paper_id
stringlengths 10
10
⌀ | yes_no
bool 2
classes | paper_index
int64 0
519
⌀ | evidence
stringlengths 0
37.7k
| question
stringlengths 4
11.7k
| answer
stringlengths 1
26k
|
|---|---|---|---|---|---|
null | false
| null |
Road Trip to Vegas via Death Valley, example guide
|
While Vegas the sin city doesn't need reason a trip to Death valley does make the visit exiting.
1-It is advisible to leave early morning when travelling from LA Suggested route is Hwy 14; it gets you out of the metro area earliest.
2-Furnace Creek Ranch is about 260 miles .its one of the key places to stop on road.
3-Visit Devil’s Golf Course, Badwater, it is a good detour.
4-A stop at Zabriskie Point when you get back on 190, and the mesmerizing ride up to
Dante’s View,
5-The direct way from Furnace Creek to Las Vegas is 190 to Death Valley Junction, take turn
to east on Stateline Road for Pahrump.
|
|
null | false
| 188
|
For this report, we are exploring the field of natural language processing, which is the broad study of how computers and machines can understand human to human communication and how texts are analyzed based on contextual information by machines.
In particular, we are using natural language processing to classify news articles as real news or “fake news”. Fake news is misinformation masked under the guise of a real news article, and is used to deceptively influence people’s beliefs.
For this report, we are classifying news articles as “real” or “fake”, which will be a binary classification problem - classifying the samples as a positive (with fake news) or negative (not fake news) sample. Many studies have used machine learning algorithms and build classifiers based on features like content, the author’s name and job-title, using lots of models like the convolutional neural network (CNN), recurrent neural network (RNN), feed-forward neural network (FFNN), long-short term memory (LSTM) and logistic regression to find the most optimal model and return its results. In [1], the author built a classifier using natural language processing and used models like CNN, RNN, FFNN, and Logistic Regression and concluded that the CNN classifiers could not be as competitive as the RNN classifiers. The authors in [2] think that their study can be improved by having more features like knowing the history of lies spoken by the news reporter or the speaker.
Moreover, apart from the traditional machine learning methods, new models have also been developed. One of the newer models, TraceMiner, creates an LSTM-RNN model inferring from the embedding of social media users in the social network structure to propagate through the path of messages and has provided high classification accuracy$^{5}$. FAKEDETECTOR is another inference model developed to detect the credibility of the fake news which is considered to be quite reliable and accurate$^{7}$.
There also have been studies that have a different approach. A paper surveys the current state-of-the-art technologies that are imperative when adopting and developing fake news detection and provides a classification of several accurate assessment methods that analyze the text and detect anomalies$^{3}$.
These previous approaches lack a clear contextual analysis used in NLP. We considered the semantic meaning of each word and we feel that the presence of particular words influence the meaning. We reckoned this important since we felt the contextual meaning of the text needs to be preserved and analyzed for better classification. Other studies emphasize the user and features related to them. In [4], “45 features…[were used] for predicting accuracy...across four types: structural, user, content, and temporal,” so features included characteristics beyond the text. Article [6] "learn[s] the representations of news articles, creators and subjects simultaneously." In our project, we emphasize the content by working with articles whose labels only relate to the text, nothing outside that scope, and have used SVM, Logistic Regression, ANN, LSTM, and Random Forest.
We had devised this problem into 3 different phases: pre-processing, text-to-numeric representation conversion using pre-trained algorithms, and then evaluate the models using state-of-the-art machine learning algorithms. We had analysed the data set and in particular the text part of the data explaining how it is distributed and then we converted each text into numeric representation using pre-training models such as TFIDF, CV and W2V for vector representation. Finally, we evaluated our numeric conversion data using significant machine learning algorithms such as neural networks, classification algorithms etc to perform the classification.
For this report, we are exploring the field of natural language processing, which is the broad study of how computers and machines can understand human to human communication and how texts are analyzed based on contextual information by machines.
|
What field does the paper explore?
|
Natural language processing.
|
null | false
| null |
Tell me a number of ways that one could learn about data science
|
There are many methods for learning Data Science and the path taken will depend on your preferred learning style. Here are some suggestions:
- read books that describe data science methods
- enrol in courses available on Massive Open Online Course (mooc) providers like Coursera
- access courses on specialists websites such as deeplearning.ai or fast.ai
- enrol in a university course with a Data Science curriculum
- join a Data Science meetup group
|
|
null | false
| null |
is Lakeview replacing dashboard
|
No
|
|
null | false
| null |
How can I become more active and do more exercise?
|
Here are some ideas that you can add to your daily life to increase activity and build a regular exercise routine.
Riding a bike - you could ride your bike to work or take a casual ride with a friend or family member, if you prefer to stay indoors take a spin class at a local gym
Walking - take your dog for a walk or borrow a dog from a friend or neighbour this could make walking more fun. If you are commuting to work you could get off the train or bus a stop early and walk to the office or take the stairs instead of the elevator.
Running - all you need is your trainers and minimum sports kit, you could get up 30 minutes early and take a short run before work, if you enjoy this sign up for a charity run for extra motivation, or join a local park run
Swimming - a low-impact activity that will help improve all aspects of your fitness
Resistance training using bodyweight or free weights at a gym will help you improve your strength and tone your muscles and is also great for your mind.
|
|
null | false
| 256
|
Image manipulation aims to modify some aspects of given images, from low-level colour or texture BIBREF0, BIBREF1 to high level semantics BIBREF2, to meet a user's preferences, which has numerous potential applications in video games, image editing, and computer-aided design. Recently, with the development of deep learning and deep generative models, automatic image manipulation has made remarkable progress, including image inpainting BIBREF3, BIBREF4, image colourisation BIBREF1, style transfer BIBREF0, BIBREF5, and domain or attribute translation BIBREF6, BIBREF7.
All the above works mainly focus on specific problems, and few studies BIBREF8, BIBREF9 concentrate on more general and user-friendly image manipulation by using natural language descriptions. More precisely, the task aims to semantically edit parts of an image according to the given text provided by a user, while preserving other contents that are not described in the text. However, current state-of-the-art text-guided image manipulation methods are only able to produce low-quality images (see Fig. FIGREF1: first row), far from satisfactory, and even fail to effectively manipulate complex scenes (see Fig. FIGREF1: second row).
To achieve effective image manipulation guided by text descriptions, the key is to exploit both text and image cross-modality information, generating new attributes matching the given text and also preserving text-irrelevant contents of the original image. To fuse text and image information, existing methods BIBREF8, BIBREF9 typically choose to directly concatenate image and global sentence features along the channel direction. Albeit simple, the above heuristic may suffer from some potential issues. Firstly, the model cannot precisely correlate fine-grained words with corresponding visual attributes that need to be modified, leading to inaccurate and coarse modification. For instance, shown in the first row of Fig. FIGREF1, both models cannot generate detailed visual attributes like black eye rings and a black bill. Secondly, the model cannot effectively identify text-irrelevant contents and thus fails to reconstruct them, resulting in undesirable modification of text-irrelevant parts in the image. For example, in Fig. FIGREF1, besides modifying the required attributes, both models BIBREF8, BIBREF9 also change the texture of the bird (first row) and the structure of the scene (second row).
To address the above issues, we propose a novel generative adversarial network for text-guided image manipulation (ManiGAN), which can generate high-quality new attributes matching the given text, and at the same time effectively reconstruct text-irrelevant contents of the original image. The key is a text-image affine combination module (ACM) where text and image features collaborate to select text-relevant regions that need to be modified, and then correlate those regions with corresponding semantic words for generating new visual attributes semantically aligned with the given text description. Meanwhile, it also encodes original image representations for reconstructing text-irrelevant contents. Besides, to further enhance the results, we introduce a detail correction module (DCM) which can rectify mismatched attributes and complete missing contents. Our final model can produce high-quality manipulation results with fine-grained details (see Fig. FIGREF1: Ours).
Finally, we suggest a new metric to assess image manipulation results. The metric can appropriately reflect the performance of image manipulation, in terms of both the generation of new visual attributes corresponding to the given text, and the reconstruction of text-irrelevant contents of the original image. Extensive experiments on the CUB BIBREF10 and COCO BIBREF11 datasets demonstrate the superiority of our model, where our model outperforms existing state-of-the-art methods both qualitatively and quantitatively.
To address the above issues, we propose a novel generative adversarial network for text-guided image manipulation (ManiGAN), which can generate high-quality new attributes matching the given text, and at the same time effectively reconstruct text-irrelevant contents of the original image.
|
What are the functions of the novel generative adversarial network?
|
It can generate high-quality new visual attributes matching the given text, and at the same time effectively reconstruct text-irrelevant contents of the original image.
|
null | false
| null |
Portuguese maritime exploration resulted in the numerous territories and maritime routes recorded by the Portuguese as a result of their intensive maritime journeys during the 15th and 16th centuries. Portuguese sailors were at the vanguard of European exploration, chronicling and mapping the coasts of Africa and Asia, then known as the East Indies, and Canada and Brazil (the West Indies), in what came to be known as the Age of Discovery.
Methodical expeditions started in 1419 along West Africa's coast under the sponsorship of prince Henry the Navigator, with Bartolomeu Dias reaching the Cape of Good Hope and entering the Indian Ocean in 1488. Ten years later, in 1498, Vasco da Gama led the first fleet around Africa to India, arriving in Calicut and starting a maritime route from Portugal to India. Portuguese explorations then proceeded to southeast Asia, where they reached Japan in 1542, forty-four years after their first arrival in India. In 1500, the Portuguese nobleman Pedro Álvares Cabral became the first European to discover Brazil.
|
From the passage provided, extract the what were some of the key years and discoveries of portuguese maritime exploration?
|
In 1419, the portuguese started their maritime explorations along the West Africa's coast. The goal was to reach India by sea, by going around Africa. In 1488, Bartolomeu Dias reaching the Cape of Good Hope and entered the Indian Ocean. In 1498, Vasco da Gama finally reached India.
By 1500 also reached discovered Brazil and reached Japan in 1542.
|
1708.00111
| false
| null |
Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. As INLINEFORM0 , each INLINEFORM1 vector converges to hard index pairs representing hard backpointers and successor candidates described in Algorithm SECREF1 . For finite INLINEFORM2 , we introduce a notion of a soft backpointer, represented as a vector INLINEFORM3 in the INLINEFORM4 -probability simplex, which represents the contribution of each beam element from the previous time step to a beam element at current time step. This is obtained by a row-wise sum over INLINEFORM5 to get INLINEFORM6 values representing soft backpointers.
Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation.
|
Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
|
The answers are shown as follows:
* continuous relaxation to top-k-argmax
|
null | false
| null |
Identify which of the following are episode titles from "The X-Files": The Pine Bluff Variant, My Teacher is an Alien, Memento Mori, The Day the Earth Stood Still, Millennium, Arcadia, The Matrix
|
The Pine Bluff Variant, Memento Mori, Millennium, Arcadia
|
|
null | false
| null |
Identify which car manufacturer is Japanese or American: Honda, Buick
|
Buick is American, Honda is Japanese
|
|
null | false
| null |
Which characters belong to DC or Marvel Universe? Thanos, Darkseid
|
Thanos is Marvel, Darkseid is DC
|
|
null | false
| null |
Why is WiFi such a popular way to implement a home network?
|
WiFi is a simple solution for creating a home network. It eliminates the need to physically connect each client device to the WiFi access point with a cable. Some devices such as smartphones and tablets often lack an Ethernet port which leaves WiFi or another wireless protocol as the only options for network connectivity. The convenience does come at a cost since air acts as a shared medium which limits both the number of clients a WiFi network can support as well as the bandwidth available to those clients.
|
|
null | false
| null |
The City of Drew is served by the Sunflower County Consolidated School District. Elementary school students attend A. W. James Elementary School in Drew and middle school students attend Drew Hunter Middle School in Drew. High school students attend Thomas E. Edwards, Sr. High School (formerly Ruleville Central High School) in Ruleville.
Residents were previously zoned to the Drew School District. Children attended A.W. James Elementary School and Drew Hunter High School in Drew. Prior to the 2010-2011 school year the Drew School District secondary schools were Hunter Middle School and Drew High School. As of July 1, 2012, the Drew School District was consolidated with the Sunflower County School District. Drew Hunter's high school division closed as of that date, with high school students rezoned to Ruleville Central High School.
Mississippi Delta Community College has the Drew Center in Drew. Sunflower County Library System operates the Drew Public Library in Drew.
|
List all the school districts in the following paragraph in a bullet list.
|
Sure, that paragraph references these school districts:
- Sunflower County Consolidated School District
- Drew School District
- Sunflower County School District
Note that "Sunflower County School District" may be a duplicate of "Sunflower County Consolidated School District", but it is unclear from the context of this paragraph
|
null | false
| null |
Which of the following wars took place in the twentieth century: World War II, American Revolution, French Revolution, Hundred Years War, and the Vietnam War?
|
World War II and the Vietnam war took place in the twentieth century.
|
|
null | false
| 335
|
While the demand for physical and manual labor is gradually declining, there is a growing need for a workforce with soft skills. Which soft skill do you think would be the most valuable in your daily life? According to an article in Forbes BIBREF0 , 70% of employed Americans agree that public speaking skills are critical to their success at work. Yet, it is one of the most dreaded acts. Many people rate the fear of public speaking even higher than the fear of death BIBREF1 . To alleviate the situation, several automated systems are now available that can quantify behavioral data for participants to reflect on BIBREF2 . Predicting the viewers' ratings from the speech transcripts would enable these systems to generate feedback on the potential audience behavior.
Predicting human behavior, however, is challenging due to its huge variability and the way the variables interact with each other. Running Randomized Control Trials (RCT) to decouple each variable is not always feasible and also expensive. It is possible to collect a large amount of observational data due to the advent of content sharing platforms such as YouTube, Massive Open Online Courses (MOOC), or ted.com. However, the uncontrolled variables in the observational dataset always keep a possibility of incorporating the effects of the “data bias” into the prediction model. Recently, the problems of using biased datasets are becoming apparent. BIBREF3 showed that the error rates in the commercial face-detectors for the dark-skinned females are 43 times higher than the light-skinned males due to the bias in the training dataset. The unfortunate incident of Google's photo app tagging African-American people as “Gorilla” BIBREF4 also highlights the severity of this issue.
We address the data bias issue as much as possible by carefully analyzing the relationships of different variables in the data generating process. We use a Causal Diagram BIBREF5 , BIBREF6 to analyze and remove the effects of the data bias (e.g., the speakers' reputations, popularity gained by publicity, etc.) in our prediction model. In order to make the prediction model less biased to the speakers' race and gender, we confine our analysis to the transcripts only. Besides, we normalize the ratings to remove the effects of the unwanted variables such as the speakers' reputations, publicity, contemporary hot topics, etc.
For our analysis, we curate an observational dataset of public speech transcripts and other meta-data collected from the ted.com website. This website contains a large collection of high-quality public speeches that are freely available to watch, share, rate, and comment on. Every day, numerous people watch and annotate their perceptions about the talks. Our dataset contains 2231 public speech transcripts and over 5 million ratings from the spontaneous viewers of the talks. The viewers annotate each talk by 14 different labels—Beautiful, Confusing, Courageous, Fascinating, Funny, Informative, Ingenious, Inspiring, Jaw-Dropping, Long-winded, Obnoxious, OK, Persuasive, and Unconvincing.
We use two neural network architectures in the prediction task. In the first architecture, we use LSTM BIBREF7 for a sequential input of the words within the sentences of the transcripts. In the second architecture, we use TreeLSTM BIBREF8 to represent the input sentences in the form of a dependency tree. Our experiments show that the dependency tree-based model can predict the TED talk ratings with slightly higher performance (average F-score 0.77) than the word sequence model (average F-score 0.76). To the best of our knowledge, this is the best performance in the literature on predicting the TED talk ratings. We compare the performances of these two models with a baseline of classical machine learning techniques using hand-engineered features. We find that the neural networks largely outperform the classical methods. We believe this gain in performance is achieved by the networks' ability to capture better the natural relationship of the words (as compared to the hand engineered feature selection approach in the baseline methods) and the correlations among different rating labels.
Our dataset contains 2231 public speech transcripts and over 5 million ratings from the spontaneous viewers of the talks.
|
What does the authors' dataset contain?
|
It contains 2231 public speech transcripts and over 5 million ratings from the spontaneous viewers of the talks.
|
null | false
| null |
Why is working out important for humans?
|
There are a multitude of reasons why people from all across the global and different background prioritize working out. To start, the obvious is because it helps grow and maintain muscle mass. As a result of having more muscle, humans typically feel better and look better about themselves. Another reason is because working out and exercising help you burn fat, and lose weight, therefore increasing your overall health. Some people also look to workout as a way to make friends and be social, group workout classes are a great way to make friends and meet people. Mentally working out also increases certain brain chemicals that help fight depression and anxiety.
|
|
null | false
| null |
Describe the following poem:
Two roads diverged in a yellow wood,
And sorry I could not travel both
And be one traveler, long I stood
And looked down one as far as I could
To where it bent in the undergrowth;
Then took the other, as just as fair,
And having perhaps the better claim
Because it was grassy and wanted wear,
Though as for that the passing there
Had worn them really about the same,
And both that morning equally lay
In leaves no step had trodden black.
Oh, I kept the first for another day!
Yet knowing how way leads on to way
I doubted if I should ever come back.
I shall be telling this with a sigh
Somewhere ages and ages hence:
Two roads diverged in a wood, and I,
I took the one less traveled by,
And that has made all the difference.
|
That poem is “The Road Not Taken” by Robert Frost. It describes a traveler walking in the woods who encounters two paths, one worn down from use and one that appears less travelled. The poem can be interpreted as an analogy for making decisions in life, where one choice is following a crowd and one is more individualistic. The author notes that this is a challenging choice but well worth it to be your own person by following the road not taken.
|
|
null | false
| null |
What is the standard tuning for a 6 string guitar?
|
From the highest tone to the lowest, the tuning of the 6 strings is E B G D A E.
|
|
null | false
| null |
Which of the following are movies with the actor Deepika Padukone?
|
Pathan, Citadel, Om Shanti Om, Love Aaj Kal, Milli, Bala
|
|
null | false
| null |
Give me a list of golf clubs you should carry in your golf bag.
|
Here is a list of golf clubs you should have in your bag. - Driver
- 3 wood
- Hybrid
- 4-9 iron
- pitching wedge
- sand wedge
- lob wedge
- putter
|
|
null | false
| null |
The Henry Holst String Quartet was founded by the Danish violinist Henry Holst in 1931. Holst had studied at the Royal Danish Conservatory under Axel Gade and Carl Nielsen. In 1923, he was appointed leader of the Berlin Philharmonic Orchestra, but in 1931 he moved to Manchester as a Professor at the Royal Manchester College of Music.
The move to Manchester led to the formation of the Henry Holst String Quartet with Charles Taylor (second violin), Herbert Downes (viola) and Anthony Pini (cello). The ensemble was recognised as fine quartet, touring extensively in the UK and broadcasting on BBC Radio. They played a number of times in Liverpool for the Rodewald Concert Society in the 1934 and 1937 seasons.
Ernest Element (2nd violin) and Frank Venton (viola) also sometimes played for the quartet. Herbert Downes left to lead his own quartet in 1935. Charles Taylor also left to found his own quartet and was replaced by Reginald Stead as second violin. Reginald Stead later became the leader of the BBC Northern Orchestra. Anthony Pini joined the London Philharmonic Orchestra in 1932 and was replaced by John C Hock as cellist. The Henry Holst Quartet finally disbanded when Henry Holst formed the Philharmonia Quartet in 1941 at the instigation of Walter Legge to record for Columbia Records.
|
How long did the Henry Holst String Quartet last with its original group of members based on this text? Explain your work.
|
It lasted for one year before an original member left. The first member to leave was Anthony Pini in 1932. The band was founded when Holst moved to Manchester in 1931. There is one year between these dates.
|
null | false
| null |
What is the list of football clubs in England by competitive honours won?
|
This article lists English association football clubs whose men's sides have won competitive honours run by official governing bodies. Friendly competitions and matches organized between clubs are not included. The football associations FIFA and UEFA run international and European competitions; while The Football Association, and its mostly self-governing subsidiary bodies the English Football League and Premier League, run national competitions.
The European governing body UEFA was founded in 1954, and created their first and most prestigious competition, the European Cup, the next year. It was expanded and renamed in 1992 as the UEFA Champions League. Liverpool hold the English record, with six wins. Parallel to UEFA, various officials created the Inter-Cities Fairs Cup in 1955, but this competition was disbanded when UEFA created the replacement tournament, UEFA Cup, in 1971 (renamed the UEFA Europa League in 2009) The English record number of Europa League wins is three, also held by Liverpool. Another competition absorbed into the UEFA Cup, in 1999, was the UEFA Cup Winners' Cup, which was created in 1960 and featured the winners of national knockout competitions The winners of this competition played the European Cup winners in the UEFA Super Cup, starting in 1972 (recognised by UEFA in 1973), which now features the winners of the Champions League and Europa League.Liverpool also hold the English record, with four wins, in the UEFA Super Cup. The International Football Cup, also known as the UEFA Intertoto Cup, was a competition for clubs not participating in the European Cup, UEFA Cup or Cup Winners' Cup. The tournament commenced in 1961, but UEFA officially recognised it only in 1995, and discontinued in 2008, with the Europa League expanded to accommodate Intertoto Cup clubs. UEFA and CONMEBOL also created an intercontinental competition in 1960, the Intercontinental Cup, featuring continental champions from both associations. In 2000, the international governing body FIFA created the FIFA Club World Cup and in 2004 the Intercontinental Cup was merged into it. Manchester United are the only English club to have won the Intercontinental Cup, while United, Chelsea and Liverpool are the only English teams to have lifted the Club World Cup.
England's first competition organised by a national body, the FA Cup, began in the 1871–72 season, making it one of the oldest football competitions in the world.Arsenal hold the record number of wins, with 14. League football began in the next decade with the founding of The Football League in 1888–89. The name First Division was adopted in 1892, when The Football League gained a second division. The First Division remained the highest division of the English league system until 1992, when the Premier League was founded. Manchester United have won the most top division titles, 20. The English equivalent of the super cup began in 1898 with the inauguration of the Sheriff of London Charity Shield, pitting the best professional and amateur sides of the year against each other. The trophy would develop into the FA Charity Shield in 1908, which was later renamed the FA Community Shield in 2002. Manchester United also hold the record here, with 21 wins. The Football League created its own knockout competition in 1960, the League Cup. Its current record is nine wins, held by Liverpool. The Anglo-Italian League Cup was created in 1969 to match English cup winners against the winners of the Coppa Italia, and was permanently disbanded in 1976. In 1985, the Full Members' Cup and Football League Super Cup were created as substitutes for UEFA competitions after UEFA responded to the Heysel Stadium disaster by banning English clubs. They finished in 1992 and 1986, respectively. The Football League Centenary Trophy marked The Football League's 100th birthday, in the 1988–89 season
Lower down in the hierarchy of English football are many other competitions, not included in the tables on this page. These include competitions run by the above national governing bodies, but organised for clubs ineligible for higher competitions. For example, the Texaco Cup and EFL Trophy. Regional competitions are organised by County Football Associations. In the years when league football was unavailable or only available to northern and midlands clubs, the county competitions coexisted with the FA Cup as the main tournaments for clubs. Nowadays, county cups are contested by lower or regional division teams and those that still participate generally field youth or reserve sides.
|
|
null | false
| null |
This article is about 21st century genre and overarching term for West African pop music. For the 1960s/1970s genre made by artists such as Fela Kuti and Tony Allen, see Afrobeat. For the British genre popularised by J Hus, see Afroswing.
|
What is the history of Afrobeats?
|
Afrobeats (not to be confused with Afrobeat or Afroswing), or Afro-pop or Afro-fusion (or Afropop or Afrofusion), is an umbrella term to describe popular music from West Africa and the diaspora that initially developed in Nigeria, Ghana, and the UK in the 2000s and 2010s. Afrobeats is less of a style per se, and more of a descriptor for the fusion of sounds flowing out of Ghana and Nigeria. Genres such as hiplife, jùjú music, highlife and naija beats, among others, were amalgamated under the 'Afrobeats' umbrella.
|
1601.00901
| false
| null |
There are almost 1.1 million sentences in the collection. The average length of a sentence is 18.3 words, while the median length is 13.8 words. There are 2.3 links per sentence.
There are 119 different relation types (unique predicates), having from just a few relations to a few million relations. Since DBpedia and Freebase are available in RDF format, we used the RDF store for querying and for storage of existing and new relations.
There are almost 1.1 million sentences in the collection.
There are 119 different relation types (unique predicates), having from just a few relations to a few million relations.
|
How big is their dataset?
|
The answers are shown as follows:
* 1.1 million sentences
* 119 different relation types (unique predicates)
|
null | false
| null |
The show follows Bluey, an anthropomorphic six-year-old Blue Heeler puppy who is characterised by her abundance of energy, imagination and curiosity of the world. The young dog lives with her father, Bandit; mother, Chilli; and younger sister, Bingo, who regularly joins Bluey on adventures as the pair embark on imaginative play together. Other characters featured each represent a different dog breed. Overarching themes include the focus on family, growing up and Australian culture. The program was created and is produced in Queensland; the culture and lifestyle of Brisbane inspires the show's setting.
|
Extract from the text the main characters in the show Bluey, the relationship to the main character, and list them using bullets in format {character name}: {relationship to Bluey}
|
- Bluey: self
- Bandit: father
- Chilli: mother
- Bingo: younger sister
|
null | false
| null |
How do I build a campfire?
|
Safety should always come first when starting a campfire. Ensure you have water or another way to extinguish your fire readily available. Use a campfire ring, pit, or clear a large area around your campfire location. Also, make sure campfires are permitted in your area.
With safety out of the way, all a fire needs are heat, fuel, and oxygen. Any combustible material will start a fire, but good campfire fuel sources include tinder, kindling, and dry logs.
Use stuff like dry pine needles, dry leaves, grasses, and wood shavings for tinder. You could also use newspaper (or any paper), dryer lint, or cardboard. For kindling, use smaller pieces of wood, such as twigs and sticks no thicker than 1 inch in diameter (about the thickness of your thumb). Dry or well-seasoned logs will ensure a good burn. Stack the wood in a way that guarantees oxygen can easily reach your flame. Many prefer a teepee or log cabin stacking design. Now, use a heat source to create a flame or spark and apply this to your tinder. Once the tinder combusts, it should light the kindling, and the kindling should eventually ignite your logs. If all goes well, you should now be enjoying your campfire.
|
|
null | false
| null |
Why have modern newspapers declined in the 21st century?
|
Modern newspapers have declined due to a loss of advertising revenue in the 21st century with the rise of digital media as a serious source of competition
|
|
null | false
| null |
the famous professor Johann Bernoulli made it a special pleasure for himself to help me along in the mathematical sciences. Private lessons, however, he refused because of his busy schedule. However, he gave me a far more salutary advice, which consisted in myself getting a hold of some of the more difficult mathematical books and working through them with great diligence, and should I encounter some objections or difficulties, he offered me free access to him every Saturday afternoon, and he was gracious enough to comment on the collected difficulties, which was done with such a desired advantage that, when he resolved one of my objections, ten others at once disappeared, which certainly is the best method of making happy progress in the mathematical sciences.
|
According to the passage what is the best way of making progress with mathematics?
|
Working through the more difficult mathematical books with great diligence, collect questions and difficulties and seek help from a professor to solve them
|
null | false
| 103
|
Data: We evaluate the spell correctors from § "Robust Word Recognition" on movie reviews from the Stanford Sentiment Treebank (SST) BIBREF24 . The SST dataset consists of 8544 movie reviews, with a vocabulary of over 16K words. As a background corpus, we use the IMDB movie reviews BIBREF25 , which contain 54K movie reviews, and a vocabulary of over 78K words. The two datasets do not share any reviews in common. The spell-correction models are evaluated on their ability to correct misspellings. The test setting consists of reviews where each word (with length $\ge 4$ , barring stopwords) is attacked by one of the attack types (from swap, add, drop and keyboard attacks). In the all attack setting, we mix all attacks by randomly choosing one for each word. This most closely resembles a real world attack setting.
In addition to our word recognition models, we also compare to After The Deadline (ATD), an open-source spell corrector. We found ATD to be the best freely-available corrector. We refer the reader to BIBREF7 for comparisons of ScRNN to other anonymized commercial spell checkers.
For the ScRNN model, we use a single-layer Bi-LSTM with a hidden dimension size of 50. The input representation consists of 198 dimensions, which is thrice the number of unique characters (66) in the vocabulary. We cap the vocabulary size to 10K words, whereas we use the entire vocabulary of 78470 words when we backoff to the background model. For training these networks, we corrupt the movie reviews according to all attack types, i.e., applying one of the 4 attack types to each word, and trying to reconstruct the original words via cross entropy loss.
We calculate the word error rates (WER) of each of the models for different attacks and present our findings in Table 2 . Note that ATD incorrectly predicts $11.2$ words for every 100 words (in the `all' setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of $6.9\%$ , leading to the best performance on word recognition. This is a $32\%$ relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are $5.25\%$ words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at $8.7\%$ , since the distribution of word frequencies is different in the background corpus compared to the foreground corpus.
The SST dataset consists of 8544 movie reviews, with a vocabulary of over 16K words.
|
How many movie reviews do the SST dataset consist of?
|
8544 movie reviews.
|
null | false
| null |
Give me some ideas of kids items to stuff into Easter eggs.
|
Here are some kids items that you might consider for your next Easter egg hunt: stickers, playdoh, bouncy balls, bubbles, coins, special rocks or gems, candy, other small toys
|
|
1705.08142
| true
| null |
FLOAT SELECTED: Table 3: Accuracy scores on in-domain and out-of-domain test sets for chunking (main task) with POS tagging as auxiliary task for different target domains for baselines and Sluice networks. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Average error reduction over single-task performance is 12.8% for in-domain; 8.9% for out-of-domain. In-domain error reduction over hard parameter sharing is 14.8%.
FLOAT SELECTED: Table 3: Accuracy scores on in-domain and out-of-domain test sets for chunking (main task) with POS tagging as auxiliary task for different target domains for baselines and Sluice networks. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Average error reduction over single-task performance is 12.8% for in-domain; 8.9% for out-of-domain. In-domain error reduction over hard parameter sharing is 14.8%.
|
Do sluice networks outperform non-transfer learning approaches?
|
Yes.
|
null | false
| null |
Avatar: The Last Airbender (abbreviated as ATLA), also known as Avatar: The Legend of Aang in some regions or simply Avatar, is an American animated fantasy action television series created by Michael Dante DiMartino and Bryan Konietzko and produced by Nickelodeon Animation Studio.
Avatar is set in an Asiatic-inspired world in which some people can telekinetically manipulate one of the four elements—water, earth, fire or air—through practices known as "bending", inspired by Chinese martial arts. The only individual who can bend all four elements, the "Avatar", is responsible for maintaining harmony among the world's four nations, and serves as the link between the physical world and the spirit world. The series is centered around the journey of twelve-year-old Aang, the current Avatar and last survivor of his nation, the Air Nomads, along with his friends Katara, Sokka, and Toph, as they strive to end the Fire Nation's war against the other nations and defeat Fire Lord Ozai before he conquers the world. It also follows the story of Zuko—the exiled prince of the Fire Nation, seeking to restore his lost honor by capturing Aang, accompanied by his uncle Iroh—and later, his sister Azula. Avatar is presented in a style that combines anime with American cartoons and relies on the imagery of primarily Chinese culture, with some other East Asian, Southeast Asian, South Asian, New World, Siberian, and Arctic influences.
|
Who is the Avatar? And what was he trying to accomplish?
|
In the show Avatar: The Last Airbender, Aang was the current Avatar and the last survivor of his nation, the Air Nomads. He and his friends Katara, Sokka, and Toph, strived to end the Fire Nation's war against the other nations and defeat Fire Lord Ozai before he conquered the world.
|
null | false
| null |
Why should you workout in the morning if you work remotely ?
|
Working out in the morning have several benefits:
1. It gives you a purpose to wake up early in the morning.
2. It fills you with sense of accomplishment early in the day.
3. It releases dopamine and make your mood better for the whole day.
4. Helps you follow routines such as intermittent fasting and you feel energetic until afternoon till you have your fist meal.
5. You can take out 1-2 hours of time for yourself before sending time with anything else during the day.
6. There is more fat burning if you work out in the morning as body is depleted for resources and has to tap in fat reserves to provide energy for workout.
|
|
null | false
| null |
Where does Nick work on the show the New Girl
|
at the Bar
|
|
null | false
| null |
which country surrounds India?
|
India shares borders with many countries like China, Afghanistan, and Pakistan. Bangladesh, Bhutan, Nepal, and Burma. India is also surrounded by the Bay of Bengal, the Atlantic Ocean, and the Indian Ocean.
|
|
null | false
| null |
What is sourdough bread?
|
Sourdough bread does not use yeast to rise. Instead it uses a starter, which is fermented dough, as its rising agent. Hence it is classified as a naturally leavened bread. It normally takes longer for a sourdough bread to rise. Sourdough bread is more flavourful than normal bread.
The starter is created using flour, salt and water. It has to be "fed" and maintained with fresh flour for the starter to remain alive. Staters can be stored in a cooler environment in order to slow down the fermentation prices.
|
|
null | false
| null |
Write an argument for keeping public lands under federal control in the Intermountain West.
|
Public lands make up a huge proportion of the region between the crest of the Sierra Nevada and the Continental Divide in the Rockies, also known as the Intermountain West, and most of these lands are owned by the Federal government. Many residents of this sparsely populated region resent the control of these lands by distant bureaucrats whose regulations place significant restrictions on how the land is used. For this reason, movements have been ongoing for many years to work for the transfer of those lands to local authorities. While this sense of disenfranchisement is understandable, the truth is that local residents would suffer more from the loss of centralized management of these lands than they would gain through local control. This region is mostly desert, a landscape and ecosystem that is unusually vulnerable to degradation. Without the concerted efforts of an administration that can apply consistent regulation and stewardship of these lands across a broad territory that crosses state boundaries, the value of these lands to local residents would decline even further, becoming less productive and less attractive to the hunters, fishermen, and other tourists whose spending represents the majority of income for many of these Intermountain communities.
|
|
2003.12218
| true
| null |
FLOAT SELECTED: Table 2: Examples of the most frequent entities annotated in CORD-NER.
FLOAT SELECTED: Table 2: Examples of the most frequent entities annotated in CORD-NER.
|
Do they list all the named entity types present?
|
No.
|
null | false
| null |
Most major religious denominations are present in Singapore, with the Inter-Religious Organisation, Singapore (IRO) recognising 10 major religions in the city state. A 2014 analysis by the Pew Research Center found Singapore to be the world's most religiously diverse nation.
Religion in Singapore, 2020
Religion Percent
Buddhism
31.1%
No religion
20.0%
Christianity
18.9%
Islam
15.6%
Taoism and folk religion
8.8%
Hinduism
5.0%
Other religions
0.6%
Buddhism is the most widely practised religion in Singapore: 31% of the resident population declared themselves adherents at the most recent census. The next-most practised religion is Christianity, followed by Islam, Taoism, and Hinduism. 20% of the population did not have a religious affiliation. The proportion of Christians, Taoists, and non-religious people increased between 2000 and 2010 by about 3 percentage points each, while the proportion of Buddhists decreased. Other faiths remained largely stable in their share of the population.
There are monasteries and Dharma centres from all three major traditions of Buddhism in Singapore: Theravada, Mahayana, and Vajrayana. Most Buddhists in Singapore are Chinese and are of the Mahayana tradition, missionaries having come into the country from China for several decades. However, Thailand's Theravada Buddhism has seen growing popularity among the populace (not only the Chinese) during the past decade. The religion of Soka Gakkai International, a Japanese Buddhist organisation, is practised by many people in Singapore, and mostly by those of Chinese descent. Tibetan Buddhism has also made slow inroads into the country in recent years.
|
What are the top 2 most practiced religions in Singapore?
|
The most widely practiced religions in Singapore are: Buddhism and Christianity at 31% and 20% respectively. However, 20% of the population did not have a religious affiliation.
|
null | false
| null |
The meaning of life, or the answer to the question: "What is the meaning of life?", pertains to the significance of living or existence in general. Many other related questions include: "Why are we here?", "What is life all about?", or "What is the purpose of existence?" There have been many proposed answers to these questions from many different cultural and ideological backgrounds. The search for life's meaning has produced much philosophical, scientific, theological, and metaphysical speculation throughout history. Different people and cultures believe different things for the answer to this question.
The meaning of life can be derived from philosophical and religious contemplation of, and scientific inquiries about existence, social ties, consciousness, and happiness. Many other issues are also involved, such as symbolic meaning, ontology, value, purpose, ethics, good and evil, free will, the existence of one or multiple gods, conceptions of God, the soul, and the afterlife. Scientific contributions focus primarily on describing related empirical facts about the universe, exploring the context and parameters concerning the "how" of life. Science also studies and can provide recommendations for the pursuit of well-being and a related conception of morality. An alternative, humanistic approach poses the question, "What is the meaning of my life?"
|
What is the meaning of life?
|
pertains to the significance of living or existence in general
|
null | false
| null |
which weighs more, cold or hot water?
|
Cold water weighs more than hot water
|
|
2002.11402
| false
| null |
We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily. To remove such titles, we deployed simple rules as follows -
After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles. Then selected minimum articles required to cover all possible 2-grams to 5-grams. This step is done to save some training time without loosing accuracy. Do note that, in future we are planning to use whole dataset and hope to see gains in F1 and Recall further. But as per manual inspection, our dataset contains enough variations of sentences with rich vocabulary which contains names of celebrities, politicians, local authorities, national/local organisations and almost all locations, India and International, mentioned in the news text, in last 4 years.
We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily.
After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles.
|
How large is the dataset they used?
|
The answers are shown as follows:
* English wikipedia dataset has more than 18 million
* a dump of 15 million English news articles
|
2001.05970
| false
| null |
Observing the results of the linear regression in Table 2, we find the normalized governmental reported cases count and regional feature to be statistically significant on the sexual harassment rate in the Twitter data ($p-value<0.05$). Specifically, the change in the number of reported cases constitutes a considerable change in the number of #MeToo users on Twitter as p-value is extremely small at $5.7e-13$. This corresponds to the research by Napolitano (2014) regarding the "Yes means yes" movement in higher education institutes in recent years, as even with some limitations and inconsistency, the sexual assault reporting system is gradually becoming more rigorous BIBREF17. Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region. This finding is interesting and warrants further scrutiny.
Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region.
|
Which geographical regions correlate to the trend?
|
Northeast U.S., West U.S. and South U.S.
|
null | false
| null |
When looking to purchase a bicycle, you need to understand a number of factors. These include the primary use of the bicycle being either for transportation or pleasure. When choosing a bicycle for transportation you want to factor in cost, weight and durability. When choosing a bicycle for pleasure, comfort is the main factor.
|
As part of the buying process, getting expert opinions will be helpful. You want to discuss your purchase with people who have made a similar purchase so that you can make an educated decision.
|
|
null | false
| 53
|
Medical search engines are an essential component for many online medical applications, such as online diagnosis systems and medical document databases. A typical online diagnosis system, for instance, relies on a medical search engine. The search engine takes as input a user query that describes some symptoms and then outputs clinical concept entries that provide relevant information to assist in diagnosing the problem. One challenge medical search engines face is the segmentation of individual clinical entities. When a user query consists of multiple clinical entities, a search engine would often fail to recognize them as separate entities. For example, the user query “fever joint pain weight loss headache” contains four separate clinical entities: “fever”, “joint pain”, “weight loss”, and “headache”. But when the search engine does not recognize them as separate entities and proceeds to retrieve results for each word in the query, it may find "pain" in body locations other than "joint pain", or it may miss "headache" altogether, for example. Some search engines allow the users to enter a single clinical concept by selecting from an auto-completion pick list. But this could also result in retrieving inaccurate or partial results and lead to poor user experience.
We want to improve the medical search engine so that it can accurately retrieve all the relevant clinical concepts mentioned in a user query, where relevant clinical concepts are defined with respect to the terminologies the search engine uses. The problem of extracting clinical concept mentions from a user query can be seen as a variant of the Concept Extraction (CE) task in the frequently-cited NLP challenges in healthcare, such as 2010 i2b2/VA BIBREF0 and 2013 ShARe/CLEF Task 1 BIBREF1. Both CE tasks in 2010 i2b2/VA and 2013 ShARe/CLEF Task 1 ask the participants to design an algorithm to tag a set of predefined entities of interest in clinical notes. These entity tagging tasks are also known as clinical Named Entity Recognition (NER). For example, the CE task in 2010 i2b2/VA defines three types of entities: “problem”, “treatment”, and “test”. The CE task in 2013 ShARe/CLEF defines various types of disorder such as “injury or poisoning”, "disease or syndrome”, etc. In addition to tagging, the CE task in 2013 ShARe/CLEF has an encoding component which requires selecting one and only one Concept Unique Identifier (CUI) from Systematized Nomenclature Of Medicine Clinical Terms (SNOMED-CT) for each disorder entity tagged. Our problem, similar to the CE task in 2013 ShARe/CLEF, also contains two sub-problems: tagging mentions of entities of interest (entity tagging), and selecting appropriate terms from a glossary to match the mentions (term matching). However, several major differences exist. First, compared to clinical notes, the user queries are much shorter, less technical, and often less coherent. Second, instead of encoding, we are dealing with term matching where we rank a few best terms that match an entity, instead of selecting only one. This is because the users who type the queries may not have a clear idea about what they are looking for, or could be laymen who know little terminology, it may be more helpful to provide a set of likely results and let the users choose. Third, the types of entities are different. Each medical search engine may have its own types of entities to tag. There is also one minor difference in the tagging scheme between our problem and the CE task in 2013 ShARe/CLEF - We limit our scope to dealing with entities of consecutive words and not disjoint entities . We use only Beginning, Inside, Outside (BIO) tags. Given the differences listed above, we need to customize a framework consisting of an entity tagging and term matching component for our CE problem.
One challenge medical search engines face is the segmentation of individual clinical entities. When a user query consists of multiple clinical entities, a search engine would often fail to recognize them as separate entities.
|
What are the challenges facing medical search engines?
|
One challenge medical search engines face is the segmentation of individual clinical entities. When a user query consists of multiple clinical entities, a search engine would often fail to recognize them as separate entities.
|
null | false
| 417
|
The explanations of our model are divided into basic explanation and explanations of the basic explanation. For fundamental explanations, visualized images of the learned prototypes and the distance between encoded input and prototype distributions are given as previous prototype-based explanation methods. To explain this basic explanation, we additionally provide two gradual explanations. These explanations can be obtained because our method constructs dense explanation space, unlike previous methods. As a result, it is possible to generate interconnection between latent representations as images. We represent changing elements by interpolating these latent representations and define them as relationships.
The relationships between inputs and prototypes can be visualized by our model. In situations where distance is given for interpretable information, the factors that can reduce the distance are critical for understanding the model's prediction. Additionally, relationships between prototypes are given. Since the prototypes are representative of the whole dataset, relationships between prototypes show which features are needed to change from one class to another class. This explanation can be used as basic information about how the model discriminates classes.
After the model is trained, basic explanation and explanations for basic explanation are visualized as follows. First, the prototype, provided as the fundamental explanation, is visualized by sending µ i of the prototype distribution through the decoder g as a prototype. Next, the relationships between prototype µ i and prototype µ j are visualized as sending µ i + (µ j − µ i )r/k for r = 0, 1, . . . , k through the decoder g, which can be controlled by increasing the value of k for detailed explanation and decreasing the value of k for a brief explanation. Finally, the relationships between inputs and prototypes are provided in the same way, and visualizations of prototype distributions are based on µ values and σ values.
The disadvantage of our model comes from this visualization. Many training samples are required to generate explanation images. In addition, visualized images of our explanations are fundamentally generated through VAE, making it challenging to generate human-recognizable images for complex datasets that are difficult for VAE to generate the observable image. However, this is a problem related to VAE's expressive power, and there are many high-resolution expressive methods based on VAE, so applying these methods would solve these problems in future work.
The disadvantage of our model comes from this visualization. Many training samples are required to generate explanation images. In addition, visualized images of our explanations are fundamentally generated through VAE, making it challenging to generate human-recognizable images for complex datasets that are difficult for VAE to generate the observable image.
|
Why generalizing to larger images and complex datasets is out of the scope for validating the scalability of the proposed network structure when there are no particular blockers from training such networks?
|
We also agree that the scalability of the proposed network structure is necessary. However, please note that the scalability issue can be dealt with as in Answer #1. Since our paper showed the advantages of using the latent space of a VAE structure in prototype-based explanation methods, we also believe that ImageNet, a larger image dataset such as the one you suggested, is a natural next step.
Since the scalability issue is important, we will mention the same comment as Answer #1 in the manuscript. We will add this to the last paragraph of section 4.3, which deals with the limitations of our paper.
|
null | false
| 13
|
The obvious difficulty when developing a dialog system is finding a way how to identify the piece of information that the user is interested in. This is especially a problem for dialog systems based on knowledge graphs containing a large amount of complex structured information. While a similar problem is being solved in a task of question answering, dialog systems have more possibilities of identifying the real intention of the user. For example, a dialog system can ask for additional information during the dialog.
We distinguish three different basic approaches to requesting knowledge bases:
A combination of the above approaches is also possible. For example, we can imagine scenarios where the dialog system starts with hand-crafted rules, which are subsequently interactively improved through dialogs with its users. With a growing demand for open domain dialog systems, it shows that creating hand-crafted policies does not scale well - therefore, machine learning approaches are gaining on popularity. Many public datasets for offline learning have been published BIBREF8 , BIBREF7 . However, to our knowledge, no public datasets for interactive learning are available. To fill this gap, we collected a dataset which enables to train interactively learned policies through a simulated interaction with users.
The obvious difficulty when developing a dialog system is finding a way how to identify the piece of information that the user is interested in.
|
What are the difficulties in developing a dialogue system?
|
The obvious difficulty when developing a dialog system is finding a way how to identify the piece of information that the user is interested in.
|
null | false
| null |
What are the only three ingredients to make authentic Chantilly cream?
|
Cream (containing at least 30% fat), icing sugar, and vanilla
|
|
null | false
| null |
Iron Man premiered in Sydney on April 14, 2008, and was released in the United States on May 2, being the first film in Phase One of the MCU. It grossed over $585 million, becoming the eighth-highest grossing film of 2008. The film received praise from critics, especially for Downey's performance, as well as Favreau's direction, visual effects, action sequences, and writing. It was selected by the American Film Institute as one of the ten best films of 2008, received two nominations at the 81st Academy Awards for Best Sound Editing and Best Visual Effects. In 2022, the film was selected for preservation in the United States National Film Registry by the Library of Congress as being "culturally, historically, or aesthetically significant". Two sequels have been released: Iron Man 2 (2010) and Iron Man 3 (2013).
|
From the passage provided, extract the gross earnings of the movie Iron Man
|
over $585 million
|
null | false
| null |
For Bitter or Worse is the sixth studio album from the Dutch singer Anouk. The album was released on 18 September 2009, via the record label EMI.
The first single from the album, "Three Days in a Row" was released in August. It reached the top of the Netherlands charts in September 2009, making it Anouk's first number one in the country. In June of the same year, one of the songs recorded for the album, "Today", was released as promo material. It was so successful that, despite never being released as an official single, the song reached number 50 in the Dutch chart. The second single Woman, was sent to radio stations at the end of October 2009. After just one day the single was at number one on airplay chart. The single was released physically on 24 November 2009.
|
Using examples from the text, list some popular songs from the album For Bitter or Worse.
|
Popular songs from the album For Bitter or Worse include "Three Days in a Row" and "Today".
|
null | false
| 115
|
The aim of the technical validation of the data is to guarantee good recording quality and to replicate findings of previous studies investigating co-registration of EEG and eye movement data during natural reading tasks (e.g. dimigen2011coregistration). We also compare the results to ZuCo 1.0 BIBREF1, which allows a more direct comparison due to the analogous recording procedure.
Moreover, we corroborated these sentence-level metrics by visualizing the skipping proportion on word level (Figure 4). The skipping proportion is the average rate of words being skipped (i.e. not being fixated) in a sentence. As expected, this also increases in the task-specific reading.
|
What's the meaning of skipping proportion in corroborating sentence-level metrics in Eye-tracking validation?
|
It is the average rate of words being skipped (i.e. not being fixated) in a sentence.
|
null | false
| null |
Charles Stuart (22 October 1660 – 5 May 1661) was the first of four sons and eight children born from the marriage between the Duke of York (later King James II of England & VII of Scotland) and his first wife, Anne Hyde. He was styled Duke of Cambridge, but never formally created so, because he died so young.
Charles was conceived seven months before his parents' official marriage and if royal advisors and Queen Henrietta Maria, the mother of James, had their way, he could have been declared illegitimate. King Charles II, James's older brother, approved of the marriage and the wedding between James and Anne was held on 3 September 1660 in London. Charles was born on 22 October and was baptised on 1 January 1661 at Worcester House. However, he died before reaching the age of one, after becoming ill with smallpox. He was buried in Westminster Abbey on 6 May 1661. Three of his younger brothers, likewise short-lived, were also called Duke of Cambridge: James, Edgar, and Charles.
Arms
See adjacent text
Coat of arms as Duke of Cambridge
During his short life, Charles bore a coat of arms, as a grandson of a British sovereign, consisting those of the kingdom, differenced by a label argent of five points ermine.
|
Who was Charles Stuart, Duke of Cambridge
|
Charles Stuart (22 October 1660 – 5 May 1661) was the first of four sons and eight children born from the marriage between the Duke of York (later King James II of England & VII of Scotland) and his first wife, Anne Hyde. He was styled Duke of Cambridge, but never formally created so, because he died so young.
Charles was conceived seven months before his parents' official marriage and if royal advisors and Queen Henrietta Maria, the mother of James, had their way, he could have been declared illegitimate. King Charles II, James's older brother, approved of the marriage and the wedding between James and Anne was held on 3 September 1660 in London. Charles was born on 22 October and was baptised on 1 January 1661 at Worcester House. However, he died before reaching the age of one, after becoming ill with smallpox. He was buried in Westminster Abbey on 6 May 1661. Three of his younger brothers, likewise short-lived, were also called Duke of Cambridge: James, Edgar, and Charles.
During his short life, Charles bore a coat of arms, as a grandson of a British sovereign, consisting those of the kingdom, differenced by a label argent of five points ermine.
|
null | false
| null |
What literary technique is used in the phrase “simmered on a smooth summer sidewalk”?
|
“Simmered on a smooth summer sidewalk” is an example of alliteration, which is a literary technique often used to emphasize a phrase or make a phrase stand out more to a reader.
|
|
null | false
| null |
Why can camels survive for long without water?
|
Camels use the fat in their humps to keep them filled with energy and hydration for long periods of time.
|
|
null | false
| null |
Lethata dispersa is a moth of the family Depressariidae. It is found in Brazil (Matto Grosso)
The wingspan is about 23 mm. The forewings are yellow with the costa rosy and the dorsum narrowly edged brown and with a faint spot in the fold brown. There is a spot at the end of the cell consisting of a ring of brown enclosing a whitish spot. There is an oblique brown line extending from the costa at near the midpoint through a spot at the end of the cell to the tornus. The terminal line is brown. The hindwings are grey.
|
What color is the Lethata dispersa?
|
Lethata dispersa is a moth with a variety of different colors. It has yellow forewings, grey hindwings, and features brown at the dorsum, with a ring of brown enclosing a white spot.
|
null | false
| null |
Peter Jordan (born 26 April 1967) is a German actor. He appeared in more than seventy films since 1995.
|
In what year was Peter born? Use roman numerals.
|
MCMLXVII
|
null | false
| null |
Operation Aurora was a series of cyber attacks conducted by advanced persistent threats such as the Elderwood Group based in Beijing, China, with ties to the People's Liberation Army. First publicly disclosed by Google on January 12, 2010, in a blog post, the attacks began in mid-2009 and continued through December 2009.
The attack was aimed at dozens of other organizations, of which Adobe Systems, Akamai Technologies, Juniper Networks, and Rackspace have publicly confirmed that they were targeted. According to media reports, Yahoo, Symantec, Northrop Grumman, Morgan Stanley, and Dow Chemical were also among the targets.
As a result of the attack, Google stated in its blog that it plans to operate a completely uncensored version of its search engine in China "within the law, if at all," and acknowledged that if this is not possible, it may leave China and close its Chinese offices. Official Chinese sources claimed this was part of a strategy developed by the U.S. government.
The attack was named "Operation Aurora" by Dmitri Alperovitch, Vice President of Threat Research at cybersecurity company McAfee. Research by McAfee Labs discovered that "Aurora" was part of the file path on the attacker's machine that was included in two of the malware binaries McAfee said were associated with the attack. "We believe the name was the internal name the attacker(s) gave to this operation," McAfee Chief Technology Officer George Kurtz said in a blog post.
According to McAfee, the primary goal of the attack was to gain access to and potentially modify source code repositories at these high-tech, security, and defense contractor companies. "[The SCMs] were wide open," says Alperovitch. "No one ever thought about securing them, yet these were the crown jewels of most of these companies in many ways—much more valuable than any financial or personally identifiable data that they may have and spend so much time and effort protecting."
History
Flowers left outside Google China's headquarters after its announcement it might leave the country
On January 12, 2010, Google revealed on its blog that it had been the victim of a cyber attack. The company said the attack occurred in mid-December and originated from China. Google stated that over 20 other companies had been attacked; other sources have since cited that more than 34 organizations were targeted. As a result of the attack, Google said it was reviewing its business in China. On the same day, United States Secretary of State Hillary Clinton issued a brief statement condemning the attacks and requesting a response from China.
On January 13, 2010, the news agency All Headline News reported that the United States Congress plans to investigate Google's allegations that the Chinese government used the company's service to spy on human rights activists.
In Beijing, visitors left flowers outside of Google's office. However, these were later removed, with a Chinese security guard stating that this was an "illegal flower tribute". The Chinese government has yet to issue a formal response, although an anonymous official stated that China was seeking more information on Google's intentions.
Attackers involved
Further information: Cyberwarfare by China
Technical evidence including IP addresses, domain names, malware signatures, and other factors, show Elderwood was behind the Operation Aurora attack. The "Elderwood" group was named by Symantec (after a source-code variable used by the attackers), and is referred to as the "Beijing Group" by Dell Secureworks. The group obtained some of Google's source code, as well as access to information about Chinese activists. Elderwood also targeted numerous other companies in the shipping, aeronautics, arms, energy, manufacturing, engineering, electronics, financial, and software sectors.
The "APT" designation for the Chinese threat actors responsible for attacking Google is APT17.
Elderwood specializes in attacking and infiltrating second-tier defense industry suppliers that make electronic or mechanical components for top defense companies. Those firms then become a cyber "stepping stone" to gain access to top-tier defense contractors. One attack procedure used by Elderwood is to infect legitimate websites frequented by employees of the target company – a so-called "water hole" attack, just as lions stake out a watering hole for their prey. Elderwood infects these less-secure sites with malware that downloads to a computer that clicks on the site. After that, the group searches inside the network to which the infected computer is connected, finding and then downloading executives' e-mails and critical documents on company plans, decisions, acquisitions, and product designs.
Attack analysis
In its blog posting, Google stated that some of its intellectual property had been stolen. It suggested that the attackers were interested in accessing Gmail accounts of Chinese dissidents. According to the Financial Times, two accounts used by Ai Weiwei had been attacked, their contents read and copied; his bank accounts were investigated by state security agents who claimed he was under investigation for "unspecified suspected crimes". However, the attackers were only able to view details on two accounts and those details were limited to things such as the subject line and the accounts' creation date.
Security experts immediately noted the sophistication of the attack. Two days after the attack became public, McAfee reported that the attackers had exploited purported zero-day vulnerabilities (unfixed and previously unknown to the target system developers) in Internet Explorer and dubbed the attack "Operation Aurora". A week after the report by McAfee, Microsoft issued a fix for the issue, and admitted that they had known about the security hole used since September. Additional vulnerabilities were found in Perforce, the source code revision software used by Google to manage their source code.
VeriSign's iDefense Labs claimed that the attacks were perpetrated by "agents of the Chinese state or proxies thereof".
According to a diplomatic cable from the U.S. Embassy in Beijing, a Chinese source reported that the Chinese Politburo directed the intrusion into Google's computer systems. The cable suggested that the attack was part of a coordinated campaign executed by "government operatives, public security experts and Internet outlaws recruited by the Chinese government." The report suggested that it was part of an ongoing campaign in which attackers have "broken into American government computers and those of Western allies, the Dalai Lama and American businesses since 2002." According to The Guardian's reporting on the leak, the attacks were "orchestrated by a senior member of the Politburo who typed his own name into the global version of the search engine and found articles criticising him personally."
Once a victim's system was compromised, a backdoor connection that masqueraded as an SSL connection made connections to command and control servers running in Illinois, Texas, and Taiwan, including machines that were running under stolen Rackspace customer accounts. The victim's machine then began exploring the protected corporate intranet that it was a part of, searching for other vulnerable systems as well as sources of intellectual property, specifically the contents of source code repositories.
The attacks were thought to have definitively ended on Jan 4 when the command and control servers were taken down, although it is not known at this point whether or not the attackers intentionally shut them down. However, the attacks were still occurring as of February 2010.
Response and aftermath
The German, Australian, and French governments publicly issued warnings to users of Internet Explorer after the attack, advising them to use alternative browsers at least until a fix for the security hole was made. The German, Australian, and French governments considered all versions of Internet Explorer vulnerable or potentially vulnerable.
In an advisory on January 14, 2010, Microsoft said that attackers targeting Google and other U.S. companies used software that exploits a hole in Internet Explorer. The vulnerability affects Internet Explorer versions 6, 7, and 8 on Windows 7, Vista, Windows XP, Server 2003, Server 2008 R2, as well as IE 6 Service Pack 1 on Windows 2000 Service Pack 4.
The Internet Explorer exploit code used in the attack has been released into the public domain, and has been incorporated into the Metasploit Framework penetration testing tool. A copy of the exploit was uploaded to Wepawet, a service for detecting and analyzing web-based malware operated by the computer security group at the University of California, Santa Barbara. "The public release of the exploit code increases the possibility of widespread attacks using the Internet Explorer vulnerability," said George Kurtz, CTO of McAfee, of the attack. "The now public computer code may help cybercriminals craft attacks that use the vulnerability to compromise Windows systems."
Security company Websense said it identified "limited public use" of the unpatched IE vulnerability in drive-by attacks against users who strayed onto malicious Web sites. According to Websense, the attack code it spotted is the same as the exploit that went public last week.[clarification needed] "Internet Explorer users currently face a real and present danger due to the public disclosure of the vulnerability and release of attack code, increasing the possibility of widespread attacks," said George Kurtz, chief technology officer of McAfee, in a blog update. Confirming this speculation, Websense Security Labs identified additional sites using the exploit on January 19. According to reports from Ahnlab, the second URL was spread through the Instant Messenger network Misslee Messenger, a popular IM client in South Korea.
Researchers have created attack code that exploits the vulnerability in Internet Explorer 7 (IE7) and IE8—even when Microsoft's recommended defensive measure (Data Execution Prevention (DEP)) is turned on.[dubious – discuss] According to Dino Dai Zovi, a security vulnerability researcher, "even the newest IE8 isn't safe from attack if it's running on Windows XP Service Pack 2 (SP2) or earlier, or on Windows Vista RTM (release to manufacturing), the version Microsoft shipped in January 2007."
Microsoft admitted that the security hole used had been known to them since September. Work on an update was prioritized and on Thursday, January 21, 2010, Microsoft released a security patch aiming to counter this weakness, the published exploits based on it and a number of other privately reported vulnerabilities. They did not state if any of the latter had been used or published by exploiters or whether these had any particular relation to the Aurora operation, but the entire cumulative update was termed critical for most versions of Windows, including Windows 7.
Security researchers continued to investigate the attacks. HBGary, a security firm, released a report in which they claimed to have found some significant markers that might help identify the code developer. The firm also said that the code was Chinese language based but could not be specifically tied to any government entity.
On February 19, 2010, a security expert investigating the cyber-attack on Google, has claimed that the people behind the attack were also responsible for the cyber-attacks made on several Fortune 100 companies in the past one and a half years. They have also tracked the attack back to its point of origin, which seems to be two Chinese schools, Shanghai Jiao Tong University and Lanxiang Vocational School. As highlighted by The New York Times, both of these schools have ties with the Chinese search engine Baidu, a rival of Google China. Both Lanxiang Vocational and Jiaotong University have denied the allegation.
In March 2010, Symantec, which was helping investigate the attack for Google, identified Shaoxing as the source of 21.3% of all (12 billion) malicious emails sent throughout the world.
Google retrospective
On October 3, 2022, Google on YouTube released a six-episode series covering the events that occurred during Operation Aurora, with commentary from insiders who dealt with the attack, though the series primary focus was to reassure the Google-using public that measures are in place to counter hacking attempts.
|
Given this article about Operation Aurora, Which nation was beleived to be behund the attacks?
|
China's People's Liberation Army (PLA) is believed to be behind this operation.
|
null | false
| 377
|
We find that our hypothesis is validated: RTE is not suitable as such to test a precise NLI system, because, for entailment to hold as tagged in RTE, much world-knowledge is required and many missing hypotheses are omitted.
By using a crowd of experts to repair the missing hypotheses, we have constructed a dataset of 150 precise entailment problems, based on text found in real-world corpora. Even though the dataset is on the small size, it is, to the best of our knowledge, the first of this kind.
An issue with the method that we used to construct our new dataset of entailment problems is that it is difficult to scale: it demands several minutes of scarce expert work per constructed problem. Our plan is to investigate the possibility to gamify the process, so that lots of people can participate in the construction of precise entailment problems, as a form of entertainment. We leave any detail to further work, but this would be an asymmetric game, where one one player tries construct watertight entailment problems, and the opposing player would try and refute such entailment problems (say, by giving counter-examples). Identifying the possible kind of mistakes, as we have done here, will help prompting the players about things to look for — in either of the possible roles.
Finally we find it striking that many mistakes committed in RTE are falling for classical fallacies (appealing to authority accounts for more than a quarter of errors). Thus, we believe that while the entailment problems are useful for the construction of NLI systems, the added hypotheses (or moves taken in our hypothetical game) could be of interest to the linguistic community at large.
We find that our hypothesis is validated: RTE is not suitable as such to test a precise NLI system, because, for entailment to hold as tagged in RTE, much world-knowledge is required and many missing hypotheses are omitted.
|
Why is the RTE not suitable as such to test a precise NLI system?
|
Because, for entailment to hold as tagged in RTE, much world-knowledge is required and many missing hypotheses are omitted.
|
null | false
| null |
Call of Duty: Modern Warfare 2 is a 2009 first-person shooter game developed by Infinity Ward and published by Activision. It is the sixth installment in the Call of Duty series and the direct sequel to Call of Duty 4: Modern Warfare. It was released worldwide on November 10, 2009, for Microsoft Windows, the PlayStation 3, and Xbox 360. A separate version for the Nintendo DS, titled Modern Warfare: Mobilized, was also released on the same day. A version for macOS was developed by Aspyr and released in May 2014, and the Xbox 360 version was made backward compatible for the Xbox One in 2018.
The game's campaign follows Task Force 141, a multi-national special forces unit commanded by Captain Soap MacTavish as they hunt Vladimir Makarov, leader of the Russian Ultranationalist party, and U.S. Army Rangers from 1st Battalion/75th Ranger Regiment who are defending the Washington, D.C. area from a Russian invasion. The game's main playable characters are Sergeant Gary "Roach" Sanderson, of the 141, and Private James Ramirez, of the Army Rangers, with Captain MacTavish becoming playable later in the campaign. An expansive multiplayer mode is featured in the game, with several new features and modes that were not seen in its predecessor.
Development for the game began in 2008, when it was still known as Call of Duty 6. It uses the IW 4.0 engine, an improved version of Call of Duty 4's IW 3.0. Infinity Ward was inspired by real-life conflicts when developing the campaign mode. They initially tested the multiplayer mode by playing an in-house beta version of the game. Modern Warfare 2 was officially announced in February 2009. Teasing of the game began in March, with short trailers being released for the game and, eventually, a full reveal trailer. The multiplayer mode was revealed shortly after. Two downloadable content packs were released for it post-release, each containing five new multiplayer maps, with some being remastered maps from Call of Duty 4.
Modern Warfare 2 received universal acclaim, with praise for its campaign, multiplayer, and amount of content, although it received some criticism for its short length and a lack of innovation. Within 24 hours of release, the game sold approximately 4.7 million copies in North America and the United Kingdom. As of 2013, the game had sold 22.7 million copies. In addition to its release, a comic book series based on character Simon "Ghost" Riley was also produced, titled Modern Warfare 2: Ghost. Despite the game's success, it was subject to some controversies, with one surrounding a playable level that had the player carry out a terror attack on an airport. A sequel, Call of Duty: Modern Warfare 3, was released in 2011 and finishes the original Modern Warfare storyline. A remaster of the game's campaign, Call of Duty: Modern Warfare 2 Campaign Remastered, was released in March 2020 on the PlayStation 4 and on Microsoft Windows and Xbox One in April 2020.
|
Given this text, summarize the major events in Call of Duty: Modern Warfare 2 game development and release.
|
2008: Call of Duty: Modern Warfare 2 development began
Feb. 2009: officially announced
March 2009: game testing bagan
Nov. 10, 2009: released worldwide for Microsoft Windows, the PlayStation 3 and Xbox 360.
2014: released for macOS
2018: released for Xbox One
March 2020: a remastered version is released on the PlayStation 4
April 2020: a remastered version is released on Microsoft WIndows and Xbox One
|
null | false
| null |
What is data denormalization in data warehouse?
|
Denormalize the data, which means adding redundant columns to some dimension tables to make querying and working with the data faster and easier. The purpose is to trade some redundancy (duplication of data) in the data model for increased query speed, by avoiding computationally expensive join operations.
|
|
1909.02776
| false
| null |
FLOAT SELECTED: Figure 3: ROUGE Quality of produced summaries in term of precision.
FLOAT SELECTED: Figure 3: ROUGE Quality of produced summaries in term of precision.
|
By how much is precission increased?
|
ROUGE-1 increases by 0.05, ROUGE-2 by 0.06 and ROUGE-L by 0.09
|
null | false
| null |
In World Wrestling Championship 2022 Free Style Wrestling India won 2 bronze medals out of total how many medals
|
120 medals(24th rank in over all medal tally)
|
|
null | false
| null |
What's the capital city of Czechoslovakia?
|
Czechoslovakia is no longer a sovereign state. It ceased to exist following the deposition of its communist leaders in a process known as the Velvet Revolution. Following the fall of that regime it split peacefully at the end of 1992 into the Czech Republic and Slovakia. The capital cities of those two nations are Prague and Bratislava respectively. While Czechoslovakia was in existence Prague was its capital.
|
|
null | false
| null |
What is nomadism?
|
Nomadism is a way of living in which people don't have a permanent place to live. Instead, they move around a certain area in search of food and grazing land. The change from being nomadic to staying in one place is what led to the first civilizations.
|
|
null | false
| null |
Hasora discolor, the green awl, is a butterfly of the family Hesperiidae. It is found as several subspecies in Australia (where it is found along the south-eastern coast of New South Wales and the northern Gulf and north-eastern coast of Queensland), the Aru Islands, Irian Jaya, the Kei Islands, Maluku and Papua New Guinea.
|
What is Hasora discolor located?
|
Hasora discolor can be found in New South Wales, Australia
|
1911.05960
| false
| null |
The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. This can be formulated as
By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated. In this paper, we apply a same-length convolution (length of the sentence does not change), i.e. $c \in \mathbb {R}^{\mathcal {\\}n*1}$. Then we apply $d$ filters with the same window size to obtain multiple feature maps. So the final output of CNN has the shape of $C \in \mathbb {R}^{\mathcal {\\}n*d}$, which is exactly the same size as $n$ word embeddings, which enables us to do exact word-level attention in various tasks.
The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words.
By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated.
|
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?
|
The answers are shown as follows:
* word embeddings to generate a new feature, i.e., summarizing a local context
|
null | false
| 114
|
Dialogue evaluation is an open problem and existing works have found that automatic metrics have low correlation to human evaluation BIBREF35 , BIBREF36 , BIBREF37 . Thus, we resorted to manual evaluation to assess the generation quality on WeiboDial. We randomly sampled 200 posts from the test set and collected the generated results from all the models. For each pair of responses (one from ARAML and the other from a baseline, given the same input post), five annotators were hired to label which response is better (i.e. win, lose or tie) in terms of grammaticality (whether a response itself is grammatical and logical) and relevance (whether a response is appropriate and relevant to the post). The two metrics were evaluated independently.
The evaluation results are shown in Table TABREF35 . To measure the inter-annotator agreement, we calculated Fleiss' kappa BIBREF38 for each pair-wise comparison where results show moderate agreement ( INLINEFORM0 ). We also conducted sign test to check the significance of the differences.
As shown in Table TABREF35 , ARAML performs significantly better than other baselines in all the cases. This result indicates that the samples surrounding true responses provide stable rewards for the generator, and stable RAML training paradigm significantly enhances the performance in both metrics.
The two metrics were evaluated independently.
|
Were the two metrics independently when assessing the generation quality on WeiboDial?
|
Yes, they are.
|
null | false
| null |
Joshua Braff (born October 11, 1967) is an American writer.
Braff's first novel, The Unthinkable Thoughts of Jacob Green, described as a Jewish coming of age tale, was published in 2004 by Algonquin Books of Chapel Hill. The Unthinkable Thoughts of Jacob Green was an ALA Notable Book, named to Booklist's Top 10 1st Novels List, and chosen for the Barnes & Noble Discover Great New Writers program. His second novel Peep Show was published by Algonquin in 2010. In a four-star review of Peep Show, People said, “Braff skillfully illuminates the failures and charms of a broken family. That teen longing for adults to act their age haunts long after the final page.”
|
Do you know anything about the writer Joshua Braff
|
Joshua Braff (born October 11, 1967) is an American writer. Braff's first novel, The Unthinkable Thoughts of Jacob Green, described as a Jewish coming of age tale, was published in 2004 by Algonquin Books of Chapel Hill.
The Unthinkable Thoughts of Jacob Green was an ALA Notable Book, named to Booklist's Top 10 1st Novels List, and chosen for the Barnes & Noble Discover Great New Writers program. His second novel Peep Show was published by Algonquin in 2010.
In a four-star review of Peep Show, People said, “Braff skillfully illuminates the failures and charms of a broken family. That teen longing for adults to act their age haunts long after the final page.”
|
null | false
| null |
Which of the following are in the X-men: Wolverine, Cyclops, Gandalf, He-man and Gambit
|
Wolverine, Cyclops and Gambit are all in or were in the X men. Gandalf is a wizard from Lord of the Rings and He-man is an animated super hero but wasn't in the X men.
|
|
null | false
| null |
Which characters belong to DC or Marvel Universe? Mr Fantastic, Plastic Man, Elongated Man
|
Plastic Man and Elongated Man are DC, Mr Fantastic is Marvel
|
|
null | false
| null |
Which author created Dick Tracy
|
Chester Gould
|
|
null | false
| null |
Rank these transportation mode with respect to their CO2 impact from largest to smallest: train, plane, car
|
plane, car, train
|
|
null | false
| 276
|
Among neural networks, LSTMs BIBREF0 are commonly used for language modeling. Although new architectures BIBREF1, BIBREF2 challenge this standard, LSTMs remain competitive for language modeling BIBREF3. However, despite the success of LM LSTMs, it is not clear what makes them so effective. In particular, are representations derived through language modeling able to effectively encode syntactic structures and relations? Do they encode them in a reliable and systematic way?
The typical metric used to compare LMs, perplexity, is not adapted to address these questions. Perplexity measures the probability assigned to held-out data from the corpus the LM is trained on. Because the held-out and training data are typically randomly extracted from an initial corpus, they have similar statistics, which is good from a machine learning viewpoint, but bad from the viewpoint of linguistic analysis: perplexity is mostly sensitive to the most common sentence types in the initial corpus and therefore will not reflect well the behavior of the LM in the tail of the distribution. In addition, the sentences extracted from a natural corpus confound several factors: syntax, semantics, pragmatics, etc. further complicating the interpretation of a good perplexity score.
To circumvent this limitation, recent work has focused on using probing techniques inspired by linguistic and psycholinguistics (for instance, grammaticality or acceptability judgments, or forced choice). In addition, instead of using sentences from the training corpus, studies rely more and more on automatically constructed test sentences, which enable for a removal of the bias in the original corpus and focus on particular linguistic phenomena. Here, we will use acceptability judgments operationalized by the log probability of sentences according to the LM and sets of synthetic sentences generated from template sentences to probe for a challenging linguistic structure: verb argument structure.
Verb argument structure provides languages with a way to link syntactic position in a sentence (subject, direct object, etc) with semantic roles (agent, patient, etc), in other words, to determine who is doing what. It is currently unknown whether neural LMs purely trained from surface statistics are able to capture this kind of structure, or whether additional information from another modality would be needed to provide some semantic grounding.
Verb argument structure is typically correlated to sentence position in many languages like English. But in other languages with relatively free word order, it is indicated by morphological markers. Here, we study German, where the arguments of a verb can occur in any position (when occurring within a relative clause), and is indicated by the case of the noun phrase (nominative, accusative, etc).
We setup a test of argument structure representation by presenting a trained LM with carefully constructed sets of sentences that either have the right set of arguments, or abnormal sentences where one case is missing or duplicated. We use word order permutations to control for unigram and positional statistics. If the LM is able to track argument structure irrespective of word order, it should assign lower grammaticality scores (log probabilities) to the incorrect sentences as compared to the correct ones.
Since at the level of the sentence, we study a global rather than local syntactic phenomenon, we depart from earlier work BIBREF4, BIBREF5, BIBREF6, BIBREF7 and do not compare pairs of sentences. Rather, we compare a set of valid grammatical variations of the template to a corresponding set of grammatical violations of the template. Thus, for each template, we measure the model's ability to discriminate grammatical sentences from ungrammatical ones using receiver operating characteristic curves, or ROC curves. We also compute the area under the ROC curve, or AUC. In our results, we often report the average AUC over templates as our metric.
We evaluate three LMs on our dataset, the two-layer LSTM of BIBREF8 trained on German Wikipedia text, as well as n-gram baselines using the same corpus. We ask proficient German speakers to annotate our sentences for grammaticality, providing a human comparison. Since some of these sentences are rather implausible because of the permutations, we also collect human meaningfulness scores. We find that our dataset is challenging for both LMs and humans and that LMs lag behind human performance.
We find that our dataset is challenging for both LMs and humans and that LMs lag behind human performance.
|
Is the dataset challenging for both LMs and humans?
|
Yes, it is.
|
null | false
| null |
Brainstorm what is the best virtual cycling platform ?
|
There are multiple cycling platforms to choose from, each with their own pro’s and con’s.
Wahoo System is great for people who want to follow a dedicated training plan, and select from a large list of predefined workouts. Users can also ride content from RGT thanks to Wahoo’s acquisition of the company. Wahoo system is aimed at what is defined as “time crunched athletes, and uses a 4 dimensional power profile, rather than the FTP (Functional Threshold Power) that other systems use.
Zwift is one of the oldest virtual cycling system, and is based on a social experience that allows riders to communicate with others nearby, do group rides, workouts and Races. Zwift was not the first to introduce many of the virtual riding concepts, but the platform gained thousands of users during the covid-19 pandemic, and capitalised on the professional esports arena as well.
Zwift uses a more game-like graphical system, with multiple worlds that users can ride, with a monthly calendar changing the available worlds to ride it. Zwit also has a comprehensive workout catalog.
Rouvy is a cycling platform that contains many of the features already mentioned, but it uses real world videos, with rider avatars superimposed over the top (Augmented Reality). This gives you the feeling of actually riding the location you have selected, while seeing your vital statistics in the heads-up display.
BKool is another software company offering many of the features that have been described in Zwift and Rouvy.
FulGaz is a newer platform that offers real video riding in high definition, and allows riders to upload their own videos to the community.
If you are looking for a riding app that allows you to ride and meet other people, group ride and just enjoy your time on the bike, then Zwift is a good choice to make. If you are looking for some serious training, with detailed fitness reporting, the Wahoo systems would make a lot of sense. If you want some serious training, but also want the experience of riding in real locations around the world, the Rouvy is the one to try.
Each system requires a subscription to ride.
|
|
null | false
| null |
The movie Marquise of Armiani
|
The Marquise of Armiani (German: Die Marchesa d'Armiani) is a 1920 German silent film directed by Alfred Halm and starring Pola Negri, Ernst Dernburg, and Elsa Wagner.
The film's sets were designed by the art director Kurt Richter.
Cast
Pola Negri as Marchesa Assunta
Ernst Dernburg as Polizeichef
Elsa Wagner as Assuntas Mutter
Max Pohl
Fritz Schulz
|
|
null | false
| null |
Classify each of the following as either ski gear or swim gear: fins, pull buoy, bib, bindings, gloves, kickboard, neck gator, boots, and parka.
|
The items used for skiing include bib, bindings, gloves, neck gator, and boots. The items used for swimming include fins, pull buoy, kickboard, and parka.
|
|
null | false
| null |
The ISS programme evolved from the Space Station Freedom, a 1984 American proposal to construct a permanently crewed Earth-orbiting station, and the contemporaneous Soviet/Russian Mir-2 proposal from 1976 with similar aims. The ISS is the ninth space station to be inhabited by crews, following the Soviet and later Russian Salyut, Almaz, and Mir stations and the American Skylab. It is the largest artificial object in the solar system and the largest satellite in low Earth orbit, regularly visible to the naked eye from Earth's surface. It maintains an orbit with an average altitude of 400 kilometres (250 mi) by means of reboost manoeuvres using the engines of the Zvezda Service Module or visiting spacecraft. The ISS circles the Earth in roughly 93 minutes, completing 15.5 orbits per day.
|
From the passage provided, extract the average altitude in miles that the ISS orbits at.
|
The ISS orbits the Earth at an average altitude of 250 miles.
|
null | false
| null |
what are non-fungible token
|
A non-fungible token (NFT) is a unique digital identifier that cannot be copied, substituted, or subdivided, that is recorded in a blockchain, and that is used to certify ownership and authenticity. The ownership of an NFT is recorded in the blockchain and can be transferred by the owner, allowing NFTs to be sold and traded. NFTs can be created by anybody, and require few or no coding skills to create. NFTs typically contain references to digital files such as photos, videos, and audio. Because NFTs are uniquely identifiable assets, they differ from cryptocurrencies, which are fungible
|
|
null | false
| null |
What is a reporting dashboard?
|
A reporting dashboard is a way to show business metrics and business insights based on business data. It enables high level leadership and management to make and implement decisions that have a positive affect on a Company's revenue. These benefits include both added revenue and stopping lost revenue. Data Analyst work with Data Engineers to develop these dashboards and reporting.
|
|
null | false
| null |
Give me a list of things to do when I feel cold
|
When you feel cold, you could try putting on a jacket, using a blanket, turning up the heating, making yourself some tea or another hot drink or starting a fire in the fireplace.
|
|
null | false
| null |
What should be your day to day creator device ?
|
1. Iphone pro max
2. DJI OSMO pocket 2
DJI is such an awesome device to shoot as it has it's gimble and easy to operate. The video quality is actually nice
|
|
null | false
| null |
On December 20, 1966, Los Angeles businessmen Sam Schulman and Eugene V. Klein, both of whom owned the AFL side San Diego Chargers, and a group of minority partners were awarded an NBA franchise for Seattle, the first major-league sports franchise in the city. Schulman served as the active partner and head of team operations, and named the team SuperSonics as a nod to the city’s ties to the aviation industry, with Boeing's having recently been awarded a contract for an SST project.
Seattle SuperSonics began play on October 13, 1967; they were coached by Al Bianchi, and included All-Star guard Walt Hazzard and All-Rookie Team members Bob Rule and Al Tucker. The expansion team debuted in San Francisco with a 144–116 loss in their first game against Golden State Warriors. On October 21, the Seattle team's first win came against the San Diego Rockets in overtime 117–110, and SuperSonics finished the season with a 23–59 record.
|
Who was the first coach of the Seattle SuperSonics?
|
Al Bianchi was the first coach.
|
null | false
| 396
|
In this section, we outline the technical details of ParityBot. The system consists of: 1) a Twitter listener that collects and classifies tweets directed at a known list of women candidates, and 2) a responder that sends out positivitweets when hateful tweets are detected.
We collect tweets from Twitter's real-time streaming API. The stream listener uses the open-source Python library Tweepy BIBREF8. The listener analyses tweets in real-time by firing an asynchronous tweet analysis and storage function for each English tweet mentioning one or more candidate usernames of interest. We limit the streaming to English as our text analysis models are trained on English language corpora. We do not track or store retweets to avoid biasing the analysis by counting the same content multiple times. Twitter data is collected and used in accordance with the acceptable terms of use BIBREF9.
The tweet analysis and storage function acts as follows: 1) parsing the tweet information to clean and extract the tweet text, 2) scoring the tweet using multiple text analysis models, and 3) storing the data in a database table. We clean tweet text with a variety of rules to ensure that the tweets are cleaned consistent with the expectations of the analysis models (see Appdx SECREF9).
The text analysis models classify a tweet by using, as features, the outputs from Perspective API from Jigsaw BIBREF10, HateSonar BIBREF11, and VADER sentiment models BIBREF12. Perspective API uses machine learning models to score the perceived impact a tweet might have BIBREF10. The outputs from these models (i.e. 17 from Perspective, 3 from HateSonar, and 4 from VADER) are combined into a single feature vector for each tweet (see Appdx SECREF10). No user features are included in the tweet analysis models. While these features may improve classification accuracy they can also lead to potential bias BIBREF13.
We measure the relative correlation of each feature with the hateful or not hateful labels. We found that Perspective API's TOXICITY probability was the most consistently predictive feature for classifying hateful tweets. Fig. FIGREF5 shows the relative frequencies of hateful and non-hateful tweets over TOXICITY scores. During both elections, we opted to use a single Perspective API feature to trigger sending positivitweets. Using the single TOXICITY feature is almost as predictive as using all features and a more complex model SECREF14. It was also simpler to implement and process tweets at scale. The TOXICITY feature is the only output from the Perspective API with transparent evaluation details summarized in a Model Card BIBREF14, BIBREF15.
We measure the relative correlation of each feature with the hateful or not hateful labels. We found that Perspective API’s TOXICITY probability was the most consistently predictive feature for classifying hateful tweets.
|
What was the most consistently predictive feature for classifying hateful tweets they found?
|
Perspective API’s TOXICITY probability.
|
null | false
| null |
Dame Jane Morris Goodall DBE (/ˈɡʊdɔːl/; born Valerie Jane Morris-Goodall on 3 April 1934), formerly Baroness Jane van Lawick-Goodall, is an English primatologist and anthropologist. She is considered the world's foremost expert on chimpanzees, after 60 years studying the social and family interactions of wild chimpanzees. Goodall first went to Gombe Stream National Park in Tanzania in 1960, where she witnessed human-like behaviours amongst chimpanzees, including armed conflict.[failed verification]
She is the founder of the Jane Goodall Institute and the Roots & Shoots programme, and she has worked extensively on conservation and animal welfare issues. As of 2022, she is on the board of the Nonhuman Rights Project. In April 2002, she was named a UN Messenger of Peace. Goodall is an honorary member of the World Future Council.
|
Given this reference text about Jane Goodall, tell me how old she is, and her most notable accomplishments.
|
Jane Goodall, 89, is a world-renown primatologist, famous for her study of chimpanzees. She has dedicated her life to conservation and animal welfare issues, and founded the Jane Goodall Institute and the Roots & Shoots programme. She was named a UN Messenger of Peace in 2002, and is an honorary member of the World Future Council.
|
null | false
| null |
Can you write a brief summary for the book "Braving the Wilderness" by Brene Brown?
|
Braving the Wilderness is a book about belonging, identity, and leadership. A core idea is that if you are courageous enough to stand alone in the name of the virtues and values you believe in, you can actually create more belonging for others. To brave the wilderness is not to go into the woods armored and ready to fight, but with an open heart and conviction steadying your spine.
|
|
null | false
| 237
|
Research in social media analysis is recently seeing a surge in the number of research works applying representation learning models to solve high-level syntactico-semantic tasks such as sentiment analysis [1], semantic textual similarity computation [2], hashtag prediction [3] and so on. Though the performance of the representation learning models are better than the traditional models for all the tasks, little is known about the core properties of a tweet encoded within the representations. In a recent work, Hill et al. [4] perform a comparison of different sentence representation models by evaluating them for different high-level semantic tasks such as paraphrase identification, sentiment classification, question answering, document retrieval and so on. This type of coarse-grained analysis is opaque as it does not clearly reveal the kind of information encoded by the representations. Our work presented here constitutes the first step in opening the black-box of vector embeddings for social media posts, particularly tweets.
Essentially we ask the following question: “What are the core properties encoded in the given tweet representation?”. We explicitly group the set of these properties into two categories: syntactic and social. Syntactic category includes properties such as tweet length, the order of words in it, the words themselves, slang words, hashtags and named entities in the tweet. On the other hand, social properties consist of `is reply', and `reply time'. We investigate the degree to which the tweet representations encode these properties. We assume that if we cannot train a classifier to predict a property based on its tweet representation, then this property is not encoded in this representation. For example, the model which preserves the tweet length should perform well in predicting the length given the representation generated from the model. Though these elementary property prediction tasks are not directly related to any downstream application, knowing that the model is good at modeling a particular property (e.g., the social properties) indicates that it could excel in correlated applications (e.g., user profiling task). In this work we perform an extensive evaluation of 9 unsupervised and 4 supervised tweet representation models, using 8 different properties. The most relevant work is that of Adi et al. [5], which investigates three sentence properties in comparing unsupervised sentence representation models such as average of words vectors and LSTM auto-encoders. We differ from their work in two ways: (1) While they focus on sentences, we focus on social media posts which opens up the challenge of considering multiple salient properties such as hashtags, named entities, conversations and so on. (2) While they work with only unsupervised representation-learning models, we investigate the traditional unsupervised methods (BOW, LDA), unsupervised representation learning methods (Siamese CBOW, Tweet2Vec), as well as supervised methods (CNN, BLSTM).
Our main contributions are summarized below.
The paper is organized as follows. Sections 2 and 3 discuss the set of proposed elementary property prediction tasks and the models considered for this study respectively. Section 4 and 5 presents the experiment setup and result analysis respectively. We conclude the work with a brief summary in Section 5.
We explicitly group the set of these properties into two categories: syntactic and social.
|
What are the categories of these properties?
|
The authors explicitly group the set of these properties into two categories: syntactic and social.
|
2004.02929
| false
| null |
Results on all sets show an important difference between precision and recall, precision being significantly higher than recall. There is also a significant difference between the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49). The time difference between the supplemental test set and the development and test set (the headlines from the the supplemental test set being from a different time period to the training set) can probably explain these differences.
Results on all sets show an important difference between precision and recall, precision being significantly higher than recall. There is also a significant difference between the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49). The time difference between the supplemental test set and the development and test set (the headlines from the the supplemental test set being from a different time period to the training set) can probably explain these differences.
|
What is the performance of the CRF model on the task described?
|
The answers are shown as follows:
* the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49)
|
null | false
| null |
Munda Airport (IATA: MUA, ICAO: AGGM) is an international airport adjacent to the town of Munda, Western Province in Solomon Islands.
Originally built by Japanese forces during World War II and further developed by the U. S. Naval Construction Battalions 24 and 73 following its capture. After the war, the airfield became a commercial airport for regional flights. In 2015, a New Zealand government aid and development project significantly upgraded the field to an international airport. The upgrade included the removal of a large amount of unexploded ordnance left behind by vacating Japanese and US forces. Although there were no international flights scheduled by 2019, the main economic purpose of the upgrade is to serve as an alternate field for international flights to Honiara International Airport, significantly reducing the fuel load and improving the economics of the flight. For example, prior to the Munda upgrade, a flight from Brisbane to Honiara had to carry sufficient fuel to return to Brisbane because that was the nearest viable alternate.
|
Note down some details about Munda Airport from the given text
|
1. Munda Airport in Solomon Islands' Western Province is a global airport with the IATA and ICAO codes MUA and AGGM.
2. Developed further by the U.S. Naval Construction Battalions 24 and 73 after its surrender. It was initially constructed by Japanese forces during World War II.
3. The airfield was converted to a commercial airport for regional flights after the war.
4. The field was considerably improved to become an international airport in 2015 as part of a New Zealand government aid and development project.
5. A significant amount of unexploded ordnance left behind by withdrawing US and Japanese forces was also removed as part of the refurbishment.
6. Despite the fact that no international flights were scheduled beyond 2019, the main economic goal of the improvement is to act as a backup field for international flights to Honiara International Airport, hence lowering fuel consumption and enhancing flight economics.
7. As an example, before the Munda improvement, an aircraft from Brisbane to Honiara required to have enough fuel on board to make a turn around and return to Brisbane, which was the closest practical alternative.
|
null | false
| null |
Our Common Future, also known as the Brundtland Report, was published on October 1987 by the United Nations through the Oxford University Press. This publication was in recognition of Gro Harlem Brundtland's, former Norwegian Prime Minister, role as Chair of the World Commission on Environment and Development (WCED).
Its targets were multilateralism and interdependence of nations in the search for a sustainable development path. The report sought to recapture the spirit of the Stockholm Conference which had introduced environmental concerns to the formal political development sphere. Our Common Future placed environmental issues firmly on the political agenda; it aimed to discuss the environment and development as one single issue.
The document was the culmination of a "900-day" international exercise which catalogued, analysed, and synthesised written submissions and expert testimony from "senior government representatives, scientists and experts, research institutes, industrialists, representatives of non-governmental organizations, and the general public" held at public hearings throughout the world.
The report defined 'sustainable development' as "Development that meets the needs of the present without compromising the ability of future generations to meet their own needs".
|
Given a reference text about Our Common Future, what was the purpose of the report?
|
The purpose of the Our Common Future report was to focus on environmental issues on the political agenda and to discuss sustainable development.
|
null | false
| null |
Identify which animal is domesticated or wild: Pig, Arabian ostrich
|
Pig is domesticated, Arabian ostrich is wild
|
|
null | false
| null |
Britney Jean Spears (born December 2, 1981) is an American singer, actress, and dancer. Often referred to as the "Princess of Pop", she is credited with influencing the revival of teen pop during the late 1990s and early 2000s. After appearing in stage productions and television series, Spears signed with Jive Records in 1997 at age fifteen. Her first two studio albums, ...Baby One More Time (1999) and Oops!... I Did It Again (2000), are among the best-selling albums of all time and made Spears the best-selling teenage artist of all time. With first-week sales of over 1.3 million copies, Oops!... I Did It Again held the record for the fastest-selling album by a female artist in the United States for fifteen years. Spears adopted a more mature and provocative style for her albums Britney (2001) and In the Zone (2003), and starred in the 2002 film Crossroads.
|
Given a reference text about Britney Spears, tell me when they were born and why they are famous.
|
Britney Spears was born December 2, 1981 she is famous for being the "princess of pop" and being the best selling teenage artist of all time.
|
1911.08976
| false
| null |
FLOAT SELECTED: Table 2: MAP scoring of new methods. The timings are in seconds for the whole dev-set, and the BERT Re-ranking figure includes the initial Iterated TF-IDF step.
FLOAT SELECTED: Table 2: MAP scoring of new methods. The timings are in seconds for the whole dev-set, and the BERT Re-ranking figure includes the initial Iterated TF-IDF step.
|
what are the three methods presented in the paper?
|
Optimized TF-IDF, iterated TF-IDF, BERT re-ranking.
|
1910.01363
| false
| null |
Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.
In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter.
Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8.
In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content.
|
What proxies for data annotation were used in previous datasets?
|
The answers are shown as follows:
* widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet
* Natural Language Processing (NLP) models can be used to automatically label text content
|
null | false
| null |
What does zan zendegi azadi mean?
|
Zan zendegi azadi translates from Farsi to woman, life, freedom.
|
|
null | false
| 136
|
Based on the models described in Section we experiment with eight variants: (a) baseline transformer model (base); (b) base with AIC (base+sum); (c) base with AIF using spacial (base+att) or object based (base+obj) image features; (d) standard deliberation model (del); (e) deliberation models enriched with image information: del+sum, del+att and del+obj.
The encoder block comprises of 6 layers, with each containing two sublayers of multi-head self-attention mechanism followed by a fully connected feed forward neural network.
|
How many layers and sublayers does the encoder block comprise of?
|
6 layers, with each containing two sublayers.
|
null | false
| null |
What did Britain swap Havana for with Spain in 1763
|
Florida
|
|
null | false
| 185
|
Learning natural language generation (NLG) models heavily relies on annotated training data. However, most available datasets are collected in a single language (typically English), which restricts deploying the applications to other languages. In this work, we aim at transferring the supervision of a monolingual NLG dataset to unseen languages, so that we can boost performance for the low-resource settings.
Various methods have been proposed over the years to learn universal cross-lingual word embeddings BIBREF0, BIBREF1, BIBREF2 or sentence encoders BIBREF3, BIBREF4, BIBREF5, which tries to encode multilingual texts into a single shared vector space. Despite achieving promising results on cross-lingual classification problems, cross-lingual pre-trained models purposed for NLG tasks remains relatively understudied.
The cross-lingual generation problem is challenging due to the following reasons. First, it requires the models to understand multilingual input texts, and generate multilingual target sequences. So both encoder and decoder should be pre-trained together. Second, the many-to-many nature of cross-lingual NLG increases language pairs with the square of the number of languages. Third, the prediction space of cross-lingual NLG is much larger than classification tasks, which makes the knowledge transfer of decoders quite critical.
Previous work mainly relies on machine translation (MT) systems to map texts to different languages. The first strand of research directly uses MT in a pipeline manner BIBREF6. For example, the input written in other languages is first translated to English, and fed into the NLG model that is trained by English data. Then the generated English text is translated back to the target language. Another strand of work employs MT to generate pseudo training data for other language pairs that are lack of annotations BIBREF7, BIBREF8. However, such methods have to use multiple MT systems, which renders them suffering from error propagation. Moreover, because the pipeline-based methods do not explicitly share the same parameter space across the languages, we can not directly transfer the task-specific supervision to other low-resource languages.
In this paper, we propose a cross-lingual pre-trained model (named as Xnlg) in order to transfer monolingual NLG supervision to other pre-trained languages by fine-tuning. Specifically, Xnlg shares the same sequence-to-sequence model across languages, and is pre-trained with both monolingual and cross-lingual objectives. The model not only learns to understand multilingual input, but also is able to generate specific languages by conditioning on the encoded semantics. Figure FIGREF2 demonstrates how to use Xnlg to perform cross-lingual transfer for downstream tasks. The proposed model enables us to fine-tune the pre-trained model on monolingual NLG training data, and then evaluate it beyond a single language, including zero-shot cross-lingual generation. Besides, we explore several fine-tuning strategies to make a compromise between cross-lingual ability and task ability. In addition, we introduce two cross-lingual NLG datasets (i.e., question generation, and abstractive summarization) for evaluation, which includes three languages, namely English, Chinese, and French. Experimental results on the NLG tasks show that Xnlg achieves competitive performance compared with the machine-translation-based pipeline model in zero-shot cross-lingual settings.
The cross-lingual generation problem is challenging due to the following reasons. First, it requires the models to understand multilingual input texts, and generate multilingual target sequences. So both the encoder and the decoder should be pre-trained together. Second, the many-to-many nature of cross-lingual NLG increases language pairs with the square of the number of languages. Third, the prediction space of cross-lingual NLG is much larger than classification tasks, which makes knowledge transfer of decoders quite critical.
|
Why is the cross-lingual generation problem challenging?
|
First, it requires the models to understand multilingual input texts, and generate multilingual target sequences. So both the encoder and the decoder should be pre-trained together. Second, the many-to-many nature of cross-lingual NLG increases language pairs with the square of the number of languages. Third, the prediction space of cross-lingual NLG is much larger than classification tasks, which makes knowledge transfer of decoders quite critical.
|
null | false
| null |
What is the best way to earn money on the side?
|
Sell your unused belongings.
Buy, refurbish and then sell other peoples' belongings for a profit.
Tutor primary school students as the curriculum should be fairly straightforward and the main value-add is being present to guide children on school subjects or on homework.
Turn your hobby into a business.
Start your own website or blog and earn money through selling advertising on your website or blog.
If you are a dog owner, start a dog walking business.
|
|
null | false
| null |
New York, often called New York City or NYC, is the most populous city in the United States. With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), New York City is the most densely populated major city in the United States and more than twice as populous as Los Angeles, the nation's second-largest city. New York City is located at the southern tip of New York State. It constitutes the geographical and demographic center of both the Northeast megalopolis and the New York metropolitan area, the largest metropolitan area in the U.S. by both population and urban area. With over 20.1 million people in its metropolitan statistical area and 23.5 million in its combined statistical area as of 2020, New York is one of the world's most populous megacities, and over 58 million people live within 250 mi (400 km) of the city. New York City is a global cultural, financial, entertainment, and media center with a significant influence on commerce, health care and life sciences, research, technology, education, politics, tourism, dining, art, fashion, and sports. Home to the headquarters of the United Nations, New York is an important center for international diplomacy, and is sometimes described as the capital of the world.
|
Given the following paragraph about New York City, how many people live in the city?
|
As of 2020, there are over 20.1 million people living in the metropolitan statistical area of New York City. When considering the combined statistical area of New York City as of 2020, it is 23.5 million people.
|
1704.05907
| false
| null |
FLOAT SELECTED: Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).
FLOAT SELECTED: Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).
|
what state of the accuracy did they obtain?
|
51.5
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.