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1808.10113
| true
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We compared our models with the following state-of-the-art baselines:
Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .
Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.
HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.
HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.
HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge.
We compared our models with the following state-of-the-art baselines:
Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .
Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.
HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.
HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.
HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge.
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Did they compare to Transformer based large language models?
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No.
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What can I do with a broken stapler?
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- Use the broken stapler as a paper weight
- Use the broken stapler to hold staples for a new stapler
- Use the broken stapler to hold a door ajar
- Use the broken stapler to hold paper that needs to be filed
- Use the broken stapler as a nutcracker
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1704.02686
| true
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When considering going from two dimensions to three, it is perhaps necessary to discuss the computational issues in such a problem size increase. However, it should be noted that the creation of pre-trained embeddings can be seen as a pre-processing step for many future NLP tasks, so if the training can be completed once, it can be used forever thereafter without having to take training time into account. Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively.
Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively.
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Do they measure computation time of their factorizations compared to other word embeddings?
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Yes.
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What is the art of command
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It is the art of influencing human behavior in order to achieve the objective assigned by the superior.
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The Dark Side of the Moon is the eighth studio album by the English rock band Pink Floyd, released on 1 March 1973 by Harvest Records. Developed during live performances before recording began, it was conceived as a concept album that would focus on the pressures faced by the band during their arduous lifestyle, and also deal with the mental health problems of former band member Syd Barrett, who departed the group in 1968. New material was recorded in two sessions in 1972 and 1973 at EMI Studios (now Abbey Road Studios) in London.
|
What is The Dark Side of the Moon?
|
The Dark Side of the Moon is Pink Floyd's 8th studio album. It is about the band's difficult lifestyle and touches on the mental health problems of former band member Syd Barrett.
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Neville shows to Det. Delroy a recently shot love scene between Keefe and Elaine, willingly pointing out a spousicide-related detail which compromises the already delicate position of the young widower. In light of this new evidence, Delroy tells the director that he will arrest the young man very soon. Neville telephones Keefe to warn him, telling him to come to his house at a certain time. The director also calls Elaine to his home, where he attempts to recreate the night of Andrea's murder, this time using Elaine. Det. Vickers watches Elaine enter the director's house from the street, but he misses Keefe, who breaks in through a window. He disrupts the liaison by pulling plug fuses out of the breaker box, shutting off the lights. Detective Vickers notices the lights shut off from the street and unsuccessfully tries to break in. In the house, Neville attacks Keefe with a pair of scissors, and the two struggle. Keefe throws Neville over a balcony, and he lands in an indoor fountain below together with a light. Simultaneously, Elaine restores electricity to the house, unknowingly electrocuting Neville to death.
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Extract all unique names from the provided paragraphs separated by comma.
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Neville, Delroy, Keefe, Elaine, Andrea, Vickers
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What is the difference between a coastal cruising and blue water sailboat?
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The designs, accommodations and compromises for these boats can be very different and reflect the likely sea conditions these boats would encounter, expected time at sea and different experiences of passenger comfort. The primary difference between these boats begins with the shape of the hull since many other design choices tend to be dictated by hull shape.
Coastal cruisers, which are generally sailed near shore and in safer weather conditions,
tend to have a flatter hull shapes since these boats, which means they tend to "slap" the water when coming off a wave. With a flatter hull shape, boat architects can make greater use of the cabin space and so coastal cruisers will generally have a more spacious interior. The helm (or the outdoor area from which the boat is navigated) is generally less protected and generally speaking, offers greater comfort and ease of moving around. Safety, protection and expected time away from marinas mean these boats can refill fresh water, recharge batteries, empty waste tanks more frequently and therefore, tend to have lower capacities.
Blue water boats, because they are intended to sail in open waters (hence the moniker "blue water") in more unpredictable weather, generally have tapered (or tear drop shaped) hull shapes with heavier keels (generally, lead) and deeper drafts. With these designs, blue water boats can withstand heavier seas and will "cut through" waves rather than "slap." Generally speaking, blue water boats offer a much more comfortable experience in rougher seas and will have bigger fresh water and fuel tanks and will likely feature generators and other equipment that allows these boats to away for longer periods.
Blue water boats feature tapered hulls so the beam or width of the boat narrows the closer it gets to the water line (and below), leaving less room in the interior cabin than an equally sized coastal cruiser. Foot for foot, blue water boats will feature fewer berths and will have more cramped quarters because of the narrowing beam.
Because blue water boats are intended to sail longer distances and will carry more fuel and fresh water, these boats interior space is further restricted to accomadate these practical requirements.
The differences in comfort, stability and likelihood of capsizing are all captured in mathematical calculations that can provide an abstracted comparison between these boat types so prospective buyers can anticipate the the experiences and trade offs each hull type would provide.
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James Stuart Pressdee (19 June 1933 – 20 July 2016) was a Welsh first class cricketer. He was a left-arm spinner and aggressive right-handed batsman. He also played association football in the Football League and Welsh Football League where he played as a left-back.
Football Career
He won a Welsh youth cap at football and was on the books at Swansea Town where provided reliable defensive cover. He turned professional at the Vetch Field in August 1951. He was given his League debut in Swansea's 2–0 defeat at home to Everton in March 1954, making eight Second Division appearances before concentrating on his cricket career. He also had spells with Brecon Corinthians and Llanelly in the Welsh Football League.
Cricket Career
Pressdee made his debut for Glamorgan against Nottinghamshire at Cardiff Arms Park when just 16 years of age, making him the county's youngest player in the post-war period.
A regular in the Glamorgan team from 1955 onwards, Pressdee scored a thousand runs in a season on six occasions, with 1911 in 1962 being his best. He scored 13 centuries including one for North-Eastern Transvaal. Playing 322 games for Glamorgan CCC between 1949 and 1965.
Pressdee was an inconsistent bowler. Having taken 71 wickets in 1955, he captured only 54 during the next seven seasons, but then in 1963 and 1964 he came to prominence as an all-rounder, completing the double (1000 runs and 100 wickets) in each season. In 1965, he took 9 for 43 against Yorkshire at Swansea. Pressdee emigrated to South Africa in 1965 where he played for North-Eastern Transvaal until 1969–70. He won the South African Cricket Annual Cricketer of the Year award in 1966 after taking 33 wickets.
Pressdee returned to Wales in the 1980s and captained the Glamorgan Colts side in the South Wales Cricket Association.
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Given this text about James Pressdee, tell me what sports they played, which awards they won, when they were born, and if they are still alive.
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James Stuart Pressdee played both football and cricket. Though he was an inconsistent bowler, he won the South African Cricket Annual Cricketer of the Year award in 1966. Prior to his professional sports career, James won a Welsh youth cap in football in Swansea Town. Jame was born on June 19th, 1933, and he died on July 20th, 2016 at the age of 83.
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| 46
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We utilize fasttext BIBREF18 to generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD. We adopt the phoneme sequence embeddings to replace the original character sequence embeddings using the method proposed by Li et al. BIBREF5 . The source domain model and the target domain model share the same set of word embedding matrix to improve the alignment between these two domains.
W-GAN is adopted for our domain discriminator BIBREF19 , which stacks 5 residual blocks of 1D convolutional layers with 96 filters and filter size 5 followed by one linear layer to convert each input vector sequence into one scalar value.
All models used in the experiments are trained with batch size 20, using adam with learning rate INLINEFORM0 and the early stop strategy. The dimension of the hidden state is set to 96 for all layers, and the number of self-attention heads is set to 2. The setup is slightly different but better than the setting suggested by the original QAnet.
Spoken-SQuAD is chosen as the target domain data for train ing and testing.
|
What is chosen as the target domain data for training and testing?
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Spoken-SQuAD
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1810.04528
| false
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In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal.
Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 . The work is focused on the analysis of gender bias associated with professions in word embeddings. So therefore into the evaluation of the accuracy of the associations generated, aiming at achieving results as good as possible without prejudicing the evaluation metrics.
In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated.
Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 .
|
What were the word embeddings trained on?
|
The answers are shown as follows:
* large Portuguese corpus
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The first prototype was entirely mechanical. Three prototypes were made, and they were improved over one year. In the meantime, sure of having found a solution to the problem of taking 360° photos, Leme applied for a patent registration, a process that went on for some years.
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How many prototypes were made?
|
Three prototypes were made and improved over one year.
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Which is a species of fish? Black Mamba or Black Sea Bass
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Black Sea Bass
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In Digital Marketing, what is the difference between SEO and SEM?
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SEO stands for Search Engine Optimization and it focuses on the creation and enhancement of webpages to improve their rankings in organic search results. SEM stands for Search Engine Marketing and it focuses on the use of paid advertisements to rank at the top of Search Engine Results Page.
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Link is a fictional character and the protagonist of Nintendo's video game franchise The Legend of Zelda. He was created by Japanese video game designer Shigeru Miyamoto. Link was introduced as the hero of the original 1986 The Legend of Zelda video game and has appeared in a total of 19 entries in the series, as well as a number of spin-offs. Common elements in the series include Link travelling through Hyrule whilst exploring dungeons, battling creatures and solving puzzles until he eventually defeats the series' primary antagonist, Ganon, and saves Princess Zelda.
Throughout The Legend of Zelda series, Link has made multiple appearances in a variety of incarnations. He has been rendered in both 2D and 3D form and has been traditionally depicted in his signature green cap and tunic carrying a sword and shield. Over the course of the series, he appears as a child or young adult of the Hylian race, which originates from the fictional kingdom of Hyrule. Within Zelda lore, Link is the soul of a legendary hero that throughout history is reincarnated within a seemingly ordinary boy or man when the need arises for a new warrior to defeat the forces of evil. To defeat Ganon, Link usually obtains the mystical Master Sword or a similar legendary weapon, which is obtained after completing various trials. Over the course of his journey, he also acquires other magical items, including musical instruments and other weaponry.
In addition to the main series, Link has appeared in other Nintendo media, including merchandise, comics and manga, and an animated television series. He is a prominent character in various spin-off games, including Hyrule Warriors, Cadence of Hyrule and Hyrule Warriors: Age of Calamity. He has appeared in entries of several other game franchises, including the Super Smash Bros. series, SoulCalibur II and Mario Kart 8, and has also been referenced in other games, such as The Elder Scrolls V: Skyrim.
Alongside fellow Nintendo character Mario, Link is one of the most recognisable characters in the video game industry. He has been instrumental in the establishment of the role-playing video game genre as the protagonist of the series, which has influenced numerous other video games with its concepts of open world and nonlinear gameplay. According to Guinness World Records, Link is the most critically acclaimed videogame playable character and the most ubiquitous action-adventure video game character, surpassing Mario. He has been recognised by the Guinness World Records Gamer's Edition as the second best video game character of all time after Mario. Critics have also named him as one of the most influential video game characters of all time and one of Shigeru Miyamoto's most famous creations.
Link is a brave, skilled warrior and the hero of The Legend of Zelda series. Over the course of the series, he has appeared in a variety of ages and forms, ranging from child to young adult, and in Twilight Princess, also appears in the form of a wolf. He displays the characteristic traits of the Hylian race, being of human form with elfin features, including pointed ears. Since the original 1986 The Legend of Zelda video game, he has been repeatedly depicted wearing his characteristic green cap and tunic. He has also appeared wearing other outfits, including a blue lobster shirt in The Wind Waker and his blue Champion's Tunic in Breath of the Wild. Link is described in the original game's instruction manual as a "young lad" and a traveller and in later games, such as Breath of the Wild, as a knight of Hyrule who is sworn to protect the kingdom and Princess Zelda. During gameplay, he carries a sword and a shield, but has also wielded a variety of other weapons, including bows, spears and axes.
Link's signature weapon is the Master Sword, a powerful magic sword that has the ability to repel evil. He is also often depicted holding the Hylian Shield. These two components have become integral aspects of the character's identity. Each game in the series follows a similar story arc in which Link must take a journey that eventually leads him to recover the Master Sword, which makes him stronger in gameplay and enables him to defeat the series' main antagonist, Ganon.
Throughout each game, Link is able to obtain various items during his adventures, which the player can then use in gameplay. Many of these objects possess magical properties that bestow specific abilities on Link, such as a magic cape that makes Link invisible when he wears it, or potions that replenish his health. Others have various practical purposes, such as the hookshot, which enables Link to pull items towards him, and bombs for detonation. Link has used various musical instruments on his travels, most notably, the Ocarina of Time, which when played is used for teleportation. In Breath of the Wild, Link's key tool is the Sheikah Slate, a handheld tablet featuring various runes that enable him to manipulate the game world.
In Zelda lore, Link is the reincarnated soul of a hero, chosen by the goddess Hylia to protect the kingdom of Hyrule from Ganon and save Princess Zelda whenever the need arises. As the goddess' chosen hero, he is also the bearer of the Triforce of Courage, one of the three components that combine to form the Triforce, a sacred artefact and symbol of power. In several Zelda games, Link's main objective is to recover the fragments of the Triforce in order to defeat Ganon. Link's character is always depicted as a fearless hero and a "symbol of courage" who is willing to protect Hyrule for the sake of others.
Relationships
Link's relationships with the other main characters has been a defining aspect of the series. Within the fictional lore, Ganon, Zelda and Link represent three pieces of the Triforce, with Ganon representing Power, Zelda representing Wisdom and Link representing Courage. This trinity transcends the fictional timeline and dictates that the three characters are destined to be forever reincarnated in an endless battle for good and evil. Link's battle with Ganon established a fictional chronology that spans across the entire series and branches into three separate timelines. The three timelines originate at the end of Ocarina of Time when the timeline splits according to whether Link successfully defeats Ganon ("The Hero is Victorious") or fails to stop him (the "Fallen Hero" timeline). The official fictional chronology was revealed in Hyrule Historia, with Skyward Sword presented as the first game in the timeline. Skyward Sword establishes that the three main characters are destined to be connected in an endless cycle after the antagonist Demise curses Link and Zelda.
Link's relationship with Zelda has been a core aspect of the series but has also been the subject of speculation. Throughout the games, the storylines have suggested the possibility of a romantic relationship between the two, but this has remained ambiguous. With each game release, the nature of their relationship has varied and Link has also been presented with other possible love interests. A romantic relationship between Link and Zelda is particularly evident in Skyward Sword and was also emphasised in an official "romance trailer" for the game. Aonuma commented on this relationship in an interview with Game Informer: "As far as the love story goes, it wasn't that we wanted to create a romance between Link and Zelda as much as we wanted the player to feel like this is a person who's very important to me, who I need to find. We used that hint of a romance between the two to tug at the heartstrings". In Breath of the Wild, the relationship between Link and Zelda is more complex and follows a story arc that begins with resentment and ends in a close bond with each willing to give their life for the other.
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What is the relationship between Link and Zelda?
|
Link's relationship with Zelda has been developing as the story flows. In lore, Link, Zelda, and Ganon (the evil) represent three pieces Triforce, corresponding to Courage, Wisdom, and Power, respectively. The three are destined connected in an endless cycle.
People speculate that Link and Zelda are in a possible romantic relationship. Although not told by the story, there is evidence in and out of the games that this relationship is true. The game producer, Eiji Aonuma, once said otherwise that it was not their intention to create a romantic relationship between the two, but to the most important person for each other.
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José Francisco Miguel António de Mendonça, or rather, Mendoça (Lisbon, October 2, 1725 - Lisbon, February 11, 1808), was the fifth Patriarch of Lisbon under the name of D. José II.
Biography
He was the son of Nuno Manuel de Mendoça, 4th Count of Vale de Reis, and his wife, D. Leonor Maria Antónia de Noronha. He was the younger brother of the 5th Count and 1st Count of Azambuja, and of D. João Rafael de Mendonça, Bishop of Porto.
He graduated in canon law, having been canon of the Patriarchal See of Lisbon, Monsignor and still principal of the same church.
José Mendonca succeeded D. Francisco de Lemos as rector of the University of Coimbra from 1780 to 1785. During his tenure he attempted introduce a number of reforms.
In 1786, he was appointed Patriarch of Lisbon. By 1792 he was still trying to get permission from the Real Mesa Censória (Royal Censorial Court) to publish two pastoral letters. A law passed on April 5, 1768 reaffirmed the right of "temporal sovereignty" over the prohibition of "pernicious books and papers" in the interest of political defense. As the Mesa had jurisdiction over all printed materials, this included pastoral letters.
Pope Pius VI made him a cardinal in 1788. He died in 1808 and his body is buried in the Convent of Grace.
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Given a reference text about José Francisco Miguel António de Mendonça, tell me when he was appointed patriarch and when he died.
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José Francisco Miguel António de Mendonça was appointed Patriarch of Lisbon in 1786 and died in 1808.
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What is a python?
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Python can mean multiple things.
As a high-level programming language, Python is interpreted and has dynamic typing. Python is used for a variety of applications such as web development, data science, artificial intelligence, and robotics. It is also used in the development of embedded systems, system administration, and systems programming.
Python can also mean a type of snake in the Pythonidae family native to the tropics and subtropics of the Eastern Hemisphere. They are nonvenomous snakes found in Asia, Africa and Australia.
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Richmond enjoys an unprecedented five-game winning streak with Zava on the team, culminating in a win away at Old Trafford against Manchester United. Sam takes the team to celebrate at his yet-to-open Nigerian restaurant, Ola's. Ted is disturbed to learn that his ex-wife Michelle is dating their former couple's therapist, Dr. Jacob; both Sharon and Sassy validate his concern that it is an ethical violation. Jamie feels overshadowed by Zava's stardom; Roy offers to train him. Rebecca visits her mother's psychic Tish, who tells her she will one day be a mother; Rebecca angrily rebuffs Tish as a fraud, but is later stunned when Sam hands her one of the green matchbooks he is giving out to the team, in line with one of Tish's premonitions. Colin introduces his boyfriend Michael to the team as just a friend, but Trent spots them kissing in private while leaving the restaurant.
|
What caused the unprecedented five-game winning streak?
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While we don't know exactly what caused the winning streak, it seems clear that a key part of the winning streak was related to Zava being on the team. We know that Jamie is overshadowed by Zava's stardom which tells us that Zava is a star, and we see that Zava was mentioned in relation to the winning streak. Therefore, we can guess that Zava joining the team helped the team to go on a five-game winning streak.
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The Academy Awards, also known as the Oscars, are awards for artistic and technical merit for the film industry. They are presented annually by the Academy of Motion Picture Arts and Sciences (AMPAS), in recognition of excellence in cinematic achievements as assessed by the Academy's voting membership. The Academy Awards are regarded by many as the most prestigious, significant awards in the entertainment industry in the United States and worldwide. The Oscar statuette depicts a knight rendered in the Art Deco style.
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Please share some important points to remember for Oscar awards from the given text
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1. The Oscars, usually referred to as the Academy Awards, are prizes given to filmmakers for outstanding artistic and technical achievement.
2. The Academy of Motion Picture Arts and Sciences (AMPAS) bestows them annually in appreciation of outstanding contributions to cinema as judged by the Academy's voting membership.
3. Many people consider the Academy Awards to be the most prestigious and important honours in the American and international entertainment industries.
4. The Oscar trophy has an Art Deco-styled rendering of a knight.
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Lumbinī (Nepali: लुम्बिनी pronounced [ˈlumbini] (listen), "the lovely") is a Buddhist pilgrimage site in the Rupandehi District of Lumbini Province in Nepal. It is the place where, according to Buddhist tradition, Queen Mahamayadevi gave birth to Siddhartha Gautama at around 566 BCE. Gautama, who, according to Buddhist tradition, achieved Enlightenment some time around 528 BCE, became Gautama Buddha and founded Buddhism. Lumbini is one of many magnets for pilgrimage that sprang up in places pivotal to the life of the Buddha.
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Where was buddha born?
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Buddha was born in Lumbini which is in present day Nepal.
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Which is a species of fish? Snapper or Tapper
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Snapper
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Write a short paragraph on the impact of nutrition on human health.
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While it is obvious that nutrition should impact health, the modern medical paradigm has refused to account for, or at least minimised, its role in human health. Food is critical to human survival, and nutritious food is a prerequisite for creating and maintaining a healthy human body and mind. Consuming the wrong kinds of foods can wreak havoc on human metabolism, and over time, lead to hormonal disorders, obesity, diabetes and heart disease. The world is witness to increased cases of such disorders since the time of the industrialisation of the food and pharmaceutical industries. After World War 2, since the 1970s, the rates of incidence of the aforementioned diseases have increased manifold, correlated strongly with the increased consumption of processed foods, seed oils, sugar and carbohydrates while being negatively correlated with the consumption of saturated fats and red meat. Thus, while the average life expectancy of human beings has increased, albeit differently for different regions of the world, with the increasing contribution of modern medical practice, the quality of life for most people has decreased. The case can be made that we, as a society, are becoming calorie-rich but nutrient-poor, with negative outcomes for public health.
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Why are people so excited about SpaceX?
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SpaceX is a space exploration company started by Elon Musk designed to make humanity multi-planetary. SpaceX is short for Space Exploration Technologies. The company develops rockets, spacecraft, launch facilities, and satellites to deliver a variety of services to people all around the world. The long term plan for SpaceX is to create a system of technologies which are capable of bringing humans to Mars and making Mars habitable.
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How does the Stock Market work?
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The stock market helps companies raise money to fund operations by selling shares of stock, and it creates and sustains wealth for individual investors.
Companies raise money on the stock market by selling ownership stakes to investors. These equity stakes are known as shares of stock. By listing shares for sale on the stock exchanges that make up the stock market, companies get access to the capital they need to operate and expand their businesses without having to take on debt. In exchange for the privilege of selling stock to the public, companies are required to disclose information and give shareholders a say in how their businesses are run.
Investors benefit by exchanging their money for shares on the stock market. As companies put that money to work growing and expanding their businesses, investors reap the benefits as their shares of stock become more valuable over time, leading to capital gains. In addition, companies pay dividends to their shareholders as their profits grow.
The performances of individual stocks vary widely over time, but taken as a whole the stock market has historically rewarded investors with average annual returns of around 10%, making it one of the most reliable ways of growing your money.
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Identify which car manufacturer is Italian or American: Abarth, Buick
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Buick is American, Abarth is Italian
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To understand natural language and analyze documents and text, computers need to represent natural languages as linguistics models. These models can be generated by using machine learning methods (as show in Figure 2).
There are two common modeling methods for basic NLP tasks, including n-gram model and bag-of-words model. The n-gram model is widely used in natural language processing while the bag-of-words model is a simplified representation used in natural language processing and information retrieval BIBREF17 , BIBREF18 . According to the bag-of-words model, the representative vector of sentences in the document does not preserve the order of the words in the original sentences. It represents the word using term frequency collected from the document rather than the order of words or the structure of sentences in the document. The bag-of-words model is commonly used in methods of document classification, where the frequency of occurrence of each word is used as an attribute feature for training a classifier. In contrast, an n-gram is a contiguous sequence of n items from a given sequence of text. An n-gram model is a type of probabilistic language model for predicting the next item in a given sequence in form of a Markov model. To address word segmentation issue, the n-gram model is usually used for approaches because it considers the order of tokens in the original sentences. The sequence is also kept the original order as input and output sentences.
There are two common modeling methods for basic NLP tasks, including n-gram model and bag-of-words model.
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What are the common modeling methods for basic NLP tasks？
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There are two: n-gram model and bag-of-words model.
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Classify these US cities into states that they are in.
Jersey City, Buffalo, Schenectady, Newark, Yonkers, Hoboken
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New Jersey - Jersey City, Newark, Hoboken
New York - Buffalo, Schenectady, Yonkers
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My daughter's friend is lactose intolerant. What foods should we avoid when we take out to eat with us?
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You'll want to look out for anything that contains milk, butter, cream, buttermilk, cheese, or ice cream. This means pizza is off the table (it almost always has cheese), and much Italian cuisine like lasagna or alfredo sauces must be avoided. You also need to be careful about fried foods, because batters often contain milk or buttermilk. Ask about the ingredients of bread, as these can contain butter: croissants typically do. Even salads can be a concern, because many restaurants will add cheese as a topping, even without listing cheese on the menu. Desserts are also problematic, because chocolate nearly always contains milk, as do most ice creams. Look for vegan products where you can, such as vegan cheeses and vegan ice cream, which can be delicious.
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Holy Week in Mexico is an important religious observance as well as important vacation period. It is preceded by several observances such as Lent and Carnival, as well as an observance of a day dedicated to the Virgin of the Sorrows, as well as a Mass marking the abandonment of Jesus by the disciples. Holy Week proper begins on Palm Sunday, with the palms used on this day often woven into intricate designs. In many places processions, Masses and other observances can happen all week, but are most common on Maundy Thursday, Good Friday, Holy Saturday and Easter Sunday, with just about every community marking the crucifixion of Jesus in some way on Good Friday. Holy Saturday is marked by the Burning of Judas, especially in the center and south of the country, with Easter Sunday usually marked by a Mass as well as the ringing of church bells. Mexico's Holy Week traditions are mostly based on those from Spain, brought over with the Spanish conquest of the Aztec Empire, but observances have developed variations in different parts of the country due to the evangelization process in the colonial period and indigenous influences. Several locations have notable observances related to Holy Week including Iztapalapa in Mexico City, Taxco, San Miguel de Allende and San Luis Potosí.
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What happens during Holy week in Mexico?
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Holy Week in Mexico is a major religious celebration. Holy week starts on Palm Sunday and the major celebrations start on Maundy Thursday, Good Friday, the day marking the crucifixion of Jesus, and Easter Sunday when Jesus resurrected and marked by ringing of church bells.
Mexico's Holy week is based on traditions from Spain, with some variations due to the evangelisation process during the colonial period and indigenous influences, for example Iztapalapa in Mexico City, Taxco, San Miguel de Allende and San Luis Potosí.
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Time series data in the real world is high dimensional, unstructured, and complex with unique properties, leading to challenges for data modeling. In addition, without human recognizable patterns, it is much harder to label time series data than images and languages in realworld applications. These labeling limitations hinder deep learning methods, which typically require a huge amount of labeled data for training, been applied on time series data. Representation learning learns a fixed-dimension embedding from the original time series that keeps their inherent features. Comparing to the raw time series data, these representations are with better transferability and generalization capacity. To deal with labeling limitations, contrastive learning methods have been widely adopted in various domains for their soaring performance on representation learning, including vision, language, and graph-structured data. In a nutshell, contrastive learning methods typically train an encoder to map instances to an embedding space where dissimilar (negative) instances are easily distinguishable from similar (positive) ones and model predictions to be invariant to small noise applied to either input examples or hidden states.
Despite being effective and prevalent, contrastive learning has been less explored in the time series domain. Existing contrastive learning approaches often involve a specific data augmentation strategy that creates novel and realistic-looking training data without changing its label to construct positive alternatives for any input sample. Their success relies on carefully designed rules of thumb guided by domain expertise. Routinely used data augmentations for contrastive learning are mainly designed for image and language data, such as color distortion, flip, word replacement, and back-translation. These augmentation techniques generally do not apply to time series data.
Figure: InfoTS is composed of three parts: (1) candidate transformation that generates different augmentations of the original inputs, (2) a meta-network that selects the optimal augmentations, (3) an encoder that learns representations of time series instances. The meta-network is learned in tandem with contrastive encoder learning.
Recently, some researchers propose augmentations for time series to enhance the size and quality of the training data. For example, and propose to adopt jittering, scaling, and permutation strategies to generate augmented instances. extracts subsequences for data augmentation. In spite of the current progress, existing methods have two main limitations. First, unlike images with human recognizable features, time series data are often associated with inexplicable underlying patterns. Strong augmentation such as permutation may ruin such patterns and consequently, the model will mistake the negative handcrafts for positive ones. While weak augmentation methods such as jittering may generate augmented instances that are too similar to the raw inputs to be informative enough for contrastive learning. On the other hand, time series datasets from different domains may have diverse nature. Adapting a universal data augmentation method, such as subsequence in, for all datasets and tasks leads to sub-optimal performances. Other works follow empirical rules to select suitable augmentations from expensive trial-and-error. Akin to hand-crafting features, hand-picking choices of data augmentations are undesirable from the learning perspective. The diversity and heterogeneity of real-life time series data further hinder these methods away from wide applicability.
To address the challenges, we first introduce the criteria for selecting good data augmentations in contrastive learning. Data augmentation benefits generalizable, transferable, and robust representation learning by correctly extrapolating the input training space to a larger region. The positive instances enclose a discriminative zone in which all the data points should be similar to the original instance. The desired data augmentations for contrastive representation learning should have both high fidelity and high variety. High fidelity encourages the augmented data to maintain the semantic identity that is invariant to transformations. For example, if the downstream task is classification, then the generated augmentations of inputs should be class-preserving. Meanwhile, generating augmented samples with high variety benefits representation learning by increasing the generalization capacity. From the motivation, we theoretically analyze the information flows in data augmentations based upon information theory and derive the criteria for selecting desired time series augmentations. Due to the inexplicability in practical time series data, we assume that the semantic identity is presented by the target in the downstream task. Thus, high fidelity can be achieved by maximizing the mutual information between the downstream label and the augmented data. A one-hot pseudo label is assigned to each instance in the unsupervised setting when downstream labels are unavailable. These pseudo labels encourage augmentations of different instances to be distinguishable from each other. We show that data augmentations preserving these pseudo labels can add new information without decreasing the fidelity. Concurrently, we maximize the entropy of augmented data conditional on the original instances to increase the variety of data augmentations.
Based on the derived criteria, we propose an adaptive data augmentation method, InfoTS (as shown in Figure), by employing a meta-learning mechanism to avoid ad-hoc choices or painstakingly trial-and-error tuning. Specifically, we utilize a meta-network to learn the augmentation prior in tandem with contrastive learning. The meta-learner automatically selects optimal augmentations from candidate augmentations to generate feasible positive samples. Along with random sampled negative instances, augmented instances are then fed into a time series encoder to learn representations in a contrastive manner. With a reparameterization trick, the meta-network can be efficiently optimized with back-propagation based upon the proposed criteria. Therefore, the meta-network can automatically select data augmentations in a per dataset and per learning task manner without resorting to expert knowledge or tedious downstream validation. Our main contributions include:
• We propose criteria to guide the selection of data augmentations for contrastive time series representation learning without prefabricated knowledge. • We propose a meta-learning based method to automatically select feasible data augmentations for different time series datasets, which can be efficiently optimized with backpropagation. • We empirically verify the effectiveness of the proposed criteria to find optimal data augmentations. Extensive experiments demonstrate that InfoTS can achieve highly competitive performance with up to 11.4% reduction in MSE on forecasting task and up to 2.8% relative improvement in accuracy on classification task over the leading baselines.
To present deep insights into the proposed method, we verify key components in InfoTS with multiple ablation studies on the Electricity dataset. Results are shown in Table. 1) We verify the effectiveness of the adopted contrastive learning objective, Eq. (), by comparing InfoTS with its two variants. "w/o Local" removes the local contrastive loss between subsequences and "w/o Global" removes the instance-level contrastive loss. The comparison between these variants and InfoTS demonstrates that both instance-level and intra-temporal objectives are important in contrastive learning for time series data. 2) To demonstrate the advantage of adaptive selection of augmentations, we compare InfoTS with variants "Random" and "All". "Random" randomly selects an augmentation from candidate transformation functions each time and "All" sequentially applies transformations to generate augmented instances. Performance decreases are observed in these variances, verifying the key role of adaptive selection in our method. 3) To show the effects of variety and fidelity objectives in meta-network training, we include two variants, "w/o Fidelity" and "w/o Variety", which dismiss the fidelity or variety objective, respectively. The comparison between InfoTS and the variants empirically confirms both variety and fidelity are important for data augmentation in contrastive learning. Note that the adopted basic augmentations are manually tuned in a previous paper with high qualities for contrastive learning. Including low quality basic augmentations, we conduct more ablation studies are shown in Appendix D.3.
Based on the derived criteria, we propose an adaptive data augmentation method, InfoTS (as shown in Figure 1), by employing a meta-learning mechanism to avoid ad-hoc choices or painstakingly trial-and-error tuning. Specifically, we utilize a meta-network to learn the augmentation prior in tandem with contrastive learning. The meta-learner automatically selects optimal augmentations from candidate augmentations to generate feasible positive samples.****To demonstrate the advantage of adaptive selection of augmentations, we compare InfoTS with variants “Random” and “All”.****To show that our InfoTS can adaptively detect the most effective augmentation based on the data distribution, we follow the setting in Section 4.4 and conduct more ablation studies to investigate comprehensively into the proposed model. We compare performances of variants that each applies a single transformation to generate augmented instances in Table 5. From the table, we know that augmentation with subsequence benefits the most for the Electricity dataset. We visualize the weight weight updating process of InfoTS in Figure 8, with each line representing the normalized importance score of the corresponding transformation. The weight for subsequence increase with the epoch, showing that InfoTS tends to adopt subsequence as the optimal transformation. Consistency between accuracy performance and weight updating process demonstrates the effectiveness of InfoTS to adaptively select feasible transformations. Besides, as shown in Table 5, InfoTS outperforms the variant that uses subsequence only. This comparison shows that the meta-network learns to consider the combinations, which is better than any (single) candidate augmentation.
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The advantage of adaptive selection: So if I understand correctly with adaptive selection the authors refer to the concrete sample from a trained categorical over possible augmentations?
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Yes, our meta-network learns a weight for each candidate augmentation, which is considered as adaptive selection.
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What is the best part of living in the Midwest?
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Midwest is often overlooked as the flyover states, but in fact the quality of life is probably better in the midwest than in the coasts. For one thing, real-estate is more affordable and cost of owning a home is much less than in the coasts. Midwest is also the home of some iconic companies such as General Electric, General Mills, Caterpillar and Kohler to name a few. Midwest also has lot of wooded areas with lakes which make it a great destination for hiking, adventure sports and fishing.
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Give me a list of some different summer holidays that occur in the United States
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Some summer Holidays include Memorial Day, Fourth of July, Juneteenth and Labor Day
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It is often the case that the meaning of a word is determined by its context, such as the previous word and the following word. [id=lq]Also, negations and emotion transitions are context sensitive. However, the memory network described in Section SECREF3 has only one memory slot with size INLINEFORM0 to represent a clause, where INLINEFORM1 is the dimension of a word embedding and INLINEFORM2 is the length of a clause. It means that when the memory network models a clause, it only considers each word separately.
In order to capture [id=lq]context information for clauses, we propose a new architecture which contains more memory slot to model the context with a convolutional operation. The basic architecture of Convolutional Multiple-Slot Memory Network (in short: ConvMS-Memnet) is shown in Figure 4.
Considering the text length is usually short in the dataset used here for emotion cause extraction, we set the size of the convolutional kernel to 3. That is, the weight of word INLINEFORM0 [id=lq]in the INLINEFORM1 -th position considers both the previous word INLINEFORM2 and the following word INLINEFORM3 by a convolutional operation: DISPLAYFORM0
For the first and the last word in a clause, we use zero padding, INLINEFORM0 , where INLINEFORM1 is the length of a clause. Then, the attention [id=lq]weightsignal for each word position in the clause is [id=lq]now defined as: DISPLAYFORM0
Note that we obtain the attention for each position rather than each word. It means that the corresponding attention for the INLINEFORM0 -th word in the previous convolutional slot should be INLINEFORM1 . Hence, there are three prediction output vectors, namely, INLINEFORM2 , INLINEFORM3 , INLINEFORM4 : DISPLAYFORM0
At last, we concatenate the three vectors as INLINEFORM0 for the prediction by a softmax function: DISPLAYFORM0
Here, the size of INLINEFORM0 is INLINEFORM1 . Since the prediction vector is a concatenation of three outputs. We implement a concatenation operation rather than averaging or other operations because the parameters in different memory slots can be updated [id=lq]respectively in this way by back propagation. The concatenation of three output vectors forms a sequence-level feature which can be used in the training. Such a feature is important especially [id=lq]when the size of annotated training data is small.
For deep architecture with multiple layer[id=lq]s training, the network is more [id=lq]complex (shown in Figure 5).
For the first layer, the query is an embedding of the emotion word, INLINEFORM0 .
In the next layer, there are three input queries since the previous layer has three outputs: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 . So, for the INLINEFORM3 -th layer ( INLINEFORM4 ), we need to re-define the weight function (5) as:
In the last layer, [id=lq]the concatenation of the three prediction vectors form the final prediction vector to generate the answer.
For model training, we use stochastic gradient descent and back propagation to optimize the loss function. Word embeddings are learned using a skip-gram model. The size of the word embedding is 20 since the vocabulary size in our dataset is small. The dropout is set to 0.4.
For model training, we use stochastic gradient descent and back propagation to optimize the loss function.
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What is used to optimize the loss function?
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The authors use stochastic gradient descent and back propagation to optimize the loss function.
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Iodine-125 (125I) is a radioisotope of iodine which has uses in biological assays, nuclear medicine imaging and in radiation therapy as brachytherapy to treat a number of conditions, including prostate cancer, uveal melanomas, and brain tumors. It is the second longest-lived radioisotope of iodine, after iodine-129.
Its half-life is 59.49 days and it decays by electron capture to an excited state of tellurium-125. This state is not the metastable 125mTe, but rather a lower energy state that decays immediately by gamma decay with a maximum energy of 35 keV. Some of the excess energy of the excited 125Te may be internally converted ejected electrons (also at 35 keV), or to x-rays (from electron bremsstrahlung), and also a total of 21 Auger electrons, which are produced at the low energies of 50 to 500 electron volts. Eventually, stable ground state 125Te is produced as the final decay product.
In medical applications, the internal conversion and Auger electrons cause little damage outside the cell which contains the isotope atom. The X-rays and gamma rays are of low enough energy to deliver a higher radiation dose selectively to nearby tissues, in "permanent" brachytherapy where the isotope capsules are left in place (125I competes with palladium-103 in such uses)
Because of its relatively long half-life and emission of low-energy photons which can be detected by gamma-counter crystal detectors, 125I is a preferred isotope for tagging antibodies in radioimmunoassay and other gamma-counting procedures involving proteins outside the body. The same properties of the isotope make it useful for brachytherapy, and for certain nuclear medicine scanning procedures, in which it is attached to proteins (albumin or fibrinogen), and where a half-life longer than that provided by 123I is required for diagnostic or lab tests lasting several days.
Iodine-125 can be used in scanning/imaging the thyroid, but iodine-123 is preferred for this purpose, due to better radiation penetration and shorter half-life (13 hours). 125I is useful for glomerular filtration rate (GFR) testing in the diagnosis or monitoring of patients with kidney disease. Iodine-125 is used therapeutically in brachytherapy treatments of tumors. For radiotherapy ablation of tissues that absorb iodine (such as the thyroid), or that absorb an iodine-containing radiopharmaceutical, the beta-emitter iodine-131 is the preferred isotope.
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What treatments is Iodine-125 used for?
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Iodine-125 is used in biological assays, nuclear medicine imaging and in radiation therapy as brachytherapy to treat a number of conditions, including prostate cancer, uveal melanomas, and brain tumors.
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The challenges of imbalanced classification—in which the proportion of elements in each class for a classification task significantly differ—and of the ability to generalise on dissimilar data have remained important problems in Natural Language Processing (NLP) and Machine Learning in general. Popular NLP tasks including sentiment analysis, propaganda detection, and event extraction from social media are all examples of imbalanced classification problems. In each case the number of elements in one of the classes (e.g. negative sentiment, propagandistic content, or specific events discussed on social media, respectively) is significantly lower than the number of elements in the other classes.
The recently introduced BERT language model for transfer learning BIBREF0 uses a deep bidirectional transformer architecture to produce pre-trained context-dependent embeddings. It has proven to be powerful in solving many NLP tasks and, as we find, also appears to handle imbalanced classification well, thus removing the need to use standard methods of data augmentation to mitigate this problem (see Section SECREF11 for related work and Section SECREF16 for analysis).
BERT is credited with the ability to adapt to many tasks and data with very little training BIBREF0. However, we show that BERT fails to perform well when the training and test data are significantly dissimilar, as is the case with several tasks that deal with social and news data. In these cases, the training data is necessarily a subset of past data, while the model is likely to be used on future data which deals with different topics. This work addresses this problem by incorporating cost-sensitivity (Section SECREF19) into BERT.
We test these methods by participating in the Shared Task on Fine-Grained Propaganda Detection for the 2nd Workshop on NLP for Internet Freedom, for which we achieve the second rank on sentence-level classification of propaganda, confirming the importance of cost-sensitivity when the training and test sets are dissimilar.
This work addresses this problem by incorporating cost-sensitivity (Section 4.2) into BERT. We test these methods by participating in the Shared Task on Fine-Grained Propaganda Detection for the 2nd Workshop on NLP for Internet Freedom, for which we achieve the second rank on sentence-level classification of propaganda, confirming the importance of cost-sensitivity when the training and test sets are dissimilar.
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The importance of what learning methods do they confirm when the training and test sets are dissimilar?
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Cost-sensitivity.
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What are some types of things you can cook at a campfire?
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S'mores, hotdogs, sausages, baked potatoes
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Taubaté Prison is a prison in Taubaté in São Paulo, Brazil. It is notorious for containing some of the most violent prisoners, for repeated prison riots, and for being the place where the Primeiro Comando da Capital criminal gang originated.
On December 19, 2000 The Prison Uprising ended at Taubaté Prison
released more than 20 hostages on Monday, ending an uprising at a maximum security facility that left nine prisoners dead, officials said.
The rebellion at the Taubate House of Custody and Psychiatric Treatment, about 80 miles outside Sao Paulo, began during visiting hours Sunday when an inmate opened fire with a revolver, provoking a fight with prisoners from another pavilion.
Taking advantage of the confusion, prisoners took 23 hostages including four children.
Inmates began releasing hostages in small groups Monday after authorities agreed to transfer 10 prisoners to another facility. The hostages, all of whom were unhurt said the prisoners treated them well during the ordeal.
The department said the nine victims were probably killed in a settling of scores between rival gangs.
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What is the Taubate Prison known for
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The Taubate Prison is a prison in Taubate in Sao Paulo, Brazil. It is notorious for containing some of the most violent prisoners, for repeated prison riots, and for being the place where the Primeiro Comando da Capital criminal gang originated.
On December 19, 2000 The Prison Uprising ended at Taubate Prison
released more than 20 hostages on Monday, ending an uprising at a maximum security facility that left nine prisoners dead, officials said.
The rebellion at the Taubate House of Custody and Psychiatric Treatment, about 80 miles outside Sao Paulo, began during visiting hours Sunday when an inmate opened fire with a revolver, provoking a fight with prisoners from another pavilion.
Taking advantage of the confusion, prisoners took 23 hostages including four children.
Inmates began releasing hostages in small groups Monday after authorities agreed to transfer 10 prisoners to another facility. The hostages, all of whom were unhurt said the prisoners treated them well during the ordeal.
The department said the nine victims were probably killed in a settling of scores between rival gangs.
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Identify which animal species is alive or extinct: Saber-Toothed Tiger, Mountain Plover
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Mountain Plover is alive, Saber-Toothed Tiger is extinct.
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Identify which instrument is string or woodwind: Pedal Harp, Baritone Saxophone
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Pedal Harp is string, Baritone Saxophone is woodwind.
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Borneo was launched in 1713 on the River Thames and made two voyages for the British East India Company (EIC) to Banjarmasin, Bencoolen, and Borneo. She then became a West Indiaman.
EIC voyage #1 (1713–1716): Captain Thomas Lewis sailed from The Downs on 2 October 1713, bound for Banjarmasin and Bencoolen. Borneo was at the Cape of Good Hope on 30 January 1714 and reached Batavia on 7 May. She was at Banjarmasin on 1 July and Bencoolen on 26 December. She was then at Bantal on 23 January 1715 before returning to Bencoolen on 9 February. She returned to Bantal on 4 March and Bencoolen on 29 March. She then was at Batavia on 13 June and Bencoolen yet again on 24 August. She stopped at Madras on 21 February 1716, and visited Bencoolen for the last time on 30 May. Her voyage ended on 1 August 1717.
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Given the below text, how many days did it take for Captain Lewis to sail the Borneo from Banjarmasin to Madras? Explain how you did this calculation
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It took 600 days. The Borneo was in Banjarmasin on July 1st 1714, and then arrived in Madras on February 21st 1716. There are 600 days between these dates.
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Which bird has the longest wingspan?
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Its the Wandering Albatross with wingspans ranging from 8 to 11 feet.
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What is Megan Rapinoe's soccer jersey number?
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15
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Give me some things to think about when planning a party
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Hosting a party can be fun, but there is work involved. Here is a list of questions to consider to being planning your party:
1. What is your budget? Planning a party can cost money and you should first determine how much you're willing to spend on it.
2. Where is your party? You should first decide on the location of your party. It could be at your home, in a pubic park, or in a rented space.
3. Who is coming? Choose a number of guests that will fit the space comfortably. Once you have a number, make your guest list. It's good to ensure that your guests will mesh well with each other, but most importantly, choose a list of people that you'd like to host.
4. When is your party? It's good to choose a time that most of your guest will be able to make it. If your party is outdoor, you should consider the weather when choosing a date.
5. What are you serving? Consider if you want alcohol at your party and how much food you will need. If your party is during a meal time, you should provide that meal for your guest.
6. Is there entertainment? You can provide entertainment like games or music at the party to keep your guests engaged. It can be as simple as playing music from your phone or as big as hiring a band.
7. Are there decorations? You can choose to theme your party and decorate the venue!
8. What logistics are needed? Don't forget things like parking and clean up when it comes to planning a party.
Once you have answers to these questions, you are ready to start planning your party!
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Where do eggs come from?
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Eggs come from female chicken in their reproductive track. Humans eat these eggs as a source of protein in meals. Eggs are normally eaten the most at breakfast time but they are also used in many desserts like cakes and cookies. Chickens can lay eggs every 26 - 24 hours. This process can also be speed up by giving female chicken more calcium, more room to roam, reduced stress environment, and free flowing water.
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Who is the greatest guitarist of all time?
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Music tastes differ greatly so it's difficult to come up with a definitive answer on who the best guitarist is. Some one once asked Jimmy Hendrix, arguably one of the greatest guitarist of all time, who the greatest guitarist is and he answered "I don't know ask Phil Keaggy". Regardless Jimmy Hendrix is considered the greatest guitarist of all time.
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The King's Building is a Grade I listed building that forms part of the Strand Campus of King's College London in the United Kingdom. Originally named the College Building, the King's Building was designed by Sir Robert Smirke in the course of the College's foundation in 1829. As the founding building, it was built between 1829 and 1831 on land granted to King's College by the Government to complete the riverside frontage of Somerset House.
There are today a total of eight floors in the King's Building: Basement level, Ground level, Levels 1 to 4, Level 4U and Level 6. The King's Building houses a number of administrative departments (Estates & Facilities Offices, Admissions Office, Accommodation and Cashiers' Office), lecture theatres (most notably the Edmond J. Safra Lecture Theatre and Anatomy Lecture Theatre), the College Chapel, the Great Hall, various function rooms (Old Council Room, Committee Room, River Room, Somerset Room, St David's Room), study rooms and a catering outlet.
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What is the top floor of the King's Building based on this article? What floor is found below that?
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The top floor is Level 6, and below that is level 4U.
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Who sold more CDs in 2016 than Beyonce?
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In 2016, Mozart sold More CDs than Beyoncé.
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Pinot noir's home is France's Burgundy region, particularly Côte-d'Or. It is also planted in Argentina, Australia, Austria, Bulgaria, Canada, Chile, northern parts of Croatia, Czech Republic, England, the Republic of Georgia, Germany, Greece, Israel, Italy, Hungary, Kosovo, the Republic of Macedonia, Moldova, New Zealand, Romania, Serbia, Slovakia, Slovenia, South Africa, Switzerland, Ukraine, United States, and Uruguay. The United States has increasingly become a major pinot noir producer, with some of the best regarded coming from Oregon's Willamette Valley, and California's Sonoma County with its Russian River Valley and Sonoma Coast appellations. Lesser-known appellations are found in Mendocino County's Anderson Valley, the Central Coast's Santa Lucia Highlands appellation, the Santa Maria Valley, and Sta. Rita Hills American Viticulture Area in Santa Barbara County. In New Zealand, it is principally grown in Martinborough, Marlborough, Waipara, and Central Otago
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In which countries in Pinot Noir most commonly grown ?
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Pinot Noir is grown in France, Argentina, Australia, Austria, Bulgaria, Canada, Chile, Croatia, Czech Republic, England, the Republic of Georgia, Germany, Greece, Israel, Italy, Hungary, Kosovo, the Republic of Macedonia, Moldova, New Zealand, Romania, Serbia, Slovakia, Slovenia, South Africa, Switzerland, Ukraine, United States, and Uruguay.
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Identify which instrument is string or percussion: Pandeiro, Kudlung
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Pandeiro is percussion, Kudlung is string.
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The history of coffee dates back to centuries of old oral tradition in modern day Ethiopia. However, neither where coffee was first cultivated nor direct evidence of its consumption prior to the 15th century have been found. Sufi monasteries in Yemen employed coffee as an aid to concentration during prayers. Coffee later spread to the Levant and Persia in the early 16th century; it caused some controversy on whether it was halal in Ottoman and Mamluk society. Coffee arrived in Italy the second half of the 16th century through commercial Mediterranean trade routes, while Central and Eastern Europeans learnt of coffee from the Ottomans. By the mid 17th century, it had reached India and the East Indies.
Coffeepot (cafetière "campanienne"), part of a service, 1836, hard-paste porcelain, Metropolitan Museum of Art
Coffee houses would establish themselves in Western Europe by the late 17th century, especially in England and Germany. One of the earliest cultivations of coffee in the New World was when Gabriel de Clieu brought coffee seedlings to Martinique in 1720. These beans later sprouted 18,680 coffee trees which enabled its spread to other Caribbean islands like Saint-Domingue and also to Mexico. By 1788, Saint-Domingue supplied half the world's coffee.
By 1852, globally, Brazil became the largest producer of coffee and has held that status ever since. The period since 1950 saw the widening of the playing field due to the emergence of several other major producers, notably Colombia, Ivory Coast, Ethiopia, and Vietnam; the latter overtook Colombia and became the second-largest producer in 1999. Modern production techniques along with the mass productization of coffee has made it a household item today.
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Extract a comma separated list of places mentioned in this entry on the history of coffee
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Ethiopia, Yemen, the Levant, Persia, Central Europe, Eastern Europe, India, East Indies, Western Europe, England, Germany, Martinique, Saint-Domingue, Caribbean, Mexico, Brazil, Colombia, Ivory Coast, Vietnam
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We proposed a simple yet efficient TF-IDF method to extract and corroborate useful keywords from pathology cancer reports. Encoding a pathology report for cancer and tumor surveillance is a laborious task, and sometimes it is subjected to human errors and variability in the interpretation. One of the most important aspects of encoding a pathology report involves extracting the primary diagnosis. This may be very useful for content-based image retrieval to combine with visual information. We used existing classification model and TF-IDF features to predict the primary diagnosis. We achieved up to 92% accuracy using XGBoost classifier. The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports.
The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports.
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What was the conclusion of the experiment?
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The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports.
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Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1.
In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture.
Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model.
The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English.
In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following:
Approximation of perplexity using a BERT architecture
Using Transformer-XL architecture with sub-word units.
Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language.
We will use the same training data as in [9] so that we can do fair comparisons with the performance of the LSTM models.
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Why the same training data as in [9] is used?
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To do fair comparisons with the performance of the LSTM models.
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Edina's parkland and open space total more than 1,550 acres (6.3 km2). The Edina Park and Recreation Department oversees 44 parks, which include amenities such as baseball, football and soccer fields; softball diamonds; basketball and tennis courts; outdoor skating rinks; playground equipment for young children; and picnic shelters. The Department also maintains eight miles (13 km) of scenic pathways for bicycling, walking, jogging, cross-country skiing and snowshoeing.
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Given a reference text about parks in Edina, determine the average size of its parks.
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Since there are 44 total parks and a total of 1,550 acres across all parks, the average size of each park would be about 35 acres, because 1,550 divided by 44 is 35 after rounding to the nearest whole number.
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At the concert last night, identify each of these musicians as singer, drummer, guitarist, or bassist: Steve Harris, Bruce Dickinson, George Lynch, Nicko McBrain, Angus Young
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Steve Harris - bassist
Bruce Dickinson - singer
George Lynch - guitarist
Nicko McBrain - drummer
Angus Young - guitarist
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The General Data Protection Regulation (2016/679, "GDPR") is a Regulation in EU law on data protection and privacy in the EU and the European Economic Area (EEA). The GDPR is an important component of EU privacy law and of human rights law, in particular Article 8(1) of the Charter of Fundamental Rights of the European Union. It also addresses the transfer of personal data outside the EU and EEA areas. The GDPR's primary aim is to enhance individuals' control and rights over their personal data and to simplify the regulatory environment for international business. Superseding the Data Protection Directive 95/46/EC, the regulation contains provisions and requirements related to the processing of personal data of individuals, formally called "data subjects", who are located in the EEA, and applies to any enterprise—regardless of its location and the data subjects' citizenship or residence—that is processing the personal information of individuals inside the EEA.
The GDPR was adopted on 14 April 2016 and became enforceable beginning 25 May 2018. As the GDPR is a regulation, not a directive, it is directly binding and applicable, and provides flexibility for certain aspects of the regulation to be adjusted by individual member states.
According to the European Commission, "Personal data is information that relates to an identified or identifiable individual. If you cannot directly identify an individual from that information, then you need to consider whether the individual is still identifiable. You should take into account the information you are processing together with all the means reasonably likely to be used by either you or any other person to identify that individual." The precise definitions of terms such as "personal data", "processing", "data subject", "controller", and "processor" are stated in Article 4 of the Regulation.
Principles
Personal data may not be processed unless there is at least one legal basis to do so. Article 6 states the lawful purposes are:
(a) If the data subject has given consent to the processing of his or her personal data;
(b) To fulfill contractual obligations with a data subject, or for tasks at the request of a data subject who is in the process of entering into a contract;
(c) To comply with a data controller's legal obligations;
(d) To protect the vital interests of a data subject or another individual;
(e) To perform a task in the public interest or in official authority;
(f) For the legitimate interests of a data controller or a third party, unless these interests are overridden by interests of the data subject or her or his rights according to the Charter of Fundamental Rights (especially in the case of children).
Security of personal data
Controllers and processors of personal data must put in place appropriate technical and organizational measures to implement the data protection principles. Business processes that handle personal data must be designed and built with consideration of the principles and provide safeguards to protect data (for example, using pseudonymization or full anonymization where appropriate). Data controllers must design information systems with privacy in mind. For instance, using the highest-possible privacy settings by default, so that the datasets are not publicly available by default and cannot be used to identify a subject. No personal data may be processed unless this processing is done under one of the six lawful bases specified by the regulation (consent, contract, public task, vital interest, legitimate interest or legal requirement). When the processing is based on consent the data subject has the right to revoke it at any time.
Article 33 states the data controller is under a legal obligation to notify the supervisory authority without undue delay unless the breach is unlikely to result in a risk to the rights and freedoms of the individuals. There is a maximum of 72 hours after becoming aware of the data breach to make the report. Individuals have to be notified if a high risk of an adverse impact is determined (Article 34). In addition, the data processor will have to notify the controller without undue delay after becoming aware of a personal data breach (Article 33). However, the notice to data subjects is not required if the data controller has implemented appropriate technical and organisational protection measures that render the personal data unintelligible to any person who is not authorised to access it, such as encryption (Article 34).
Data protection officer
Article 37 requires appointment of a data protection officer. If processing is carried out by a public authority (except for courts or independent judicial authorities when acting in their judicial capacity), or if processing operations involve regular and systematic monitoring of data subjects on a large scale, or if processing on a large scale of special categories of data and personal data relating to criminal convictions and offences (Articles 9 and Article 10) a data protection officer (DPO)—a person with expert knowledge of data protection law and practices—must be designated to assist the controller or processor in monitoring their internal compliance with the Regulation.
A designated DPO can be a current member of staff of a controller or processor, or the role can be outsourced to an external person or agency through a service contract. In any case, the processing body must make sure that there is no conflict of interest in other roles or interests that a DPO may hold. The contact details for the DPO must be published by the processing organisation (for example, in a privacy notice) and registered with the supervisory authority.
The DPO is similar to a compliance officer and is also expected to be proficient at managing IT processes, data security (including dealing with cyberattacks) and other critical business continuity issues associated with the holding and processing of personal and sensitive data. The skill set required stretches beyond understanding legal compliance with data protection laws and regulations. The DPO must maintain a living data inventory of all data collected and stored on behalf of the organization. More details on the function and the role of data protection officer were given on 13 December 2016 (revised 5 April 2017) in a guideline document.
Organisations based outside the EU must also appoint an EU-based person as a representative and point of contact for their GDPR obligations (Article 27). This is a distinct role from a DPO, although there is overlap in responsibilities that suggest that this role can also be held by the designated DPO.
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Given a reference text about GDPR, tell me how GDPR helps to secure personal data.
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General Data Protection Regulation provides guidelines on storing and processing personal data. Personal data is any information about an identified or identifiable person, also known as the data subject. Personal data includes information such as their:
name
address
ID card/passport number
income
cultural profile
Internet Protocol (IP) address
data held by a hospital or doctor (which uniquely identifies a person for health purposes).
Special categories of data
You may not process personal data about someone's:
racial or ethnic origin
sexual orientation
political opinions
religious or philosophical beliefs
trade-union membership
genetic, biometric or health data except in specific cases (e.g. when you've been given explicit consent or when processing is needed for reasons of substantial public interest, on the basis of EU or national law)
personal data related to criminal convictions and offences unless this is authorised by EU or national law.
Who processes the personal data?
During processing, personal data can pass through various different companies or organisations. Within this cycle there are two main profiles that deal with processing personal data:
The data controller - decides the purpose and way in which personal data is processed.
The data processor - holds and processes data on behalf of a data controller.
Who monitors how personal data is processed within a company?
The Data Protection Officer (DPO), who may have been designated by the company, is responsible for monitoring how personal data is processed and to inform and advise employees who process personal data about their obligations. The DPO also cooperates with the Data Protection Authority (DPA), serving as a contact point towards the DPA and individuals.
Processing data for another company
A data controller can only use a data processor who offers sufficient guarantees, these should be included in a written contract between the parties involved. The contract must also contain a number of mandatory clauses, e.g. that the data processor will only process personal data when instructed to do so by the data controller.
Data transfer outside the EU
When personal data is transferred outside the EU, the protection offered by the GDPR should travel with the data. This means that if you export data abroad, your company must ensure one of the following measures are adhered to:
The non-EU country's protections are deemed adequate by the EU.
Your company takes the necessary measures to provide appropriate safeguards, such as including specific clauses in the agreed contract with the non-European importer of the personal data.
Your company relies on specific grounds for the transfer (derogations) such as the consent of the individual.
When is data processing allowed?
EU data protection rules mean you should process data in a fair and lawful manner, for a specified and legitimate purpose and only process the data necessary to fulfil this purpose. You must ensure that you fulfil one of the following conditions to process the personal data; you:
have been given the consent of the individual concerned
need the personal data to fulfil a contractual obligation with the individual
need the personal data to satisfy a legal obligation
need the personal data to protect the vital interests of the individual
process personal data to carry out the task in the interest of the public
are acting in your company's legitimate interests, as long as the fundamental rights and freedoms of the individual whose data are processed are not seriously impacted. If the person's rights override your company's interests, then you cannot process the personal data.
Agreeing to data processing - consent
Right to access and right to data portability
Right to correct and right to object
Right to erasure (right to be forgotten)
Automated decision-making and profiling
Data breaches – providing proper notification
Responding to requests
Impact assessments
Keeping a record
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Marshall Strickland (born March 1, 1983) is an American former professional basketball player. He played college basketball at Indiana University for four years from 2002 to 2006. He is originally from Kingston, Massachusetts and went to Winchendon Prep in Winchendon before transferring to South Carroll High School in Carroll County, Maryland. He is 6 ft 2 in (1.88 m) tall and weighs 195 pounds (88 kg). He is the son of Marshall and Joanna Strickland, and younger brother of former Maryland Terrapins star Marche Strickland. Marshall is a graduate of the University of Maryland School of Dentistry in 2017.
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Who is Marshall Strickland
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Marshall Strickland (born March 1, 1983) is an American former professional basketball player. He played college basketball at Indiana University for four years from 2002 to 2006.
He is originally from Kingston, Massachusetts and went to Winchendon Prep in Winchendon before transferring to South Carroll High School in Carroll County, Maryland. He is 6 ft 2 in (1.88 m) tall and weighs 195 pounds (88 kg).
He is the son of Marshall and Joanna Strickland, and younger brother of former Maryland Terrapins star Marche Strickland. Marshall is a graduate of the University of Maryland School of Dentistry in 2017.
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Did Harry Potter put his own name in the goblet of fire?
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Harry did not put his own name in the goblet of fire. Barty Crouch Junior, using polyjuice potion to disguise himself as Alistair Moody, put Harry's name in the goblet of fire as part of his plan to deliver Harry into Lord Voldemort's hands.
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What is the best way to wake up fresh in the morning?
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1. Sleep on time at night. Those who sleep at the same time daily tend to wake up fresher
2. Have a ton of water as soon as you wake up
3. Do a little yoga stretch first thing in the morning to relax your body
4. Wake up at the same time everyday.
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What are the titles to the six volumes of Sir Winston Churchill's "The Second World War"?
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The six volumes of Sir Winston Churchill's "The Second World War" were published between 1948 and 1953. They are:
1. The Gathering Storm
2. Their Finest Hour
3. The Grand Alliance
4. The Hinge of Fate
5. Closing the Ring
6. Triumph and Tragedy
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| 199
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Evidence-based medicine (EBM) is of primary importance in the medical field. Its goal is to present statistical analyses of issues of clinical focus based on retrieving and analyzing numerous papers in the medical literature BIBREF0 . The PubMed database is one of the most commonly used databases in EBM BIBREF1 .
Biomedical papers, describing randomized controlled trials in medical intervention, are published at a high rate every year. The volume of these publications makes it very challenging for physicians to find the best medical intervention for a given patient group and condition BIBREF2 . Computational methods and natural language processing (NLP) could be adopted in order to expedite the process of biomedical evidence synthesis. Specifically, NLP tasks applied to well structured documents and queries can help physicians extract appropriate information to identify the best available evidence in the context of medical treatment.
Clinical questions are formed using the PIO framework, where clinical issues are broken down into four components: Population/Problem (P), Intervention (I), Comparator (C), and Outcome (O). We will refer to these categories as PIO elements, by using the common practice of merging the C and I categories. In BIBREF3 a literature screening performed in 10 systematic reviews was studied. It was found that using the PIO framework can significantly improve literature screening efficacy. Therefore, efficient extraction of PIO elements is a key feature of many EBM applications and could be thought of as a multi-label sentence classification problem.
Previous works on PIO element extraction focused on classical NLP methods, such as Naive Bayes (NB), Support Vector Machines (SVM) and Conditional Random Fields (CRF) BIBREF4 , BIBREF5 . These models are shallow and limited in terms of modeling capacity. Furthermore, most of these classifiers are trained to extract PIO elements one by one which is sub-optimal since this approach does not allow the use of shared structure among the individual classifiers.
Deep neural network models have increased in popularity in the field of NLP. They have pushed the state of the art of text representation and information retrieval. More specifically, these techniques enhanced NLP algorithms through the use of contextualized text embeddings at word, sentence, and paragraph levels BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .
More recently, jin2018pico proposed a bidirectional long short term memory (LSTM) model to simultaneously extract PIO components from PubMed abstracts. To our knowledge, that study was the first in which a deep learning framework was used to extract PIO elements from PubMed abstracts.
In the present paper, we build a dataset of PIO elements by improving the methodology found in BIBREF12 . Furthermore, we built a multi-label PIO classifier, along with a boosting framework, based on the state of the art text embedding, BERT. This embedding model has been proven to offer a better contextualization compared to a bidirectional LSTM model BIBREF9 .
It was found that using the PIO framework can significantly improve literature screening efficacy.
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Does the PIO framework can significantly improve literature screening efficacy?
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Yes.
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Dăeni is a commune in Tulcea County, Northern Dobruja, Romania. It is composed of a single village, Dăeni.
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Extract the names of villages in the commune of Dăeni from the text. Separate them with a comma.
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Dăeni
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Name some famous anime shows?
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Attack on Titan, Naruto, Dragon Ball, Bleach, One Piece
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The Masters is the first major championship of the year. Since 1948, its final round has been scheduled for the second Sunday of April, with several exceptions.
The Masters has the smallest field of the four golf major championships.
The Masters is an invitation only tournament.
Previous Champions are eligible to play for life.
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Please give me a list of qualities that make the Masters is golf's top tournament unique?
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As with the other majors, winning the Masters gives a golfer several privileges which make his career more secure. Masters champions are automatically invited to play in the other three majors (the U.S. Open, The Open Championship, and the PGA Championship) for the next five years (except for amateur winners, unless they turn pro within the five-year period), and earn a lifetime invitation to the Masters. They also receive membership on the PGA Tour for the following five seasons and invitations to The Players Championship for five years.
Because the tournament was established by an amateur champion, Bobby Jones, the Masters has a tradition of honoring amateur golf. It invites winners of the most prestigious amateur tournaments in the world. Also, the current U.S. Amateur champion always plays in the same group as the defending Masters champion for the first two days of the tournament.
Amateurs in the field are welcome to stay in the "Crow's Nest" atop the Augusta National clubhouse during the tournament. The Crow's Nest is 1,200 square feet (110 m2) with lodging space for five during the competition.
The total prize money for the 2021 Masters Tournament was $11,500,000, with $2,070,000 going to the winner. In the inaugural year of 1934, the winner Horton Smith received $1,500 out of a $5,000 purse. After Nicklaus's first win in 1963, he received $20,000, while after his final victory in 1986 he won $144,000. In recent years the purse has grown quickly. Between 2001 and 2014, the winner's share grew by $612,000, and the purse grew by $3,400,000.
Green jacket
In addition to a cash prize, the winner of the tournament is presented with a distinctive green jacket, formally awarded since 1949 and informally awarded to the champions from the years prior. The green sport coat is the official attire worn by members of Augusta National while on the club grounds; each Masters winner becomes an honorary member of the club. The recipient of the green jacket has it presented to him inside the Butler Cabin soon after the end of the tournament in a televised ceremony, and the presentation is then repeated outside near the 18th green in front of the patrons. Winners keep their jacket for the year after their victory, then return it to the club to wear whenever they are present on the club grounds. Sam Snead was the first Masters champion to be awarded the green jacket after he took his first Masters title in 1949.
The green jacket is only allowed to be removed from the Augusta National grounds by the reigning champion, after which it must remain at the club. Exceptions to this rule include Gary Player, who in his joy of winning mistakenly took his jacket home to South Africa after his 1961 victory (although he has always followed the spirit of the rule and has never worn the jacket); Seve Ballesteros who, in an interview with Peter Alliss from his home in Pedreña, showed one of his two green jackets in his trophy room; and Henry Picard, whose jacket was removed from the club before the tradition was well established, remained in his closet for a number of years, and is now on display at Canterbury Golf Club in Beachwood, Ohio, where he was the club professional for many years.
By tradition, the winner of the previous year's Masters Tournament puts the jacket on the current winner at the end of the tournament. In 1966, Jack Nicklaus became the first player to win in consecutive years and he donned the jacket himself. When Nick Faldo (in 1990) and Tiger Woods (in 2002) repeated as champions, the chairman of Augusta National put the jacket on them.
In addition to the green jacket, winners of the tournament receive a gold medal. In 2017, a green jacket that was found at a thrift store in 1994 was sold at auction for $139,000.
There are several awards presented to players who perform exceptional feats during the tournament. The player who has the daily lowest score receives a crystal vase, while players who score a hole-in-one or a double eagle win a large crystal bowl. For each eagle a player makes, they receive a pair of crystal goblets.
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Advocates of the designated hitter point to the extension of many careers it has enabled, and, in a few cases, created long, productive careers for players who are weak fielders or have a history of injuries, such as Cecil Fielder and David Ortiz. Hall of Fame members George Brett, Carl Yastrzemski, Paul Molitor, Harold Baines and Edgar Martínez continued their careers longer than they ordinarily would have without the rule. Barry Bonds, who spent his entire career in the National League and even won eight Gold Gloves earlier in his career, was used strictly as a DH later in his career when the San Francisco Giants played away interleague games because of his diminishing fielding skills. Some believe that extending careers of older players is more of a disadvantage than an advantage, filling spots that otherwise may have been taken by younger players who end up not finding a place in the major leagues.
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How has the designated hitter role affected baseball players' careers?
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The designated hitter role has extended the careers of baseball players who are weak fielders or have a history of injuries. However, this means they take up team roster spots that otherwise might be filled by younger players.
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Harry Potter lives with his abusive aunt and uncle, Vernon and Petunia Dursley, and their bullying son, Dudley. On Harry's eleventh birthday, a half-giant named Rubeus Hagrid personally delivers an acceptance letter to Hogwarts School of Witchcraft and Wizardry, revealing that Harry's parents, James and Lily Potter, were wizards. When Harry was one year old, an evil and powerful dark wizard, Lord Voldemort, murdered his parents. Harry survived Voldemort's killing curse that rebounded and seemingly destroyed the Dark Lord, leaving a lightning bolt-shaped scar on his forehead. Unknown to Harry, this act made him famous in the wizarding world.
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Why was Harry Potter famous in the wizarding world?
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Harry Potter survived the killing curse of a powerful dark wizard - Lord Voldemort.
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Throughout the series, a connection is suggested between Dungeon Master and Venger. At the end of the episode "The Dragon's Graveyard", Dungeon Master calls Venger "my son". The final unproduced episode "Requiem" would have confirmed that Venger is the Dungeon Master's corrupted son (making Kareena the sister of Venger and the daughter of Dungeon Master), redeemed Venger (giving those trapped in the realm their freedom), and ended on a cliffhanger where the six children could finally return home or deal with evil that still existed in the realm.
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According to the paragraph below, what is the relationship between Dungeon Master and Venger?
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Venger is Dungeon Master's son.
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| 16
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Data annotation is a major bottleneck for the application of supervised learning approaches to many problems. As a result, unsupervised methods that learn directly from unlabeled data are increasingly important. For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent years – for example, for both part-of-speech (POS) induction BIBREF0 , BIBREF1 and unsupervised dependency parsing BIBREF2 , BIBREF3 , BIBREF4 . While similar models have had success on a range of unsupervised tasks, they have mostly ignored the apparent utility of continuous word representations evident from supervised NLP applications BIBREF5 , BIBREF6 . In this work, we focus on leveraging and explicitly representing continuous word embeddings within unsupervised models of syntactic structure.
Pre-trained word embeddings from massive unlabeled corpora offer a compact way of injecting a prior notion of word similarity into models that would otherwise treat words as discrete, isolated categories. However, the specific properties of language captured by any particular embedding scheme can be difficult to control, and, further, may not be ideally suited to the task at hand. For example, pre-trained skip-gram embeddings BIBREF7 with small context window size are found to capture the syntactic properties of language well BIBREF8 , BIBREF9 . However, if our goal is to separate syntactic categories, this embedding space is not ideal – POS categories correspond to overlapping interspersed regions in the embedding space, evident in Figure SECREF4 .
In our approach, we propose to learn a new latent embedding space as a projection of pre-trained embeddings (depicted in Figure SECREF5 ), while jointly learning latent syntactic structure – for example, POS categories or syntactic dependencies. To this end, we introduce a new generative model (shown in Figure FIGREF6 ) that first generates a latent syntactic representation (e.g. a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function. The latent embeddings can be jointly learned with the structured syntax model in a completely unsupervised fashion.
By choosing an invertible neural network as our non-linear projector, and then parameterizing our model in terms of the projection's inverse, we are able to derive tractable exact inference and marginal likelihood computation procedures so long as inference is tractable in the underlying syntax model. In sec:learn-with-inv we show that this derivation corresponds to an alternate view of our approach whereby we jointly learn a mapping of observed word embeddings to a new embedding space that is more suitable for the syntax model, but include an additional Jacobian regularization term to prevent information loss.
Recent work has sought to take advantage of word embeddings in unsupervised generative models with alternate approaches BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . BIBREF9 build an HMM with Gaussian emissions on observed word embeddings, but they do not attempt to learn new embeddings. BIBREF10 , BIBREF11 , and BIBREF12 extend HMM or dependency model with valence (DMV) BIBREF2 with multinomials that use word (or tag) embeddings in their parameterization. However, they do not represent the embeddings as latent variables.
In experiments, we instantiate our approach using both a Markov-structured syntax model and a tree-structured syntax model – specifically, the DMV. We evaluate on two tasks: part-of-speech (POS) induction and unsupervised dependency parsing without gold POS tags. Experimental results on the Penn Treebank BIBREF13 demonstrate that our approach improves the basic HMM and DMV by a large margin, leading to the state-of-the-art results on POS induction, and state-of-the-art results on unsupervised dependency parsing in the difficult training scenario where neither gold POS annotation nor punctuation-based constraints are available.
To this end, we introduce a new generative model (shown in Figure 2) that first generates a latent syntactic representation (e.g. a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function.
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Is their model a generative model or a discriminative model?
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A new generative model.
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| 46
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The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD.
The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document.
Here we choose QANet [3] as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF [18] when reaching the same performance on SQuAD.
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Why do authors choose QANet as the base model?
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1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF [18] when reaching the same performance on SQuAD.
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Which is a bird or fish: Red-backed shrike, Red salmon
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Red salmon is a fish, Red-backed shrike is a bird
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Germany is the ninth-most visited country in the world as of 2017, with 37.4 million visits. Domestic and international travel and tourism combined directly contribute over €105.3 billion to German GDP. Including indirect and induced impacts, the industry supports 4.2 million jobs.
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Based on this paragraph, how many jobs are created by tourism in Germany?
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4.2 million jobs are created by tourism in Germany.
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In 1982, Nintendo developed a prototype system called the Advanced Video System (AVS). Its accessories include controllers, a tape drive, a joystick, and a lightgun. The system can be used as a simple home computer. It was never released and is on display at the Nintendo World Store in New York. In July 1983, Nintendo released the Family Computer console in Japan, as its first attempt at a cartridge-based video game console. More than 500,000 units were sold within two months at around US$100. After a few months of favorable sales, Nintendo received complaints that some Famicom consoles would freeze on certain games. The fault was found in a malfunctioning chip and Nintendo decided to recall all Famicom units that were currently on store shelves, at a cost of approximately half a million USD.
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From the passage provided, extract all the accessories provided with the Advanced Video System
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Accessories for the Advanced Video System (AVS) were controllers, a tape drive, a joystick, and a lightgun.
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What is the Evergreen Game?
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The Evergreen Game is a famous chess game won by Adolf Anderssen against Jean Dufresne in 1852.
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1912.05066
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This paper presents a study that compares the opinions of users on microblogs, which is essentially the crowd wisdom, to that of the experts in the field. Specifically, we explore three datasets: US Presidential Debates 2015-16, Grammy Awards 2013, Super Bowl 2013. We determined if the opinions of the crowd and the experts match by using the sentiments of the tweets to predict the outcomes of the debates/Grammys/Super Bowl. We observed that in most of the cases, the predictions were right indicating that crowd wisdom is indeed worth looking at and mining sentiments in microblogs is useful. In some cases where there were disagreements, however, we observed that the opinions of the experts did have some influence on the opinions of the users. We also find that the features that were most useful in our case of multi-label classification was a combination of the document-embedding and topic features.
This paper presents a study that compares the opinions of users on microblogs, which is essentially the crowd wisdom, to that of the experts in the field. Specifically, we explore three datasets: US Presidential Debates 2015-16, Grammy Awards 2013, Super Bowl 2013.
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Who are the experts?
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The answers are shown as follows:
* the experts in the field
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What elements make a soap opera and what are examples?
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A soap opera is typically a long-running show that follows a family. Audiences associate the following key elements with the genre. Most notably, there is family drama regarding the struggle for power over a position.
There’s also sibling rivalry, sometimes in the form of a love triangle. For example, the same woman is the love interest of two brothers. Soap operas are also known for a character’s unidentified paternity, also known as “Who’s the Daddy?” or WTD storyline. Such a plot point typically arises from the previously mentioned love triangle.
Another element soap operas are best known for is soap opera rapid aging syndrome (SORAS), in which a child character is not seen on-screen for a long period of time so that the character is rapidly aged so that they may now participate in adult storylines.
The final element that characterizes a soap opera is a secret relative that seemingly appears out of nowhere, usually to challenge a main character for power.
The best known examples of soap operas are shows on daytime television such as General Hospital, Days of Our Lives, and The Young & the Restless. However, soap operas can also include primetime shows such as Grey’s Anatomy and even blockbuster movies such as Black Panther and Black Panther: Wakanda Forever, which exhibit many elements that allow for these works to be considered soap operas.
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What is bond duration?
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Duration is a time-weighted measurement of the average cash flow of an interest paying financial instrument. Bond investors use duration to measure the interest rate sensitivity of an asset. Type of duration calculations include Macaulay, Modified and Effective durations.
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List some American candy bars that have chocolate and caramel.
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Twix, Snickers, Rolo, Caramel M&Ms, Milky Way, Milk Duds
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From February 2019 – 2020, average monthly temperatures in Germany ranged from a low of 3.3 °C (37.9 °F) in January 2020 to a high of 19.8 °C (67.6 °F) in June 2019. Average monthly precipitation ranged from 30 litres per square metre in February and April 2019 to 125 litres per square metre in February 2020. Average monthly hours of sunshine ranged from 45 in November 2019 to 300 in June 2019.
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Based on this text, what average temperature in degree celsius can I expect in April in Germany?
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The average temperature in April in Germany is 13.2 °C.
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What is Ayurveda?
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Ayurveda is a natural system of medicine that originated in India more than 3,000 years ago. The term Ayurveda is derived from the Sanskrit words ayur (life) and veda (science or knowledge). Thus, Ayurveda translates to knowledge of life. Based on the idea that disease is due to an imbalance or stress in a person's consciousness, Ayurveda encourages certain lifestyle interventions and natural therapies to regain a balance between the body, mind, spirit, and the environment.
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Select the odd word from this list and explain why:
Chair
Sofa
Table
Dinosaur
Cupboard
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Dinosaur is the odd word in this list. All of the other words are items of furniture, but dinosaurs are large extinct reptiles, not items of furniture.
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1905.00563
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FLOAT SELECTED: Table 2: Data Statistics of the benchmarks.
FLOAT SELECTED: Table 2: Data Statistics of the benchmarks.
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What datasets are used to evaluate this approach?
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Kinship and Nations knowledge graphs, YAGO3-10 and WN18KGs knowledge graphs
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| 63
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The elementary system Segmenter$_{\mu }$ (baseline) relies solely on a list of discursive markers to perform the segmentation. It replaces the appearance of a marker in the list with a special symbol, for example $\mu $, which indicates a boundary between the right and left segment. Be the sentence of the preceding example: La ville d'Avignon est la capitale du Vaucluse, qui est un département du Sud de la France.. The Segmenter split the sentence in two parts: the left segment (SE), La ville d'Avignon est la capitale du Vaucluse, and the right segment (SD), est un département du sud de la France.
The Segmenter$_{\mu +}$ system presents an improvement to the Segmenter$_{\mu }$: inclusion of grammar categories with the TreeTagger tool. The advantage of this system is the detection of certain grammatical forms in order to condition the segmentation. Since it is based on the Segmenter$_{\mu }$, we try to recognise the opportune conditions to gather two segments when both are part of the same discursive segment. We try to identify more subtly when it is pertinent to leave the two segments separate. The Segmenter$_{\mu }$ has two distinct strategies:
Segmentador$_{\mu +V}$ (verbal version, V): it relies solely on the presence of verbal forms to the right and left of the discursive marker. The two grammatical rules of this strategy are:
If there are no verbs in the left and right segments, regroup them.
If there is at least one verb in the left or right segment, the segments will remain separate.
Segmenter$_{\mu +(V-N)}$ (verb-noun version, V-N): it relies on the presence of verbs and nouns. For this version, four rules are considered:
If there is no noun in either the left or right segment, we regroup the segments.
We regroup the segments if at least one of them has no noun.
If at least one noun is present in both segments, they remain independent.
If there is no verb-nominal form, the segments remain independent.
The elementary system SEGMENTERµ (baseline) relies solely on a list of discursive markers to perform the segmentation.
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What is their baseline?
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SEGMENTERµ.
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The earliest references to peanut butter can be traced to Aztec and Inca civilizations, who ground roasted peanuts into a paste.[clarification needed] However, several people can be credited with the invention of modern peanut butter and the processes involved in making it.[according to whom?]
The US National Peanut Board credits three modern inventors with the earliest patents related to the production of modern peanut butter. Marcellus Gilmore Edson of Montreal, Quebec, Canada, obtained the first patent for a method of producing peanut butter from roasted peanuts using heated surfaces in 1884. Edson's cooled product had "a consistency like that of butter, lard, or ointment" according to his patent application which described a process of milling roasted peanuts until the peanuts reached "a fluid or semi-fluid state". He mixed sugar into the paste to harden its consistency.[citation needed]
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From the text below about the history of peanut butter, where can the first reference be traced from?
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The earliest reference to peanut butter can be traced to Aztec and Inca civilizations.
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What is a flat earther and is it possible for the world to be flat?
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A flat earther is someone who believes the world is flat. They also believe that there is scientific evidence to support this claim. They do not believe the earth is round, even when we have satellite images of the earth to prove that it is. Basic laws of physics can also disprove that the earth is flat. The physics community should respond to these individuals to prevent the propagation of false information.
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what was the world's first stored-program computer?
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The Manchester Baby was the world's first stored-program computer. It was built at the University of Manchester in England by Frederic C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948. It was designed as a testbed for the Williams tube, the first random-access digital storage device.
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what method of commuting works best to get from Marin County to downtown San Francisco?
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There are many ways to get from Marin County to downtown San Francisco. You can drive a car, which can be fast in the morning, taking, say, 45 minutes or less if you leave before 8am. Driving home, however, can take up to an hour and a half or more depending on traffic.
You can take a bus, which generally takes about an hour and fifteen minutes either way. You can also take a bicycle to a bus stop, and the bus will carry your bike, but the spaces for bikes can fill up before you get on the bus, so check the route before trying this method.
Ferries that take you from Larkspur to the Ferry Building are also very popular. You can drive to the ferry landing, park your car, and get on the ferry to downtown San Francisco. When the weather is nice, this can be a wonderful way to either unwind or catch up on work. Ferries can be subject to weather delays and cancellations, so check the weather before trying this method.
Finally, you can take a bicycle or even run all the way in to downtown San Francisco from Marin County. Bicycling is generally an hour to hour and a half round trip, but again check the weather and your fitness level before committing to this. And most of all, make sure you eat enough calories during the day to fuel that ride home! There are some intrepid souls who have taken to the water and swam from Cavallo Point to Aquatic Park, which takes a lot of practice and planning.
There are many ways to commute to San Francisco from Marin, so feel free to mix and match and try new methods to keep yourself happy, fit, and motivated for that awesome job in the city!
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The topic of question generation, initially motivated for educational purposes, is tackled by designing many complex rules for specific question types BIBREF4, BIBREF23. Heilman2010GoodQS improve rule-based question generation by introducing a statistical ranking model. First, they remove extraneous information in the sentence to transform it into a simpler one, which can be transformed easily into a succinct question with predefined sets of general rules. Then they adopt an overgenerate-and-rank approach to select the best candidate considering several features.
With the rise of dominant neural sequence-to-sequence learning models BIBREF5, Du2017LearningTA frame question generation as a sequence-to-sequence learning problem. Compared with rule-based approaches, neural models BIBREF24 can generate more fluent and grammatical questions. However, question generation is a one-to-many sequence generation problem, i.e., several aspects can be asked given a sentence, which confuses the model during train and prevents concrete automatic evaluation. To tackle this issue, Zhou2017NeuralQG propose the answer-aware question generation setting which assumes the answer is already known and acts as a contiguous span inside the input sentence. They adopt a BIO tagging scheme to incorporate the answer position information as learned embedding features in Seq2Seq learning. Song2018LeveragingCI explicitly model the information between answer and sentence with a multi-perspective matching model. Kim2019ImprovingNQ also focus on the answer information and proposed an answer-separated Seq2Seq model by masking the answer with special tokens. All answer-aware neural models treat question generation as a one-to-one mapping problem, but existing models perform poorly for sentences with a complex structure (as shown in Table TABREF2).
Our work is inspired by the process of extraneous information removing in BIBREF0, BIBREF25. Different from Heilman2010GoodQS which directly use the simplified sentence for generation and cao2018faithful which only consider aggregate two sources of information via gated attention in summarization, we propose to combine the structured answer-relevant relation and the original sentence. Factoid question generation from structured text is initially investigated by Serban2016GeneratingFQ, but our focus here is leveraging structured inputs to help question generation over unstructured sentences. Our proposed model can take advantage of unstructured sentences and structured answer-relevant relations to maintain informativeness and faithfulness of generated questions. The proposed model can also be generalized in other conditional sequence generation tasks which require multiple sources of inputs, e.g., distractor generation for multiple choice questions BIBREF26.
Compared with rule-based approaches, neural models (Yuan et al., 2017) can generate more fluent and grammatical questions.
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Which models are superior in generating fluent and grammatical questions, rule-based approaches or neural models?
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Neural models.
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The Panama Canal is an artificial 82 km (51 mi) waterway in Panama that connects the Atlantic Ocean with the Pacific Ocean and divides North and South America. The canal cuts across the Isthmus of Panama and is a conduit for maritime trade. One of the largest and most difficult engineering projects ever undertaken, the Panama Canal shortcut greatly reduces the time for ships to travel between the Atlantic and Pacific oceans, enabling them to avoid the lengthy, hazardous Cape Horn route around the southernmost tip of South America via the Drake Passage or Strait of Magellan.
Colombia, France, and later the United States controlled the territory surrounding the canal during construction. France began work on the canal in 1881, but stopped because of lack of investors' confidence due to engineering problems and a high worker mortality rate. The United States took over the project on May 4, 1904, and opened the canal on August 15, 1914. The US continued to control the canal and surrounding Panama Canal Zone until the 1977 Torrijos–Carter Treaties provided for its handover to Panama. After a period of joint American–Panamanian control, the canal was taken over by the Panamanian government in 1999. It is now managed and operated by the government-owned Panama Canal Authority.
Canal locks at each end lift ships up to Gatun Lake, an artificial lake 26 meters (85 ft) above sea level, created to reduce the amount of excavation work required for the canal, and then lower the ships at the other end. The original locks are 33.5 meters (110 ft) wide. A third, wider lane of locks was constructed between September 2007 and May 2016. The expanded waterway began commercial operation on June 26, 2016. The new locks allow transit of larger, New Panamax ships.
Annual traffic has risen from about 1,000 ships in 1914, when the canal opened, to 14,702 vessels in 2008, for a total of 333.7 million Panama Canal/Universal Measurement System (PC/UMS) tons. By 2012, more than 815,000 vessels had passed through the canal. In 2017 it took ships an average of 11.38 hours to pass between the canal's two locks. The American Society of Civil Engineers has ranked the Panama Canal one of the seven wonders of the modern world.
|
From this passage, find out who controls the Panama Canal
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Colombia, France, and later the United States controlled the territory surrounding the Panama canal during its construction. France started the project in 1881 and passed the baton to the United States in 1904. After finishing the project in 1914, the US continued to control the canal and surrounding Panama Canal Zone till 1977. There onwards, after a period of joint American–Panamanian control, the canal was taken over by the Panamanian government in 1999. It is now managed and operated by the government-owned Panama Canal Authority.
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South India, also known as Peninsular India, consists of the peninsular southern part of India. It encompasses the Indian states of Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and Telangana, as well as the union territories of the Andaman and Nicobar Islands, Lakshadweep and Puducherry, comprising 19.31% of India's area (635,780 km2 or 245,480 sq mi) and 20% of India's population. Covering the southern part of the peninsular Deccan Plateau, South India is bounded by the Bay of Bengal in the east, the Arabian Sea in the west and the Indian Ocean in the south. The geography of the region is diverse with two mountain ranges – the Western and Eastern Ghats – bordering the plateau heartland. The Godavari, Krishna, Kaveri, Tungabhadra, Periyar, Bharathappuzha, Pamba, Thamirabarani, Palar, and Vaigai rivers are important perennial rivers.
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From the passage provided, extract the union territories that encompass South India. Separate them with a comma.
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Andaman and Nicobar Islands, Lakshadweep and Puducherry
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What is the best University in Canada for computer Science?
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To decide which university is the best university is a rather subjective choice, however the consensus (based on multiple University rankings) is that The University of Waterloo is the best school to study computer science.
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null | false
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Give me a bulleted list of some of the greatest parks in London
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Here is a list of London's greatest parks:
- Hampstead Heath
- Regent’s Park
- Greenwich Park
- Victoria Park
- Holland Park
- Richmond Park
- Green Park
- Hyde Park
- St James’s Park
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1808.03894
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Instead of considering individual dimensions of the gating signals, we aggregate them to consider their norm, both for the signal and for its saliency. Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.
Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.
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How many layers are there in their model?
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The answers are shown as follows:
* two LSTM layers
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The 149th Boat Race took place on 6 April 2003. Held annually, the Boat Race is a side-by-side rowing race between crews from the Universities of Oxford (in dark blue) and Cambridge (in light greenish blue) along a 4.2-mile (6.8 km) tidal stretch of the River Thames in south-west London. The lead changed twice during the race, which Oxford won by one foot (30 cm), the smallest margin of victory in the history of the event. The close race has been described as "epic," while Olympic gold medalist Steve Redgrave suggested that the race was the "greatest we will see in our lifetimes."
|
What is the 149th Boat Race?
|
The 149th Boat Race is a side-by-side rowing race along a 4.2 mile tidal stretch of the River Thames in south-west London that took place on April 6th, 2003 between the University of Oxford and the University of Cambridge.
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Identify from this list which are Crayola crayon colors: banana biscuits, razzle dazzle rose, whoopsie daisies, purple pizzazz, carnation pink, cotton candy, unmellow yellow, macaroni and cheese, coffee table blue, craisin, aquamarine, jazzberry jam, sea green
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Razzle dazzle rose, purple pizzazz, carnation pink, cotton candy, unmellow yellow, macaroni and cheese, aquamarine, jazzberry jam, sea green
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null | false
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Describe these items as expensive or cheap to buy: a Rolex watch, a box of playing cards, a car, a laptop computer, a gallon of milk, an engagement ring, a house
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a Rolex watch: expensive, a box of playing cards: cheap, a car: expensive, a laptop computer: expensive, a gallon of milk: cheap, an engagement ring: expensive, a house: expensive
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| 164
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In June 2015, the operators of the online discussion site Reddit banned several communities under new anti-harassment rules. BIBREF0 used this opportunity to combine rich online data with computational methods to study a current question: Does eliminating these “echo chambers” diminish the amount of hate speech overall? Exciting opportunities like these, at the intersection of “thick” cultural and societal questions on the one hand, and the computational analysis of rich textual data on larger-than-human scales on the other, are becoming increasingly common.
Indeed, computational analysis is opening new possibilities for exploring challenging questions at the heart of some of the most pressing contemporary cultural and social issues. While a human reader is better equipped to make logical inferences, resolve ambiguities, and apply cultural knowledge than a computer, human time and attention are limited. Moreover, many patterns are not obvious in any specific context, but only stand out in the aggregate. For example, in a landmark study, BIBREF1 analyzed the authorship of The Federalist Papers using a statistical text analysis by focusing on style, based on the distribution of function words, rather than content. As another example, BIBREF2 studied what defines English haiku and showed how computational analysis and close reading can complement each other. Computational approaches are valuable precisely because they help us identify patterns that would not otherwise be discernible.
Yet these approaches are not a panacea. Examining thick social and cultural questions using computational text analysis carries significant challenges. For one, texts are culturally and socially situated. They reflect the ideas, values and beliefs of both their authors and their target audiences, and such subtleties of meaning and interpretation are difficult to incorporate in computational approaches. For another, many of the social and cultural concepts we seek to examine are highly contested — hate speech is just one such example. Choices regarding how to operationalize and analyze these concepts can raise serious concerns about conceptual validity and may lead to shallow or obvious conclusions, rather than findings that reflect the depth of the questions we seek to address.
These are just a small sample of the many opportunities and challenges faced in computational analyses of textual data. New possibilities and frustrating obstacles emerge at every stage of research, from identification of the research question to interpretation of the results. In this article, we take the reader through a typical research process that involves measuring social or cultural concepts using computational methods, discussing both the opportunities and complications that often arise. In the Reddit case, for example, hate speech is measured, however imperfectly, by the presence of particular words semi-automatically extracted from a machine learning algorithm. Operationalizations are never perfect translations, and are often refined over the course of an investigation, but they are crucial.
We begin our exploration with the identification of research questions, proceed through data selection, conceptualization, and operationalization, and end with analysis and the interpretation of results. The research process sounds more or less linear this way, but each of these phases overlaps, and in some instances turns back upon itself. The analysis phase, for example, often feeds back into the original research questions, which may continue to evolve for much of the project. At each stage, our discussion is critically informed by insights from the humanities and social sciences, fields that have focused on, and worked to tackle, the challenges of textual analysis—albeit at smaller scales—since their inception.
In describing our experiences with computational text analysis, we hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thick social and cultural concepts. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many. And this leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be.
We begin our exploration with the identification of research questions, proceed through data selection, conceptualization, and operationalization, and end with analysis and the interpretation of results.
|
How does the team summarize their research process in brief?
|
They begin their exploration with the identification of research questions, proceed through data selection, conceptualization, and operationalization, and end with analysis and the interpretation of results.
|
1909.00153
| true
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In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied. (We note that the batch size and learning rates are the same for all the languages in MLDoc, so the variation seen in Figure FIGREF15 are not affected by those factors.)
In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied.
|
Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
|
Yes.
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null | false
| 170
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The study of gender and language has a rich history in social science. Its roots are often attributed to Robin Lakoff, who argued that language is fundamental to gender inequality, “reflected in both the ways women are expected to speak, and the ways in which women are spoken of” BIBREF2. Prominent scholars following Lakoff have included Deborah Tannen BIBREF3, Mary Bucholtz and Kira Hall BIBREF4, Janet Holmes BIBREF5, Penelope Eckert BIBREF6, and Deborah Cameron BIBREF7, along with many others.
In recent decades, the study of gender and language has also attracted computational researchers. Echoing Lakoff's original claim, a popular strand of computational work focuses on differences in how women and men talk, analyzing key lexical traits BIBREF8, BIBREF9, BIBREF10 and predicting a person's gender from some text they have written BIBREF11, BIBREF12. There is also research studying how people talk to women and men BIBREF13, as well as how people talk about women and men, typically in specific domains such as sports journalism BIBREF14, fiction writing BIBREF15, movie scripts BIBREF16, and Wikipedia biographies BIBREF17, BIBREF18. Our work builds on this body by diving into two novel domains: celebrity news, which explores gender in pop culture, and student reviews of CS professors, which examines gender in academia and, particularly, the historically male-dominated field of CS. Furthermore, many of these works rely on manually constructed lexicons or topics to pinpoint gendered language, but our methods automatically infer gender-associated words and labeled clusters, thus reducing supervision and increasing the potential to discover subtleties in the data.
Modeling gender associations in language could also be instrumental to other NLP tasks. Abusive language is often founded in sexism BIBREF0, BIBREF1, so models of gender associations could help to improve detection in those cases. Gender bias also manifests in NLP pipelines: prior research has found that word embeddings preserve gender biases BIBREF19, BIBREF20, BIBREF21, and some have developed methods to reduce this bias BIBREF22, BIBREF23. Yet, the problem is far from solved; for example, BIBREF24 showed that it is still possible to recover gender bias from “de-biased” embeddings. These findings further motivate our research, since before we can fully reduce gender bias in embeddings, we need to develop a deeper understanding of how gender permeates through language in the first place.
We also build on methods to cluster words in word embedding space and automatically label clusters. Clustering word embeddings has proven useful for discovering salient patterns in text corpora BIBREF25, BIBREF26. Once clusters are derived, we would like them to be interpretable. Much research simply considers the top-n words from each cluster, but this method can be subjective and time-consuming to interpret. Thus, there are efforts to design methods of automatic cluster labeling BIBREF27. We take a similar approach to BIBREF28, who leverage word embeddings and WordNet during labeling, and we extend their method with additional techniques and evaluations.
The study of gender and language has a rich history in social science. Its roots are often attributed to Robin Lakoff, who argued that language is fundamental to gender inequality, “reflected in both the ways women are expected to speak, and the ways in which women are spoken of” (Lakoff, 1973).
|
Who is the root of gender and language study?
|
Robin Lakoff, who argued that language is fundamental to gender inequality, “reflected in both the ways women are expected to speak, and the ways in which women are spoken of”.
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| 54
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Knowledge graphs BIBREF0 enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants. Knowledge graphs are typically interpreted as comprising of discrete triples of the form (entityA, relationX, entityB) thus representing a relation (relationX) between entityA and entityB. However, one limitation of only a discrete representation of triples is that it does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph. Consequently, one popular alternative is to learn dense continuous representations of entities and relations by embedding them in latent continuous vector spaces, while seeking to model the inherent structure of the knowledge graph. Most knowledge graph embedding methods can be classified into two major classes: one class which operates purely on triples like RESCAL BIBREF1 , TransE BIBREF2 , DistMult BIBREF3 , TransD BIBREF4 , ComplEx BIBREF5 , ConvE BIBREF6 and the second class which seeks to incorporate additional information (like multi-hops) BIBREF7 . Learning high-quality knowledge graph embeddings can be quite challenging given that (a) they need to effectively model the contextual usages of entities and relations (b) they would need to be useful for a variety of predictive tasks on knowledge graphs.
In this paper, we present a new type of knowledge graph embeddings called Dolores that are both deep and contextualized. Dolores learns both context-independent and context-dependent embeddings of entities and relations through a deep neural sequential model. Figure 1 illustrates the deep contextualized representations learned. Note that the contextually independent entity embeddings (see Figure 1 ) reveal three clusters of entities: writers, philosophers, and musicians. The contextual dependent embeddings in turn effectively account for specific relations. In particular, the context-dependent representations under the relation nationality now nicely cluster the above entities by nationality namely Austrians, Germans, and British/Irish. Similarly Figure 1 shows contextual embeddings given the relation place-lived. Note that these embeddings correctly capture that even though Beethoven and Brahms being Germans, they lived in Vienna and are closer to other Austrian musicians like Schubert.
Unlike most knowledge graph embeddings like TransD, TransE BIBREF2 , BIBREF4 etc. which are typically learned using shallow models, the representations learned by Dolores are deep: dependent on an entire path (rather than just a triple), are functions of internal states of a Bi-Directional LSTM and composed of representations learned at various layers potentially capturing varying degrees of abstractions. Dolores is inspired by recent advances in learning word representations (word embeddings) from deep neural language models using Bi-Directional LSTMs BIBREF8 . In particular, we derive connections between the work of Peters et al. ( BIBREF8 ) who learn deep contextualized word embeddings from sentences using a Bi-Directional LSTM based language model and random walks on knowledge graphs. These connections enable us to propose new “deep contextualized” knowledge graph embeddings which we call Dolores embeddings.
Knowledge Embeddings learned using Dolores can easily be used as input representations for predictive models on knowledge graphs. More importantly, when existing predictive models use input representations for entities and relations, we can easily replace those representations with Dolores representations and significantly improve the performance of existing models. Specifically, we show that Dolores embeddings advance the state-of-the-art models on various tasks like link prediction, triple classification and missing relation type prediction.
To summarize, our contributions are as follows:
Learning high-quality knowledge graph embeddings can be quite challenging given that (a) they need to effectively model the contextual usages of entities and relations (b) they would need to be useful for a variety of predictive tasks on knowledge graphs.
|
Why can learning high quality knowledge graph embedding be challenging?
|
(a) They need to effectively model the contextual usages of entities and relations. (b) They would need to be useful for a variety of predictive tasks on knowledge graphs.
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How many strings are on a guitar?
|
Standard guitars contain 6 strings: (high) E, B, G, D, A, and (low) E are the notes associated with the 6 strings.
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| 182
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Personalized dialogue agents have been shown efficient in conducting human-like conversation. This progress has been catalyzed thanks to existing conversational dataset such as Persona-chat BIBREF0, BIBREF1. However, the training data are provided in a single language (e.g., English), and thus the resulting systems can perform conversations only in the training language. For wide, commercial dialogue systems are required to handle a large number of languages since the smart home devices market is increasingly international BIBREF2. Therefore, creating multilingual conversational benchmarks is essential, yet challenging since it is costly to perform human annotation of data in all languages.
A possible solution is to use translation systems before and after the model inference, a two-step translation from any language to English and from English to any language. This comes with three major problems: 1) amplification of translation errors since the current dialogue systems are far from perfect, especially with noisy input; 2) the three-stage pipeline system is significantly slower in terms of inference speed; and 3) high translation costs since the current state-of-the-art models, especially in low resources languages, are only available using costly APIs.
In this paper, we analyze two possible workarounds to alleviate the aforementioned challenges. The first is to build a cross-lingual transferable system by aligning cross-lingual representations, as in BIBREF3, in which the system is trained on one language and zero-shot to another language. The second is to learn a multilingual system directly from noisy multilingual data (e.g., translated data), thus getting rid of the translation system dependence at inference time.
To evaluate the aforementioned systems, we propose a dataset called Multilingual Persona-Chat, or XPersona, by extending the Persona-Chat corpora BIBREF1 to six languages: Chinese, French, Indonesian, Italian, Korean, and Japanese. In XPersona, the training sets are automatically translated using translation APIs with several human-in-the-loop passes of mistake correction. In contrast, the validation and test sets are annotated by human experts to facilitate both automatic and human evaluations in multiple languages.
Furthermore, we propose competitive baselines in two training settings, namely, cross-lingual and multilingual, and compare them with translation pipeline models. Our baselines leverage pre-trained cross-lingual BIBREF4 and multilingual BIBREF5 models.
An extensive automatic and human evaluation BIBREF6 of our models shows that a multilingual system is able to outperform strong translation-based models and on par with or even improve the monolingual model. The cross-lingual performance is still lower than other models, which indicates that cross-lingual conversation modeling is very challenging. The main contribution of this paper are summarized as follows:
We present the first multilingual non-goal-oriented dialogue benchmark for evaluating multilingual generative chatbots.
We provide both cross-lingual and multilingual baselines and discuss their limitations to inspire future research.
We show the potential of multilingual systems to understand the mixed language dialogue context and generate coherent responses.
An extensive automatic and human evaluation of our models shows that a multilingual system is able to outperform strong translation-based models and on par with or even improve the monolingual model.
|
Is a multilingual system able to outperform strong translation-based models?
|
Yes.
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| null |
Why is music so special?
|
Music is an art form consisting of arranging sound to create a combination of rhythm and harmonies to convey emotions to individuals. It can be considered a unique art form by certain people due to its ephemeral aspect as opposed to other art types such as painting or sculpture which are mostly permanent. Music can generate a large variety of emotions in individuals through the mere sense of hearing it. Research has shown that listening to music activates multiple areas of the brain and is known to liberate important amounts of dopamine which contribute to giving pleasure to the listener. On the other hand, learning and playing music requires several mental and physical skills such as ear and relative pitch, rhythm, and hand coordination.
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