DataHammer/scimrc huggingface dataset:
Question: How do they incorporate lexicon into the neural network?
Evidence: The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus.
The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus.
Answer: The answers are shown as follows:
* concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation
* cannot directly concatenate
*  re-build the latter in token-based form
Question: What numerical experiments they perform?
Evidence: The aim of this section is to validate the applicability of our theoretical results—which state that self-attention can perform convolution—and to examine whether self-attention layers in practice do actually learn to operate like convolutional layers, when being trained on standard image classification tasks. In particular, we study the relationship between self-attention and convolution with quadratic and learned relative positional encodings. We find that for both cases, the attention probabilities learned tend to respect the conditions of Lemma UNKREF15, corroborating our hypothesis.
We study a fully attentional model consisting of six multi-head self-attention layers. As it has already been shown by BIBREF9 that combining attention features with convolutional features improves performance on Cifar-100 and ImageNet, we do not focus on attaining state-of-the-art performance. Nevertheless, to validate that our model learns a meaningful classifier we compare it to the standard ResNet18 BIBREF14 on the CIFAR-10 dataset BIBREF15. In all experiments, we use a $2\times 2$ invertible down-sampling BIBREF16 on the input to reduce the size of the image as storing the attention coefficient tensor requires a large amount of GPU memory. The fixed size representation of the input image is computed as the average pooling of the last layer representations and given to a linear classifier.
The aim of this section is to validate the applicability of our theoretical results—which state that self-attention can perform convolution—and to examine whether self-attention layers in practice do actually learn to operate like convolutional layers, when being trained on standard image classification tasks. In particular, we study the relationship between self-attention and convolution with quadratic and learned relative positional encodings. We find that for both cases, the attention probabilities learned tend to respect the conditions of Lemma UNKREF15, corroborating our hypothesis.
Nevertheless, to validate that our model learns a meaningful classifier we compare it to the standard ResNet18 BIBREF14 on the CIFAR-10 dataset BIBREF15.
Answer: The answers are shown as follows:
* attention probabilities learned tend to respect the conditions of Lemma UNKREF15, corroborating our hypothesis
* validate that our model learns a meaningful classifier we compare it to the standard ResNet18
Question: What is a pot hole?
Answer: A pot hole is the part of a road that has become damaged with a large enough interruption in the road the the driver of a vehicle can feel a jarring impact when the vehicle travels over that area. Cities try to fill in pot holes because they can damage vehicles as when they become too large.
Question: Where did Gretel attend primary school?
Evidence: Gretel Beer was born in Vienna into a Jewish family. She was mostly raised by her aunt Olga Springer (Bechin, Bohemia 1879–1942 Maly Trostenets extermination camp) the widow of a physician (in 1937: 9th district, Porzellangasse 45), as her mother Regina Weidenfeld née Pisk died when Margaret was six years old and her father, Dionys (Duny) Weidenfeld, did not keep up a household. (At Porzellangasse, until 1938 Eric Pleskow and Ari Rath spent their childhood, as they told the Austrian broadcaster ORF in 2012.) After attending primary school at Marchegg, a small town east of Vienna near the border with Slovakia, she attended a federal Realschule at Vereinsgasse in Vienna's 2nd district, where many Jewish Viennese lived.

In the spring of 1938, after the annexation of Austria by Germany, she and 48 other pupils were forced to leave this school and attend a Jewish class elsewhere in Vienna. At the entrance hall of her school, which is now called Bundesrealgymnasium Vereinsgasse, since 1989 the names of the expelled pupils are displayed on a memorial inscription.
Answer: Gretel Beer attended primary school in Marchegg, in east of Vienna.
Question: What is the hero WoD called Murph?
Answer: The workout is done in memory to Michael Murphy who died in Afghanistan in June 2005.  He was only 29 years old.  The workout is modeled after his favorite workout call "Body Armor".  It consists of running, pullups, pushups and airsquats.  The workout is done for time.  It starts with a 1-mile run followed by 100 pullups, 200 pushups and 300 airsquats and ends with another 1-mile run.  Most will do the workout while wearing a 20# vest but is not required.  The pullups, pushups and airsquats can be partitioned into sets if needed.
Question: When was hailstorm island first photographed given this text
Evidence: Hailstorm Island is a rocky island, 0.5 kilometres (0.25 nmi) long, between Cameron Island and the east end of Burnett Island in the central part of the Swain Islands, Antarctica. It was first roughly mapped from air photos taken by U.S. Navy Operation Highjump, 1946–47, and included in a 1957 survey of the Swain Islands by Wilkes Station personnel under Carl R. Eklund. It was named by Eklund for Radioman Kenneth J. Hailstorm, U.S. Navy, a Naval support force member of the 1957 wintering party at Wilkes Station during the International Geophysical Year.
Answer: 1946
Question: When did Harriet live in the Andaman Islands?
Evidence: The Tytlers were introduced to photography by Felix Beato and Dr John Murray of Agra. In May 1857 the couple were resident in the military cantonments outside Delhi, where Robert Tytler's regiment, the 38th Bengal Native Infantry was one of the first to mutiny during the great rebellion of that year. Because she was heavily pregnant, Harriet was allowed to stay on and was the only British woman present at the Siege of Delhi. She later gave birth in a donkey cart while escaping to safer areas. They named this third child Stanley Delhi-Force Tytler. From 1862 to 1864 she lived in the Andaman Islands where her husband was posted as a Superintendent of the Ross Island Penal Colony, a Penal Settlement (which was established mainly to house prisoners from the 1857 uprising). The region around a 1100 foot high hill in the South Andamans, which was named after her as Mt. Harriet(now known as mount manipur), was cleared of forest by Tytler. The area is now protected as the Mount Harriet National Park. Tytler's tenure in the Andamans was short and they moved to Shimla. In Shimla, Harriet founded an Asiatic Christian Orphanage. She died in Shimla on 24 November 1907.
Answer: Between 1862 and 1864.
Question: Why do people like pickleball?
Answer: Pickleball is one of the fastest growing sports today. People of all ages and fitness can play pickleball. Pickball is played on a court that is smaller than a tennis course so requires less movement. Pickleball is also more of a finesse game vs. power.
Question: How is a distiller used?
Answer: A distiller is used in the production of alcohol or hard liquor.  It works by using heat to cause evaporation and condensation with a source substance.  If you take water for example, distilling happens when you heat the water and then it evaporates into steam and then the steam is re-condensed.  This re-condensed steam is distilled water.  This same process can be applied to other raw foods than have been fermenting to produce clear alcohol.
Question: Write me a message to send to a candidate recently admitted to business school from a second year MBA
Answer: Congratulations on your admission to our program. I hope you are doing some well-deserved celebrating!!

I am finishing up my second year in my MBA and will graduate with fond memories of the community and people. Since day 1, I felt so much support and guidance throughout the recruitment process. 

I would love to be a resource for you as you make your decision. Please feel free to email me here or text/call me.
Question: What is the third limb of yoga?
Evidence: sanas originated in India. In his Yoga Sutras, Patanjali (c. 2nd to 4th century CE) describes asana practice as the third of the eight limbs (Sanskrit: अष्टाङ्ग, aṣṭāṅga, from अष्ट् aṣṭ, eight, and अङ्ग aṅga, limb) of classical, or raja yoga.
Answer: The third limb of yoga is know as Asana.  This refers to the physical practice of yoga, or the yoga postures.  By putting the body into difficult positions you achieve several key benefits. The first is you are helping your body heal pain.  If you want to meditate and work on healing your mind, you need to first heal your body.  Otherwise your easily distracted mind will focus on pain in your body versus the pain in your mind.  Secondly, commitment to the Asana will teach you the discipline your mind needs to heal.  When going into uncomfortable situations, bad patterns in the mind often arise.  Stepping into discomfort actually becomes an opportunity to study the mind and change patterns of response.
Question: Is the performance improved in learning the front end?
Evidence: Recent work on convolutional neural network architectures showed that they are competitive with recurrent architectures even on tasks where modeling long-range dependencies is critical, such as language modeling BIBREF0 , machine translation BIBREF1 , BIBREF2 and speech synthesis BIBREF3 . In end-to-end speech recognition however, recurrent architectures are still prevalent for acoustic and/or language modeling BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 .
There is a history of using convolutional networks in speech recognition, but only as part of an otherwise more traditional pipeline. They have been first introduced as TDNNs to predict phoneme classes BIBREF9 , and later to generate HMM posteriorgrams BIBREF10 . They have more recently been used in end-to-end frameworks, but only in combination with recurrent layers BIBREF6 , or n-gram language models BIBREF11 , or for phone recognition BIBREF12 , BIBREF13 . Nonetheless, convolutional architectures are prevalent when learning from the raw waveform BIBREF14 , BIBREF15 , BIBREF16 , BIBREF13 , BIBREF17 , because they naturally model the computation of standard features such as mel-filterbanks. Given the evidence that they are also suitable on long-range dependency tasks, we expect convolutional neural networks to be competitive at all levels of the speech recognition pipeline.
In this paper, we present a fully convolutional approach to end-to-end speech recognition. Building on recent advances in convolutional learnable front-ends for speech BIBREF13 , BIBREF17 , convolutional acoustic models BIBREF11 , and convolutional language models BIBREF0 , the paper has four main contributions:
In addition, we complement the promising results of [18] regarding the performance of learning the front-end of speech recognition systems: first, we show that learning the front-end yields substantial improvements on noisy speech compared to a mel-filterbanks front-end.
Answer: Yes.
Question: List some common citrus-based beverages.
Answer: Some common citrus-based beverages include orange juice, grapefruit juice, lemonade, Mountain Dew, mimosas
Question: Give me the Seven Wonders of the Industrial World:
Answer: Seven Wonders of the Industrial World:

- SS Great Eastern, British oceangoing passenger steamship, launched in 1858
- Bell Rock Lighthouse, in the North Sea off the coast of Angus, Scotland, completed in 1810
- Brooklyn Bridge, in New York City, New York, United States, opened to traffic in 1883
- London sewerage system, serving London, England since the late 19th century
- First transcontinental railroad, 1,912-mile (3,077 km) continuous railroad line connecting existing rail networks in Iowa, Nebraska, Wyoming, Utah, Nevada, and California in the United States, completed in 1869
- Panama Canal, 51-mile (82 km) artificial waterway crossing the Isthmus of Panama and connecting the Atlantic and Pacific oceans, completed in 1914
- Hoover Dam, on the Colorado River, spanning the border between Nevada and Arizona in the United States, completed in 1936
Question: Based on this paragraph about San Diego, what is the largest city in the state of california?
Evidence: San Diego (Spanish for 'Saint Didacus'; /ˌsæn diˈeɪɡoʊ/ SAN dee-AY-goh, Spanish: [san ˈdjeɣo]) is a city on the Pacific Ocean coast of Southern California located immediately adjacent to the Mexico–United States border. With a 2020 population of 1,386,932, it is the eighth most populous city in the United States and the seat of San Diego County, the fifth most populous county in the United States, with 3,286,069 estimated residents as of 2021. The city is known for its mild year-round Mediterranean climate, natural deep-water harbor, extensive beaches and parks, long association with the United States Navy, and recent emergence as a healthcare and biotechnology development center. San Diego is the second largest city in the state of California after Los Angeles.
Answer: Los Angeles
Question: What is the difference between a paint and a pinto?
Answer: A paint horse is a breed called the "American Paint Horse" with bloodlines from registered Quarter Horses, Thoroughbreds or Arabian horses with bold two-color markings and a pinto is a loose term used to describe any horse with bold white markings on its coat but can be any breed.
Question: How many coaches (full and part-time) from The Voice have won Grammys?
Answer: 12
Question: What approach is used to detect and classify events?
Evidence: Current societies are exposed to a continuous flow of information that results in a large production of data (e.g. news articles, micro-blogs, social media posts, among others), at different moments in time. In addition to this, the consumption of information has dramatically changed: more and more people directly access information through social media platforms (e.g. Facebook and Twitter), and are less and less exposed to a diversity of perspectives and opinions. The combination of these factors may easily result in information overload and impenetrable “filter bubbles”. Events, i.e. things that happen or hold as true in the world, are the basic components of such data stream. Being able to correctly identify and classify them plays a major role to develop robust solutions to deal with the current stream of data (e.g. the storyline framework BIBREF0 ), as well to improve the performance of many Natural Language Processing (NLP) applications such as automatic summarization and question answering (Q.A.).
Event detection and classification has seen a growing interest in the NLP community thanks to the availability of annotated corpora BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 and evaluation campaigns BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . In the context of the 2014 EVALITA Workshop, the EVENTI evaluation exercise BIBREF11 was organized to promote research in Italian Temporal Processing, of which event detection and classification is a core subtask.
Since the EVENTI campaign, there has been a lack of further research, especially in the application of deep learning models to this task in Italian. The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier. The pre-trained models and scripts running the system (or re-train it) are publicly available. .
The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier.
Answer: A single step approach.
Question: How many hours does it take to drive 12 miles on this road?
Evidence: TELLURIDE ——>
CITY OF GOLD
12 MILES - 2 HOURS
YOU DON'T HAVE TO BE
CRAZY TO DRIVE THIS
ROAD - BUT IT HELPS

JEEPS ONLY
Answer: 2 hours
Question: Does taking long walks do anything good for my health?
Answer: Exercise is very important in your daily lifestyle for overall health and well-being.  A 15 minute brisk walk each day can do so many good things for your including keeping your heart healthy and lowering blood pressure.  It will help keep your weight in balance, and also give your more energy throughout the day.  A brisk walk in the morning is a great way to wake up without the need for caffeine to get going.  It can also help you lose weight and may help to prevent stroke and type 2 diabetes.
Question: Tell me which of the following countries as considered SAARC countries: Afghanistan, Bangladesh, Bhutan, India, United States of America, United Kingdom, China, Singapore
Answer: Afghanistan, Bangladesh, Bhutan and India are part of the SAARC eight member countries. United States of America, United Kingdom, China and Singapore are not affiliated to SAARC countries.
Question: Where can you find the best bagel in the world?
Answer: Montreal bagels are the best in the world. The real magic is that Montréal bagels are baked in a wood-fired oven and the bakers simmer their dough in honey water, giving it a slight sweetness.
Question: What method do the authors propose?
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.
We propose a fast adaptation method for obtaining a bilingual BERTBASE of English and a target language within a day using one Tesla V100 16GB GPU (§3).
Answer: A fast adaptation method.
Question: What country is to the north of the United States?
Answer: Canada is America's northern neighbor
Question: What year was Kerala formed?
Evidence: Kerala (English: /ˈkɛrələ/ KERR-ə-lə; Malayalam: [ke:ɾɐɭɐm] (listen)) is a state on the Malabar Coast of India. It was formed on 1 November 1956, following the passage of the States Reorganisation Act, by combining Malayalam-speaking regions of the erstwhile regions of Cochin, Malabar, South Canara, and Travancore. Spread over 38,863 km2 (15,005 sq mi), Kerala is the 21st largest Indian state by area. It is bordered by Karnataka to the north and northeast, Tamil Nadu to the east and south, and the Lakshadweep Sea to the west. With 33 million inhabitants as per the 2011 census, Kerala is the 13th-largest Indian state by population. It is divided into 14 districts with the capital being Thiruvananthapuram. Malayalam is the most widely spoken language and is also the official language of the state.
Answer: 1956
Question: When was the Bitcoin whitepaper published?
Answer: The Bitcoin whitepaper was published on October 31, 2008.