ability to transfer between low lexical similarity language pair, such as English and Chinese. Recent studies BIBREF17, BIBREF12, BIBREF18 show that cross-lingual language models have the ability to enable preliminary zero-shot transfer on simple natural language understanding tasks, but zero-shot transfer of RC has not been studied. To our knowledge, this is the first work systematically exploring the cross-lingual transferring ability of multi-BERT on RC tasks.
We fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair to explore the cross-lingual ability of multi-BERT.
Answer: They fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair.
Question: Classify these foods as vegan, vegetarian or neither: frittata, yogurt, apple, lard, chicken noodle soup, tofu, flour, cheesecake
Answer: frittata: vegetarian, yogurt: vegetarian, apple: vegan, lard: neither, chicken noodle soup: neither, tofu: vegan, flour: vegan, cheesecake: vegetarian
Question: Why did the Seattle Supersonics leave Seattle?
Answer: In 2006 the owner of the team, Howard Schultz, sold the team Clay Bennett. Clay Bennett originally agreed to keep the team in Seattle, however, he then told the city that if a new arena was not built using public funds that he would move the team. Ultimately, public funding for the arena fell through and the team was moved to Oklahoma. The team is now known at the Oklahoma City Thunder. The relocation of the team was very controversial as local news organizations and fans believe that Clay Bennett's intention was to remove the team from Seattle and did not provide reasonable terms for a renovation of the basketball arena.
Question: What are common gaming consoles used to play video games?
Answer: Common gaming consoles used to play video games include personal computers, Xbox, and Playstation
Question: Randy Couture was one of how many fighters over the age of 40 to have won a UFC championship?
Evidence: Couture has competed in a record 16 title fights. He is tied for the record for the most wins in UFC Heavyweight Championship bouts (6) with former UFC Heavyweight champion Stipe Miocic. He had the most title reigns in the UFC, with six. His last fight with Lyoto Machida marked his 24th fight in the UFC. He is the fourth member of the UFC Hall of Fame. He is one of only 2 over the age of 40 to have won a UFC championship fight, having done so four times. He is one of the few MMA champions to regain a title he had lost, and the only to have done it three times (twice at heavyweight, once at light heavyweight).
Answer: Randy Couture was one of two fighters over the age of 40 that won a UFC championship fight.
Question: What is a Goldendoodle?
Evidence: The Goldendoodle is a designer dog created by crossbreeding a Golden Retriever and a Poodle. First widely bred in the 1990s, they are bred in three different sizes—each corresponding to the size of Poodle used as a parent.

Goldendoodles often demonstrate Golden Retrievers' intuitive and human-oriented nature in addition to the intelligent personality and "allergy-friendly" coat of a Poodle.

In the 1990s designer dog breeders in Australia and the United States (Ryan Harvey) started to widely cross Golden Retrievers and Poodles, adopting the term Goldendoodle to describe the cross. The name Doodle is also used to describe this breed. The name Goldendoodle is derived from "Golden" (from Golden Retriever) and "Labradoodle" (itself a portmanteau of Labrador Retriever and a Poodle). Deliberately breeding Poodles with Golden Retrievers actually preceded the 1990s. An example of this was done by Monica Dickens, the great-granddaughter of Charles Dickens, who crossed the two breeds in 1969. The cross can result in a healthier animal than both parent breeds.

The first litter of Mini Goldendoodles was created by Amy Lane of Fox Creek Farm in Berkeley Springs, WV. The litter arrived on 1/11/02 and consisted of 10 puppies.[citation needed] Two of these puppies were retained for future breeding and one of them produced the first litter of F1B Mini Goldendoodles. The term "Goldendoodle" was coined by Amy Lane.[citation needed] Amy Lane also created the first, and to this date, the only kennel club for Goldendoodles called the Goldendoodle Association of North America, Inc. This is a non-profit organization that guides the development of the Goldendoodle and also provides a registration service to document the lineage of Goldendoodles.[citation needed]

The original aim of the Goldendoodle's breeders was to produce an alternative to the popular Labradoodle. Initially Goldendoodles were only bred from standard-sized Poodles, but designer breeders also started using small varieties of Poodles to create a smaller crossbreed. As they are a hybrid cross, they are not recognized by the AKC or British Kennel Clubs. Goldendoodles have been developed beyond the F1 (Golden Retriever crossed with a Poodle) and are now available in deeper generations created by crossing two Goldendoodles. These are called multigenerational or multigen Goldendoodles.
Answer: A Goldendoodle is a crossbreed between a poodle and a golden retriever. Goldendoodles are so popular due to them being a hypoallergenic dog. Typically goldendooles have the personality of a Golden retriever and the hair and intelligent personality from a poodle.
Question: Where is Budhor Kalan
Answer: Budhor Kalan is a village in the Bhopal district of Madhya Pradesh, India. It is located in the Berasia tehsil.

Demographics
According to the 2011 census of India, Budhor Kalan has 144 households. The effective literacy rate (i.e. the literacy rate of population excluding children aged 6 and below) is 66.83%.
Question: What do the authors investigate in the paper?
Evidence: Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques.
Answer: The role of shallow syntax-aware representations for NLP tasks using two techniques.
Question: What is the difference between a quartz and a mechanical watch?
Answer: Both quartz and mechanical watches are accurate approaches to telling the time. However, mechanical watches were the original technology used in watchmaking, dating back to the 18th century.

Mechanical movements are intricate pieces of machinery that use the energy from a slowly unwinding spring to accurately tell the time. They are prized for their artistry, beauty and heritage and often command high prices today through luxury brands such as Rolex, Patek Phillipe and many others.

Quartz watches, by comparison, send an electrical current through a quartz crystal, which causes regular vibrations that can be used to tell the time. This is why quartz watches require a battery. They are significantly less expensive that mechanical watches, and significantly more accurate due to the regularity of the vibration in the quartz crystal. Quartz watches are also distinguished at a glance by their ticking seconds hands, whereas mechanical watches have a smooth sweeping seconds hand.
Question: What is the development corpus for each language composed of?
Evidence: In this work, we propose a new evaluation framework for highly multilingual document classification which significantly extends the current state. We continue to use Reuters Corpus Volume 2, but based on the above mentioned limitations of the current subset of RCV2, we propose new tasks for cross-lingual document classification. The design choices are as follow:
Uniform class coverage: we sample from RCV2 the same number of examples for each class and language;
Split the data into train, development and test corpus: for each languages, we provide training data of different sizes (1k, 2k, 5k and 10k stories), a development (1k) and a test corpus (4k);
Support more languages: German (DE), English (EN), Spanish (ES), French (FR), Italian (IT), Japanese (JA), Russian (RU) and Chinese (ZH). Reference baseline results are available for all languages.
Most works in the literature use only 1 000 examples to train the document classifier. To invest the impact of more training data, we also provide training corpora of 2 000, 5 000 and 10 000 documents. The development corpus for each language is composed of 1 000 and the test set of 4 000 documents respectively. All have uniform class distributions. An important aspect of this work is to provide a framework to study and evaluate cross-lingual document classification for many language pairs. In that spirit, we will name this corpus “Multilingual Document Classification Corpus”, abbreviated as MLDoc. The full Reuters Corpus Volume 2 has a special license and we can not distribute it ourselves. Instead, we provide tools to extract all the subsets of MLDoc at https://github.com/facebookresearch/MLDoc.
The development corpus for each language is composed of 1 000 and the test set of 4 000 documents respectively.
Answer: The development corpus for each language is composed of 1 000 and the test set of 4 000 documents respectively.
Question: Which structured prediction approach do they adopt for temporal entity extraction?
Evidence: We examine a deep-learning approach to sequence labeling using a vanilla recurrent neural network (RNN) with word embeddings, as well as a joint inference, structured prediction approach using Stanford's knowledge base construction framework DeepDive BIBREF1 . Our DeepDive application outperformed the RNN and scored similarly to 2015's best-in-class extraction systems, even though it only used a small set of context window and dictionary features. Extraction performance, however lagged this year's best system submission. For document creation time relations, we again use DeepDive. Our system examined a simple temporal distant supervision rule for labeling time expressions and linking them to nearby event mentions via inference rules. Overall system performance was better than this year's median submission, but again fell short of the best system.
We examine a deep-learning approach to sequence labeling using a vanilla recurrent neural network (RNN) with word embeddings, as well as a joint inference, structured prediction approach using Stanford's knowledge base construction framework DeepDive BIBREF1 .
Answer: The answers are shown as follows:
* DeepDive BIBREF1
Question: Why is LaSalle Street in Chicago important?
Answer: There could be many answers to this question as many people, businesses, and organizations call LaSalle Street their home and various historical events have transpired on and around LaSalle Street in Chicago. A notable usage of LaSalle Street was in the filming of Batman: The Dark Knight and Ferris Bueller's Day Off. LaSalle Street is home to the Chicago Board of Trade building which is a center of commerce and finance for the city of Chicago and the international financial markets.
Question: What are the domains covered in the dataset?
Evidence: The 17 domains (`Alarm' domain not included in training) present in our dataset are listed in Table TABREF5. We create synthetic implementations of a total of 34 services or APIs over these domains. Our simulator framework interacts with these services to generate dialogue outlines, which are a structured representation of dialogue semantics. We then used a crowd-sourcing procedure to paraphrase these outlines to natural language utterances. Our novel crowd-sourcing procedure preserves all annotations obtained from the simulator and does not require any extra annotations after dialogue collection. In this section, we describe these steps in detail and then present analyses of the collected dataset.
FLOAT SELECTED: Table 2: The number of intents (services in parentheses) and dialogues for each domain in the train and dev sets. Multidomain dialogues contribute to counts of each domain. The domain Service includes salons, dentists, doctors etc.
The 17 domains (`Alarm' domain not included in training) present in our dataset are listed in Table TABREF5. We create synthetic implementations of a total of 34 services or APIs over these domains. 
FLOAT SELECTED: Table 2: The number of intents (services in parentheses) and dialogues for each domain in the train and dev sets. Multidomain dialogues contribute to counts of each domain. The domain Service includes salons, dentists, doctors etc.
Answer: Alarm
Bank
Bus
Calendar
Event
Flight
Home
Hotel
Media
Movie
Music
RentalCar
Restaurant
RideShare
Service
Travel
Weather
Question: What are books are available to get an understanding of California prior to the gold-rush of 1849?
Answer: A great resource for pre-Gold Rush California is the book "Two Years Before the Mast" by Richard Henry Dana who stepped away from Harvard Law School and signed up for a tour on the merchant vessel out of Boston in 1834. The book describes the voyage around the treacherous Cape Horn under the leadership of a hard and driving captain who shows little remorse or regard for his crew as the ship is battered below the cape by the confluence of the Atlantic and Pacific oceans. Awareness of Ahab of Melville's Moby Dick cannot help but inform your mental picture of this captain. 

The ship makes its way up the coast, passing the newly independent countries of South and central America, to the northern part of Mexico, an area called Alta California. Traveling along the coast, the ship trades with the Spanish and indigenous peoples in tiny settlements and missions named San Diego, San Pedro, San Juan, Nuestra Señora La Reina de los Angeles de Porciuncula, Santa Barbara, Monterey and north to San Francisco Bay. 

Dana's detailed descriptions of the natural harborages and the tiny settlements and missions are richly described and contrast sharply with the California we see today. One is left with the singular impression of the remoteness of California to those traveling from east cost of North America or Europe.  

The crew was trading in hides and tallow, incredible to those reading from contemporary times, who consider the distance of the journey and the archaicness of the cargo. The trading goods seem altogether more ancient when considering the change the entire area would undergo in 1848 at Sutter's Mill.
Question: Do they enumerate automatic quality measures used in experiments?
Evidence: Currently, probabilistic topic models are important tools for improving automatic text processing including information retrieval, text categorization, summarization, etc. Besides, they can be useful in supporting expert analysis of document collections, news flows, or large volumes of messages in social networks BIBREF0 , BIBREF1 , BIBREF2 . To facilitate this analysis, such approaches as automatic topic labeling and various visualization techniques have been proposed BIBREF1 , BIBREF3 .
Boyd-Graber et al. BIBREF4 indicate that to be understandable by humans, topics should be specific, coherent, and informative. Relationships between the topic components can be inferred. In BIBREF1 four topic visualization approaches are compared. The authors of the experiment concluded that manual topic labels include a considerable number of phrases; users prefer shorter labels with more general words and tend to incorporate phrases and more generic terminology when using more complex network graph. Blei and Lafferty BIBREF3 visualize topics with ngrams consisting of words mentioned in these topics. These works show that phrases and knowledge about hyponyms/hypernyms are important for topic representation.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics. The structure of the paper is as follows. In Section 2 we consider related works. Section 3 describes the proposed approach. Section 4 enumerates automatic quality measures used in experiments. Section 5 presents the results obtained on several text collections according to automatic measures. Section 6 describes the results of manual evaluation of combined topic models for Islam Internet-site thematic analysis.
Section 4 enumerates automatic quality measures used in experiments.
Answer: Yes, in Section four.
Question: why do you like India
Answer: I like India because it is a country of diverse culture with more than 25 states thousands of language spoken and with many religions.You get to witness diverse food culture and religious festivals across.With all these diversity you can still see a unity within the people that is the beauty of the country.
Question: Can you give examples of extreme conditions for habitation?
Answer: Any environment not favorable to live or makes it hard to survive due to a lack of or limited availability of basic resources needed for living is an extreme conditions. There are many such places on planet Earth: the north and south pole with extreme cold, the deserts with extreme heat or ver high temperatures, very high altitudes with drastic weather patterns are good examples of extreme conditions for habitation.
Question: Why do potato chip bags become stale after opening?
Answer: Potato chip bags are filled with nitrogen! Many consumers think the chip companies are out there to get your money by filling three quarters of your bag with air, but it’s actually nitrogen to keep your chips fresh. This is why once you open the bag, you should finish the chips as soon as possible. There, now you have a reason to finish the whole bag of chips in one go without feeling guilty.
Question: What evaluation metric is used?
Evidence: During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks. The length penalty is set to 0.8 for En-Fr according to the validation results, 1 for the two small datasets following the default setting of BIBREF14. We do not tune beam width and length penalty but use the setting reported in BIBREF0. The BLEU metric is adopted to evaluate the model performance during evaluation.
The BLEU metric is adopted to evaluate the model performance during evaluation.
Answer: The answers are shown as follows:
* The BLEU metric
Question: Explain Ionizing radiation as a twelve years old kid.
Answer: Ionizing radiation is caused when a very heavy atom emits a particle at the speed of light called ionizing radiation.
On contact with a material, either the atom absorbs the ionizing particle or 
The ionizing particle breaks the atom in two and forms two ionizing particles which can create a chain reaction.
There are three kinds of ionizing particles: alpha, beta and gamma
Alpha particles react a lot with matter but travel a very short distance.

Beta particles react less than alpha particles with matter but travel a very short distance. 
Alpha particles react less than alpha particles with matter but travel a longer distance than alpha particles.

Gamma particles react less with matter than beta particles but travel a longer distance than beta particles.
Question: If I have auditioned for and accepted a role in a local, community theater production, is it acceptable to drop the show later if something else comes along I like better?
Answer: No, even small, local theaters take role acceptance very seriously. There is no contract, so they cannot keep you from dropping out, but doing so often will burn your bridges with the producing and artist team. Choose your projects carefully, and keep the lines of communication open. If you have auditioned for more than one show, it is possible to ask to wait to accept the role until you have heard back from both opportunities. However, the offer  of a role may be rescinded if you don't accept right away. Most companies will not wait longer than a few hours/few days to hear back whether you will accept the role or not. Once accepted, dropping out of the show for any reason other than personal or family emergencies or health situations is generally frowned upon.
Question: Classify each of the following as either as a country or state: California, Texas, Oklahoma, New York, Illinois.
Answer: These are classified as states.
Question: When was the Tesla Factory in Fremont purchased by Tesla?
Answer: In May 2010
Question: Why Bollywood is so famous
Answer: Because of all famous actor and female actor and their famous Bollywood movies which earns billions of Rupees
Question: Are recurrent neural networks trained on perturbed data?
Evidence: For evaluation, we used the widely popular dialog act and intent prediction datasets. MRDA BIBREF12 is a dialog corpus of multi-party meetings with 6 classes, 78K training and 15K test data; ATIS BIBREF13 is intent prediction dataset for flight reservations with 21 classes, 4.4K training and 893 test examples; and SWDA BIBREF14, BIBREF15 is an open domain dialog corpus between two speakers with 42 classes, 193K training and 5K test examples. For fair comparison, we train LSTM baseline with sub-words and 240 vocabulary size on MRDA, ATIS and SWDA. We uniformly randomly initialized the input word embeddings. We also trained the on-device SGNN model BIBREF6. Then, we created test sets with varying levels of perturbation operations - $\lbrace 20\%,40\%,60\%\rbrace $.
Then, we created test sets with varying levels of perturbation operations - $\lbrace 20\%,40\%,60\%\rbrace $.
Answer: No.