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Does the HP U32 4K HDR Monitor have built-in speakers?
|
No. Built-in speakers are not listed in the manufacturers specification page for this monitor. "HP U32 4K HDR Monitor Product Specifications" https://support.hp.com/us-en/document/c07721979.
A review of this monitor by PCMag lists "Lacks built-in speakers" as a Con against the monitor. https://www.pcmag.com/reviews/hp-u28-4k-hdr-monitor
The monitor does have a 3.5 mm Audio Jack (Audio Out) which can be used to connect external speakers.
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null | false
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Classify each of the following objects as either heavy or lightweight: bubble, aircraft, bear, mouse, bus, raisin, strawberry, popcorn, elephant, piano, plastic cup, train, butterfly, whale, refrigerator, feather, pencil, ship, tree, flower.
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Heavy weight objects include: aircraft, bear, bus, elephant, piano, refrigerator, ship, tree, train, whale.
Lightweight objects include: bubble, butterfly, feather, flower, mouse, pencil, plastic cup, popcorn, raisin, strawberry.
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null | false
| null |
What is retail banking?
|
Retail banking, also known as consumer banking or personal banking, is the provision of services by a bank to the general public, rather than to companies, corporations or other banks, which are often described as wholesale banking. Banking services which are regarded as retail include provision of savings and transactional accounts, mortgages, personal loans, debit cards, and credit cards. Retail banking is also distinguished from investment banking or commercial banking. It may also refer to a division or department of a bank which deals with individual customers.
In the U.S., the term commercial bank is used for a normal bank to distinguish it from an investment bank. After the Great Depression, the Glass–Steagall Act restricted normal banks to banking activities, and investment banks to capital market activities. That distinction was repealed in the 1990s. Commercial bank can also refer to a bank or a division of a bank that deals mostly with deposits and loans from corporations or large businesses, as opposed to individual members of the public (retail banking).
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1811.00854
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FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet. The first version of FarsNet contained more than 10,000 synsets while version 2.0 and 2.5 contained 20,000 synsets. Currently, FarsNet version 3 is under release and contains more than 40,000 synsets [7].
FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet.
|
What is the WordNet counterpart for Persian?
|
The answers are shown as follows:
* FarsNet
|
1808.00265
| false
| null |
We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is `looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0
We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is `looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0
|
How do they measure the correlation between manual groundings and model generated ones?
|
The answers are shown as follows:
* rank-correlation BIBREF25
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null | false
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What is skydiving?
|
Skydiving is considering a thrill seeking opportunity. It can be classified as a risky sport because every safety check and setup has to be 100% perfect in order to execute the skydiving perfectly each time. If something does go wrong or you miss a safety check injury or death can occur. That is why its important to triple check all your gear and equipment before skydiving or have an expert who has 1000's of jumps check your gear for you. Skydiving is considered fun for some and scare for others. If you are risk-averse you can consider this sport as a new opportunity for fun and enjoyment
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Lakshmi Puja is celebrated as a part of Tihar, a second national festival of Nepal after Dashain. In Nepal, it is celebrated for five days, which include Kag (crow) Tihar; Kukur (dog) Tihar; Gai (cow) Tihar in the morning and Lakshmi Puja at night; Maha puja (self puja); Goru (Ox and Bull) Tihar and Gobardhan puja; and finally, Bhai Tika (Bhai dhooj)—respectively the first, second, third, fourth and fifth days.
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What is the last festival celebrated in Tihar and how long does the festival last?
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The festival lasts for 5 days and Bhai Tika is the last festival.
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null | false
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How do you cook ribs?
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1. Spread French's mustard all over the ribs
2. Season ribs top and bottom liberally with the rub. Seriously don't be shy.
3. Place ribs on a smoker at 225 for 2-3 hours (oak).
4. In a bowl mix one cup of honey, brown sugar, a table spoon of apple cider vinegar, Mix together and Drizzle in the middle of the foil, place ribs top side down in the foil. Wrap tight.
5. Return to the smoke for another 1.5-2 hours or until tender but not 'falling off the bone'
6. In a sauce pan add a bottle of your favorite BBQ sauce, one cup of apple jelly, one finely shredded apple, and one tsp of rib rub.
7. Pull them out of the foil, season again with rub.
8. Top with BBQ sauce with apple and apple jelly.
9. Put the ribs back on the pit for 30 minutes for the sauce to set-up.
Ingredients:
· Three racks of pork ribs
· Rib rub
· French’s mustard
· Honey
· Brown sugar
· Apple cider vinegar
· KC Masterpiece BBQ sauce
· Apple jelly
· One finely shredded apple
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null | false
| 451
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A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function. Next we give the formal definition of kernel machines discussed in this paper. Given a training data set {X, y} of n training instances where {X ∈ R n×d , y ∈ R n } = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )}, and (x i , y i ) denotes the instance x i ∈ R d with its label y i , the objective of the kernel machine training is to find an optimal ω * which minimizes the structural risk as follows.
min
where λ denotes the regularization constant and f (ω, x i ) = ω, φ(x i ) . The variable ω is defined on the reproducing kernel Hilbert space (RKHS) and •, • is the inner product on the RKHS. The function φ(•) maps the instances from their original data space to a higher dimensional feature space induced by the kernel function. Assume the loss l(•, •) is an affine function of ω. The representer theorem shows that a minimizer of the optimization problem ()
where k(x i , x j ) denotes a positive definite kernel function and k(x i , x j ) = φ(x i ), φ(x j ) . By substituting the expressions of f (ω, x i ) and ω into the Equation (1), we have the objective with respect to α below.
where α = [α 1 . . . α n ] T is an n-dimension vector, each dimension of which corresponds to the contribution of a training instance to the kernel machine.
Then, we can derive that the Hessian matrix H = [H ij ] n×n of Problem () is equal to the kernel matrix. The element in the i-th row and j-th column of the matrix H is H ij = k(x i , x j ). The derivations of the first and second derivatives can be found in the supplementary material. For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function (Keerthi & Lin, 2003; Hofmann et al., 2008).
|
'A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem is linearly separable.''-Is the term ``linearly separable'' correct?
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It should be ''may be linearly separable''.
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null | false
| 38
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As observed by a recent article of Nature News BIBREF0 , “Wikipedia is among the most frequently visited websites in the world and one of the most popular places to tap into the world's scientific and medical information". Despite the huge amount of consultations, open issues still threaten a fully confident fruition of the popular online open encyclopedia.
A first issue relates to the reliability of the information available: since Wikipedia can be edited by anyone, regardless of their level of expertise, this tends to erode the average reputation of the sources, and, consequently, the trustworthiness of the contents posted by those sources. In an attempt to fix this shortcoming, Wikipedia has recently enlisted the help of scientists to actively support the editing on Wikipedia BIBREF0 . Furthermore, lack of control may lead to the publication of fake Wikipedia pages, which distort the information by inserting, e.g., promotional articles and promotional external links. Fighting vandalism is one of the main goals of the Wikimedia Foundation, the nonprofit organization that supports Wikipedia: machine learning techniques have been considered to offer a service to “judge whether an edit was made in good faith or not" BIBREF1 . Nonetheless, in the past recent time, malicious organisations have acted disruptively with purposes of extortion - see, e.g., the recent news on the uncovering of a blackmail network of accounts, which threatened celebrities with the menace of inserting offending information on their Wikipedia pages.
Secondly, articles may suffer from readability issues: achieving a syntactical accuracy that helps the reader with a fluid reading experience is —quite obviously— a property which articles should fulfill. Traditionally, the literature has widely adopted well known criteria, as the “Flesch-Kincaid" measure" BIBREF2 , to automatically assess readability in textual documents. More recently, new techniques have been proposed too, for assessing the readability of natural languages (see, e.g., BIBREF3 for the Italian use case, BIBREF4 for the Swedish one, BIBREF5 for English).
In this paper, we face the quest for quality assessment of a Wikipedia article, in an automatic way that comprehends not only readability and reliability criteria, but also additional parameters testifying completeness of information and coherence with the content one expects from an article dealing with specific topics, plus sufficient insights for the reader to elaborate further on some argument. The notion of data quality we deal with in the paper is coherent with the one suggested by recent contributions (see, e.g., BIBREF6 ), which points out like the quality of Web information is strictly connected to the scope for which one needs such information.
Our intuition is that groups of articles related to a specific topic and falling within specific scopes are intrinsically different from other groups on different topics within different scopes. We approach the article evaluation through machine learning techniques. Such techniques are not new to be employed for automatic evaluation of articles quality. As an example, the work in BIBREF7 exploits classification techniques based on structural and linguistic features of an article. Here, we enrich that model with novel features that are domain-specific. As a running scenario, we focus on the Wikipedia medical portal. Indeed, facing the problems of information quality and ensuring high and correct levels of informativeness is even more demanding when health aspects are involved. Recent statistics report that Internet users are increasingly searching the Web for health information, by consulting search engines, social networks, and specialised health portals, like that of Wikipedia. As pointed out by the 2014 Eurobarometer survey on European citizens' digital health literacy, around six out of ten respondents have used the Internet to search for health-related information. This means that, although the trend in digital health literacy is growing, there is also a demand for a qualified source where people can ask and find medical information which, to an extent, can provide the same level of familiarity and guarantees as those given by a doctor or a health professional.
We anticipate here that leveraging new domain-specific features is in line with this demand of articles quality. Moreover, as the outcomes of our experiments show, they effectively improve the classification results in the hard task of multi-class assessment, especially for those classes that other automatic approaches worst classify. Remarkably, our proposal is general enough to be easily extended to other domains, in addition to the medical one.
Section "Dataset" first describes the structure of the articles present in the medical portal. Then, it gives details on the real data used in the experiments, which are indeed articles extracted from the medical portal and labeled according to the manual assessment by the Wikimedia project. Section "Baseline: the actionable model" briefly presents the actionable model in BIBREF7 : we adopt it as the baseline for our analysis. In Section "The medical domain model" , we present the domain-specific, medical model we newly adopt in this paper as an extension of the baseline. The extended model includes features specifically extracted from the medical domain. One novel feature is based on the article textual content. Section "Bio-medical entities" presents the process which its extraction relies on, with a non trivial analysis of natural language and domain knowledge. Section "Experiments and results" presents experiments and results, with a comparison of the baseline model with the new one. In Section "Related work" , we survey related work in the area and in Section "Conclusions" we conclude the paper.
Moreover, as the outcomes of our experiments show, they effectively improve the classification results in the hard task of multiclass assessment, especially for those classes that other automatic approaches worst classify.
|
Does their approach improve the classification results in the hard task of multiclass assessment?
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Yes.
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null | false
| null |
Give me a few ways that I can get stronger and in better shape before the next ski season.
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Skiing requires both muscular strength and endurance in your legs and core, and it is important that as you exercise these muscles your knees and other joints are strengthened as well. Some good leg and knee exercises to prepare for skiing include both normal lunges and reverse lunges, lateral jumps, and both normal planks and side planks. Improving your cardiovascular health is also important, although this can be done in any way that keeps your heart rate in Zone 2 or Zone 3 for an extended duration of time.
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2002.01359
| false
| null |
FLOAT SELECTED: Table 2: The total number of intents (services in parentheses) and dialogues for each domain across train1, dev2 and test3 sets. Superscript indicates the datasets in which dialogues from the domain are present. Multi-domain dialogues contribute to counts of each domain. The domain Services includes salons, dentists, doctors, etc.
FLOAT SELECTED: Table 2: The total number of intents (services in parentheses) and dialogues for each domain across train1, dev2 and test3 sets. Superscript indicates the datasets in which dialogues from the domain are present. Multi-domain dialogues contribute to counts of each domain. The domain Services includes salons, dentists, doctors, etc.
|
What domains are present in the data?
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Alarm, Banks, Buses, Calendar, Events, Flights, Homes, Hotels, Media, Messaging, Movies, Music, Payment, Rental Cars, Restaurants, Ride Sharing, Services, Train, Travel, Weather
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null | false
| null |
Identify which animal species is alive or extinct: Meiolania, Tiger
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Tiger is alive, Meiolania is extinct.
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null | false
| null |
Why is the indictment of a former President such a divisive topic in the United States?
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You need to look at both the micro and macro implications to fully understand this topic. The micro implications are very specific to the case(s) being brought and therefore won't be covered here. The key macro implication of indicting a former President is the risk that the criminal justice system becomes a tool to persecute political opponents (former, current OR potential) by whichever person or party is in power. There are several examples of this in other countries throughout history. That being said, there are just as many examples of countries indicting former presidents without any sign of abuse. In fact, it can be an important part of the "checks and balances" of a democracy (see France, South Korea, etc.) One of the factors you can look at when determining if the justice system is being abused or not is the number of cases/indictments being brought against the former head of government. When the number of cases is low, there is a higher likelihood of abuse. But when the number of cases is high and for a diverse number of reasons, the likelihood of abuse is lower.
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null | false
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who is considered the father of computers?
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Charles Babbage, an English mechanical engineer and polymath, originated the concept of a programmable computer. Considered the "father of the computer", he conceptualized and invented the first mechanical computer in the early 19th century. After working on his revolutionary difference engine, designed to aid in navigational calculations, in 1833 he realized that a much more general design, an Analytical Engine, was possible.
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null | false
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Batuley (Gwatle lir) is a language spoken on the Aru Islands of eastern Indonesia. It is close to Mariri; Hughes (1987) estimates that around 80% of lexical items are shared. The language's name comes from the Gwatle island (Batuley in Indonesian), which the Batuley consider their homeland (Daigle (2015)).
|
What language is spoken on the Aru Islands?
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Batulay is the language spoken on the Aru Islands.
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null | false
| null |
The University of Oxford is a collegiate research university in Oxford, England. There is evidence of teaching as early as 1096, making it the oldest university in the English-speaking world and the world's second-oldest university in continuous operation. It grew rapidly from 1167 when Henry II banned English students from attending the University of Paris. After disputes between students and Oxford townsfolk in 1209, some academics fled north-east to Cambridge where they established what became the University of Cambridge. The two English ancient universities share many common features and are jointly referred to as Oxbridge.
|
Given the following paragraph, what university has evidence of teaching as early as 1096?
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The University of Oxford
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null | false
| null |
What caused World War 1?
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There is no definite answer, however there are several speculations about what caused World War 1. An answer approved by many historians is the assassination of Archduke Franz Ferdinand by the Black Hand, a Serbian nationalist organization. This assassination led to Austria-Hungary declaring war against Serbia. In response to this declaration, many countries joined forces under the guise of 'alliance'.
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null | false
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Which of these are fruits and vegetables: apple, tomato, pear, peach, broccoli?
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Of the ones listed, apple, pear and peach are fruits while broccoli is a vegetable. Tomatoes however, are botanically considered fruits while culinarily considered a vegetable.
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null | false
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Subtitled Explorations into Microtonal Tuning, Volume 1, the album is recorded in quarter tone tuning, where an octave is divided into 24 (logarithmically) equal-distanced quarter tones; it was originally conceived to play on a baglama, so the band members used instruments specifically modified for microtonal tuning, as well as other Middle-Eastern instruments like the zurna. The name "Flying Microtonal Banana" comes from Stu Mackenzie's custom-built yellow guitar, fitted with additional microtonal frets.
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What is the meaning of the album title "Flying Microtonal Banana", by King Gizzard and the Lizard Wizard?
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The title of the album "Flying Microtonal Banana" comes from the name of Stu Mackenzie's custom yellow guitar, fitted with microtonal frets. The guitar was built to use quarter tone tuning, in which the octave is divided into 24 equal-distanced tones.
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null | false
| null |
I'm preparing for a trip to Barbados. Which of these things would it be wise or unwise or dangerous to take: swimwear, fireworks, ski-jacket, sunscreen, hat, sunglasses and bike helmet.
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You would be wise to take: sunscreen, hat, swimwear and sunglasses.
You would be unwise to take: ski-jacket, bike helmet
It would be dangerous to take: fireworks
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null | false
| 33
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The information content of a concept may be derived from a corpus (corpus–based) or directly from a taxonomy (intrinsic–based). In this work we focus on corpus–based techniques.
For corpus–based information content, we estimate the probability of a concept INLINEFORM0 by taking the sum of the probability of the concept INLINEFORM1 and the probability its descendants INLINEFORM2 (Equation EQREF16 ). DISPLAYFORM0
The initial probabilities of a concept ( INLINEFORM0 ) and its descendants ( INLINEFORM1 ) are obtained by dividing the number of times each concept and descendant occurs in the corpus, and dividing that by the total numbers of concepts ( INLINEFORM2 ).
Ideally the corpus from which we are estimating the probabilities of concepts will be sense–tagged. However, sense–tagging is a challenging problem in its own right, and it is not always possible to carry out reliably on larger amounts of text. In fact in this paper we did not use any sense–tagging of the corpus we derived information content from.
Instead, we estimated the probability of a concept by using the UMLSonMedline dataset. This was created by the National Library of Medicine and consists of concepts from the 2009AB UMLS and the counts of the number of times they occurred in a snapshot of Medline taken on 12 January, 2009. These counts were obtained by using the Essie Search Engine BIBREF14 which queried Medline with normalized strings from the 2009AB MRCONSO table in the UMLS. The frequency of a CUI was obtained by aggregating the frequency counts of the terms associated with the CUI to provide a rough estimate of its frequency. The information content measures then use this information to calculate the probability of a concept.
Another alternative is the use of Intrinsic Information Content. It assess the informativeness of concept based on its placement within a taxonomy by considering the number of incoming (ancestors) relative to outgoing (descendant) links BIBREF15 (Equation EQREF17 ). DISPLAYFORM0
where INLINEFORM0 are the number of descendants of concept INLINEFORM1 that are leaf nodes, INLINEFORM2 are the number of concept INLINEFORM3 's ancestors and INLINEFORM4 are the total number of leaf nodes in the taxonomy.
Instead, we estimated the probability of a concept by using the UMLSonMedline dataset. This was created by the National Library of Medicine and consists of concepts from the 2009AB UMLS and the counts of the number of times they occurred in a snapshot of Medline taken on 12 January, 2009.
|
Which organization created the UMLSonMedline dataset?
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The National Library of Medicine.
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null | false
| null |
Identify which car manufacturer is British or American: Vauxhall, Tesla
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Tesla is American, Vauxhall is British
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null | false
| null |
What season are each of these associated with? Snow, flowers, beaches, pumpkins
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Snow: winter, flowers: spring, beaches: summer, pumpkins: fall
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null | false
| 8
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PDTB-style discourse relations, mostly defined between two adjacent text spans (i.e., discourse units, either clauses or sentences), specify how two discourse units are logically connected (e.g., causal, contrast). Recognizing discourse relations is one crucial step in discourse analysis and can be beneficial for many downstream NLP applications such as information extraction, machine translation and natural language generation.
Commonly, explicit discourse relations were distinguished from implicit ones, depending on whether a discourse connective (e.g., “because” and “after”) appears between two discourse units BIBREF0 . While explicit discourse relation detection can be framed as a discourse connective disambiguation problem BIBREF1 , BIBREF2 and has achieved reasonable performance (F1 score $>$ 90%), implicit discourse relations have no discourse connective and are especially difficult to identify BIBREF3 , BIBREF2 , BIBREF4 . To fill the gap, implicit discourse relation prediction has drawn significant research interest recently and progress has been made BIBREF5 , BIBREF6 by modeling compositional meanings of two discourse units and exploiting word interactions between discourse units using neural tensor networks or attention mechanisms in neural nets. However, most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between.
To further improve implicit discourse relation prediction, we aim to improve discourse unit representations by positioning a discourse unit (DU) in its wider context of a paragraph. The key observation is that semantic meaning of a DU can not be interpreted independently from the rest of the paragraph that contains it, or independently from the overall paragraph-level discourse structure that involve the DU. Considering the following paragraph with four discourse relations, one relation between each two adjacent DUs:
(1): [The Butler, Wis., manufacturer went public at $15.75 a share in August 1987,] $_{DU1}$ and (Explicit-Expansion) [Mr. Sim's goal then was a $29 per-share price by 1992.] $_{DU2}$ (Implicit-Expansion) [Strong earnings growth helped achieve that price far ahead of schedule, in August 1988.] $_{DU3}$ (Implicit-Comparison) [The stock has since softened, trading around $25 a share last week and closing yesterday at $23 in national over-the-counter trading.] $_{DU4}$ But (Explicit-Comparison) [Mr. Sim has set a fresh target of $50 a share by the end of reaching that goal.] $_{DU5}$
Clearly, each DU is an integral part of the paragraph and not independent from other units. First, predicting a discourse relation may require understanding wider paragraph-level contexts beyond two relevant DUs and the overall discourse structure of a paragraph. For example, the implicit “Comparison” discourse relation between DU3 and DU4 is difficult to identify without the background information (the history of per-share price) introduced in DU1 and DU2. Second, a DU may be involved in multiple discourse relations (e.g., DU4 is connected with both DU3 and DU5 with a “Comparison” relation), therefore the pragmatic meaning representation of a DU should reflect all the discourse relations the unit was involved in. Third, implicit discourse relation prediction should benefit from modeling discourse relation continuity and patterns in a paragraph that involve easy-to-identify explicit discourse relations (e.g., “Implicit-Comparison” relation is followed by “Explicit-Comparison” in the above example).
Following these observations, we construct a neural net model to process a paragraph each time and jointly build meaning representations for all DUs in the paragraph. The learned DU representations are used to predict a sequence of discourse relations in the paragraph, including both implicit and explicit relations. Although explicit relations are not our focus, predicting an explicit relation will help to reveal the pragmatic roles of its two DUs and reconstruct their representations, which will facilitate predicting neighboring implicit discourse relations that involve one of the DUs.
In addition, we introduce two novel designs to further improve discourse relation classification performance of our paragraph-level neural net model. First, previous work has indicated that recognizing explicit and implicit discourse relations requires different strategies, we therefore untie parameters in the discourse relation prediction layer of the neural networks and train two separate classifiers for predicting explicit and implicit discourse relations respectively. This unique design has improved both implicit and explicit discourse relation identification performance. Second, we add a CRF layer on top of the discourse relation prediction layer to fine-tune a sequence of predicted discourse relations by modeling discourse relation continuity and patterns in a paragraph.
Experimental results show that the intuitive paragraph-level discourse relation prediction model achieves improved performance on PDTB for both implicit discourse relation classification and explicit discourse relation classification.
However, most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between.
|
What is the drawback of existing approaches to predict implicit discourse relations?
|
Most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between.
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null | false
| null |
What film won Best Picture at the 95th Academy Awards in 2023?
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Everything Everywhere All at Once
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null | false
| null |
Forster began her work as a music promoter in Munich. Her home there became a meeting place for "rock royalty". Some of the acts she worked with in Germany were Jimi Hendrix, Wishbone Ash and Yes. She found German society to be too restricting, and decided to move to London with her daughter in the latter half of the 1960s. Their first flat was located in a "cold, damp and dark" basement in West London, near the Chelsea football ground. Following that they moved to a small house off Gowrie Road in South London. During this time she came to be called a "Punk Mummy Warrior" who guided her daughter, Ari Up's musical pursuits and supported the development of her band, The Slits, when Ari was just fourteen or fifteen. Forster hosted numerous musician house guests, and among them was Neneh Cherry, the teenage step-daughter of Don Cherry. Cherry performed backing vocal with The Slits for a time.
During the 1960s and 70s, Forster was part of the bohemian scene in London. Starting in the late 1960s Forster's home in Shepherd's Bush became a crash pad, salon, and meeting place for rock musicians including Joe Strummer of The Clash, Jimi Hendrix, Jon Anderson of the band Yes, and many other bands. She helped to financially support the punk bands The Slits, the Sex Pistols and The Clash.
|
Provide a summary of Nora Forster's career based on this passage.
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Nora Foster helped numerous bands and musical artists develop their careers in Germany and London. She would house them, financially support them, and promote their upcoming concerts and album releases. Some notable artists she helped included The Clash, The Slits, and Jimi Hendrix. Her homes were well known as meeting places for "rock royalty."
|
1912.11602
| false
| null |
Pretraining. We collect three years of online news articles from June 2016 to June 2019. We filter out articles overlapping with the evaluation data on media domain and time range. We then conduct several data cleaning strategies.
We collect three years of online news articles from June 2016 to June 2019.
|
What unlabeled corpus did they use?
|
The answers are shown as follows:
* three years of online news articles from June 2016 to June 2019
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null | false
| null |
Due to its historical roots as a town housing railroad workers, Hillyard acquired a rather rough reputation, which lasts into recent decades. (As recently as 1979, Daniel Leen described the Hillyard train yards in his book The Freighthoppers Manual for North America as having "the feel of warmed-over death.") After the Great Northern underwent a series of mergers, becoming the Burlington Northern Railroad and eventually the BNSF Railway, the Hillyard yard and shops were closed in the early 1980s with BN's main yard operations in Spokane moving to Yardley. Only the decaying structures of the old shops remain today. Hillyard suffers some of the worst poverty in Spokane, per capita it is the poorest neighborhood in the state of Washington. The eastern border of the neighborhood, east of the railroad tracks, is sometimes affectionately referred to as "Dog Town" by some residents
|
What was the final name of Great Northern in this passage
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After the first merger Great Northern became Burlington Northern Railroad, then eventually BNSF Railway.
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null | false
| null |
What should I do on a free afternoon in San Francisco?
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You can visit and take photos at San Francisco's landmarks such as Pier 39 or Golden Gate Bridge. If you enjoy walking outdoors, there are several large parks in the city. You can visit Golden Gate Park and the various attractions and museums located in the park. The Presidio is also a popular park where you can get some of the best views of Golden Gate Bridge.
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null | false
| null |
Classify each of the following as names of Pokemon or pop music groups: ABBA, Moltres, Pikachu, Eagles, *NSYNC, TLC, Vaporeon, Snorlax, i5, Dream, Blondie, Charizard, Charmander, Fleetwood Mac
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Pokemon: Moltres, Pikachu, Vaporeon, Snorlax, Charizard, Charmander
Pop music groups: ABBA, Eagles, TLC, i5, Dream, Blondie, Fleetwood Mac
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null | false
| null |
What is the capital of Australia?
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The capital of Australia is Canberra
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| 136
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We build and test our MMT models on the Multi30K dataset BIBREF21 . Each image in Multi30K contains one English (EN) description taken from Flickr30K BIBREF22 and human translations into German (DE), French (FR) and Czech BIBREF23 , BIBREF24 , BIBREF25 . The dataset contains 29,000 instances for training, 1,014 for development, and 1,000 for test. We only experiment with German and French, which are languages for which we have in-house expertise for the type of analysis we present. In addition to the official Multi30K test set (test 2016), we also use the test set from the latest WMT evaluation competition, test 2018 BIBREF25 .
We build and test our MMT models on the Multi30K dataset (Elliott et al., 2016).
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What dataset do their MMT models build and test on?
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Multi30K dataset.
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Give me a list of some American states which border Mexico
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California, Arizona, New Mexico and Texas all border Mexico
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tell me a name which is colour and fruit
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orange
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Who amongst these are women: Virat Kohli, Roger Federer, Rafael Nadal, Novak Djokovic, Serena Williams, PV Sindhu, Virender Sehwag, Maria Sharapova, Steffi Graf, Venus Williams.
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Serena Williams, PV Sindhu, Maria Sharapova, Steffi Graf, Venus Williams are women.
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An isotherm is a line that connects points on a map that have the same temperature. Therefore, all points through which an isotherm passes have the same or equal temperatures at the time indicated. An isotherm at 0 °C is called the freezing level. The term was coined by the Prussian geographer and naturalist Alexander von Humboldt, who as part of his research into the geographical distribution of plants published the first map of isotherms in Paris, in 1817.
|
What is an isotherm?
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An isotherm is a line that connects points on a map that have the same temperature.
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Tell me if the following countries are in Europe or in Africa: Portugal, Italy, Burkina Faso, Rwanda, Germany, Sweden, Spain
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Portugal, Italy, Germany, Sweden and Spain are located in Europe.
Burkina Faso and Rwanda are located in Africa.
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Wakesurfing is a water sport in which a rider trails behind a boat, riding the boat's wake without being directly pulled by the boat. After getting up on the wake, typically by use of a tow rope, the wakesurfers will drop the rope, and ride the steep face below the wave's peak in a fashion reminiscent of surfing. Wakesurfers generally use special boards, designed specifically for wakes.
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Given this paragraph about the description of wakesurfing, what is needed to generate the wave?
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a boat
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Which technology is best to learn for 2023 and the future?
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Artificial Intelligence, which is also popularly known as (AI), hits the top of the list of technologies 2023 to learn. Artificial Intelligence is very much high in demand today, where today, the machines are known to have brilliant skills to study, think, and examine without the need for human efforts.
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What does non subjective mean?
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Subjectiveness has an element of personal opinion, emotion and/or feelings, bias in response to question that invokes an answer based on your personal experience. Non subjectivity deals with being objective. Objectiveness deals with facts that are true based on historical data. There is no bias in objectiveness.
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Named Entity Recognition (NER) is a foremost NLP task to label each atomic elements of a sentence into specific categories like "PERSON", "LOCATION", "ORGANIZATION" and othersBIBREF0. There has been an extensive NER research on English, German, Dutch and Spanish language BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, and notable research on low resource South Asian languages like HindiBIBREF6, IndonesianBIBREF7 and other Indian languages (Kannada, Malayalam, Tamil and Telugu)BIBREF8. However, there has been no study on developing neural NER for Nepali language. In this paper, we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing.
Recent neural architecture like BIBREF1 is used to relax the need to hand-craft the features and need to use part-of-speech tag to determine the category of the entity. However, this architecture have been studied for languages like English, and German and not been applied to languages like Nepali which is a low resource language i.e limited data set to train the model. Traditional methods like Hidden Markov Model (HMM) with rule based approachesBIBREF9,BIBREF10, and Support Vector Machine (SVM) with manual feature-engineeringBIBREF11 have been applied but they perform poor compared to neural. However, there has been no research in Nepali NER using neural network. Therefore, we created the named entity annotated dataset partly with the help of Dataturk to train a neural model. The texts used for this dataset are collected from various daily news sources from Nepal around the year 2015-2016.
Following are our contributions:
We present a novel Named Entity Recognizer (NER) for Nepali language. To best of our knowledge we are the first to propose neural based Nepali NER.
As there are not good quality dataset to train NER we release a dataset to support future research
We perform empirical evaluation of our model with state-of-the-art models with relative improvement of upto 10%
In this paper, we present works similar to ours in Section SECREF2. We describe our approach and dataset statistics in Section SECREF3 and SECREF4, followed by our experiments, evaluation and discussion in Section SECREF5, SECREF6, and SECREF7. We conclude with our observations in Section SECREF8.
To facilitate further research our code and dataset will be made available at github.com/link-yet-to-be-updated
In this paper, we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data preprocessing.
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Is data preprocessing needed for the model?
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No.
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Radioactive decay (also known as nuclear decay, radioactivity, radioactive disintegration, or nuclear disintegration) is the process by which an unstable atomic nucleus loses energy by radiation. A material containing unstable nuclei is considered radioactive. Three of the most common types of decay are alpha decay (α-decay), beta decay (β-decay), and gamma decay (γ-decay), all of which involve emitting one or more particles. The weak force is the mechanism that is responsible for beta decay, while the other two are governed by the electromagnetism and nuclear force. A fourth type of common decay is electron capture, in which an unstable nucleus captures an inner electron from one of the electron shells. The loss of that electron from the shell results in a cascade of electrons dropping down to that lower shell resulting in emission of discrete X-rays from the transitions. A common example is iodine-125 commonly used in medical settings.
|
What is radioactive decay?
|
Radioactive decay is the process by which an unstable atomic nucleus loses energy by radiation. It is also known by terms such as nuclear decay, radioactivity, radioactive disintegration, or nuclear disintegration.
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Where are brook trout found?
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Native brook trout can be found in eastern North America, from northeastern Canada, and down the eastern seaboard as far south as the state of Georgia in the United States, where they are found in many high-elevation streams. Despite being native to those regions, brook trout can also be found throughout the rest of the world where populations are managed by hatcheries and stocked into local fisheries.
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What is the difference between a microservice and an API?
|
Microservices is an approach to building an application that breaks its functionality into modular components. APIs are part of an application that communicates with other applications.
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My dog is a 100lb pitbull purebreed. She's always barking at people when we walk her on the street. She's friendly, however, when outside with me or the kids, her bark is loud and deep. How can I get my dog to stop barking at strangers and remove her feeling of "threat"?
|
Ensure that when you walk her to use a prong collar. Ensure the collar is not loose on her neck, but high up and tight (cannot move easily) under her ear. This will make sure she doesn't take sudden movements and allow her to ensure that you will direct her whether the oncoming distraction is a threat.
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Lysergic acid diethylamide, commonly known as LSD (from German Lysergsäure-diethylamid), also known colloquially as acid, is a potent psychedelic drug. Effects typically include intensified thoughts, emotions, and sensory perception. At sufficiently high dosages LSD manifests primarily mental, visual, as well as auditory, hallucinations. Dilated pupils, increased blood pressure, and increased body temperature are typical. Effects typically begin within half an hour and can last for up to 20 hours. LSD is also capable of causing mystical experiences and ego dissolution. It is used mainly as a recreational drug or for spiritual reasons. LSD is both the prototypical psychedelic and one of the "classical" psychedelics, being the psychedelics with the greatest scientific and cultural significance. LSD is typically either swallowed or held under the tongue. It is most often sold on blotter paper and less commonly as tablets, in a watery solution or in gelatin squares called panes.
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What is LSD
|
Lysergic acid diethylamide, commonly known as LSD (from German Lysergsaeure-diethylamid), also known colloquially as acid, is a potent psychedelic drug.
Effects typically include intensified thoughts, emotions, and sensory perception. At sufficiently high dosages LSD manifests primarily mental, visual, as well as auditory, hallucinations. Dilated pupils, increased blood pressure, and increased body temperature are typical.
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In literature who is the alter ego of Percy Blakney
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Scarlet Pimpernel
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Etlingera diepenhorstii is a monocotyledonous plant species that was first described by Johannes Elias Teijsmann and Simon Binnendijk, and given its current name by Rosemary Margaret Smith. Etlingera diepenhorstii is part of the genus Etlingera and the family Zingiberaceae.
The species' range is in Sumatra. No subspecies are listed in the Catalog of Life.
|
Do you know the species Etlingera diepenhorstii
|
Yes, I do. The Etlingera diepenhorstii is a monocotyledonous plant species that was first described by Johannes Elias Teijsmann and Simon Binnendijk, and given its current name by Rosemary Margaret Smith. Etlingera diepenhorstii is part of the genus Etlingera and the family Zingiberaceae.
The species' range is in Sumatra. No subspecies are listed in the Catalog of Life.
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What are the most high demands jobs in the market right now?
|
-Nurse Practitioner
-Physician Assistant
-Medical and Health Services Manager
-Information Security Analyst
-Web Developer
-Dentist
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1907.01413
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The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. The networks are optimized for 40 epochs with a mini-batch of 32 samples using stochastic gradient descent. Based on preliminary experiments on the validation set, hyperparameters such learning rate, decay rate, and L2 weight vary depending on the input format (Raw, PCA, or DCT). Generally, Raw inputs work better with smaller learning rates and heavier regularization to prevent overfitting to the high-dimensional data. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes. The convolutional layers use 16 filters, 8x8 and 4x4 kernels respectively, and rectified units. The fully-connected layers use dropout with a drop probability of 0.2. Because CNN systems take longer to converge, they are optimized over 200 epochs. For all systems, at the end of every epoch, the model is evaluated on the development set, and the best model across all epochs is kept.
The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function.
As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes.
|
What model do they use to classify phonetic segments?
|
The answers are shown as follows:
* feedforward neural networks
* convolutional neural networks
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Nicaragua has a large diaspora that make significant contributions to the economy. Recognizing that forcing them to give up Nicaraguan nationality to acquire a different nationality would impact the country, changes were made to eliminate the possibility that birthright Nicaraguans could lose their nationality. Nicaragua has dual nationality treaties with Argentina, the nations of Central America, Italy and Spain. Children born abroad who obtain nationality by birth in another place are also accepted as dual nationals. In the case that their parents are naturalized Nicaraguans, they must ratify their choice of Nicaraguan citizenship upon attaining the age of 16.
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Given this paragraph about Nicaraguan nationality law, with which countries can Nicaraguan citizens obtain dual citizenship?
|
Nicaraguan nationals can obtain dual citizenship through joint-treaty with Argentina, Central American nations, Italy, and Spain. Children with parents who are Nicaraguan citizens that are born abroad will receive dual citizenship.
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Once per game, each king can make a move known as castling. Castling consists of moving the king two squares toward a rook of the same color on the same rank, and then placing the rook on the square that the king crossed.
Castling is permissible if the following conditions are met:
Neither the king nor the rook has previously moved during the game.
There are no pieces between the king and the rook.
The king is not in check and does not pass through or land on any square attacked by an enemy piece.
Castling is still permitted if the rook is under attack, or if the rook crosses an attacked square.
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Can you castle if the rook previously moved
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No.
In order to castle, the king and the rook involved in the move, must have never moved previously in the game.
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What are Newton’s laws of motion?
|
Newton’s laws of motion are three fundamental laws residing in mechanical physics. These laws describe the relationship of objects, the paths of motion, and the forces that are in action between them.
Newton’s laws are as follows:
- An object at rest remains at rest, and an object in motion remains in motion unless acted upon by an external force.
- The force of an object is equivalent to that object’s mass multiplied by that object’s acceleration. (F=ma)
- For every action, there is an equal and opposite reaction.
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What is solar minimum and how it is related to sun cycle?
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Sun has a cycle that lasts on an average of 11 years
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Pullman National Historical Park is a historic district located in Chicago and was the first model, planned industrial community in the United States. The district had its origins in the manufacturing plans and organization of the Pullman Company and became one of the most well-known company towns in the United States, as well as the scene of the violent 1894 Pullman strike. It was built for George Pullman as a place to produce the famous Pullman sleeping cars.
|
What is Pullman National Historical Park?
|
Pullman National Historical Park is a historic district located in Chicago and was the first model, planned industrial community in the United States. It was built for George Pullman as a place to produce the famous Pullman sleeping cars.The park was made a national monument by president Barack Obama on February 19, 2015. The district had its origins in the manufacturing plans and organization of the Pullman Company and became one of the most well-known company towns in the United States, as well as the scene of the violent 1894 Pullman strike. During the strike the American Railway Union led by Eugene V. Debs boycotted the company following the panic of 1893 crippling railroad traffic in the United States. Thousands of U.S marshals and army troops were deployed to break up the strike. The strike ultimately changed the American labor landscape showing the power a union could have against an industry.
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Necrotizing enterocolitis (NEC) is a devastating intestinal disease that affects premature or very low birth weight infants. Symptoms may include poor feeding, bloating, decreased activity, blood in the stool, vomiting of bile, bowel death, multiorgan failure, and even death.
The exact cause is unclear. However, several risk factors have been identified. Consistently described risk factors include formula feeding, intestinal dysbiosis, low birth weight, and prematurity. Maternal factors such as chorioamnionitis, cocaine abuse, in utero growth restriction, intrahepatic cholestasis during pregnancy, increased body mass index, lack of prenatal steroids, mode of delivery, placental abruption, preeclampsia, and smoking have not been consistently implicated with the development of NEC. Other risk factors potentially implicated include congenital heart disease, birth asphyxia, exchange transfusion, and prelabor rupture of membranes. The underlying mechanism is believed to involve a combination of poor blood flow and infection of the intestines. Diagnosis is based on symptoms and confirmed with medical imaging.
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Can formula feeding cause NEC in a pre-mature infant?
|
Yes, formula feeding can increase the risk of an pre-mature infant getting Necrotizing enterocolitis.
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Shulman was born in St. Paul, Minnesota, and raised in the city's Selby-Dale neighborhood. His father Abraham, a house painter, and his mother Bessie Karchmar were Jewish immigrants from Belarus.
As a student at the University of Minnesota, where he was classmate of Thomas Heggen, Thomas R. St. George and Norman Katkov, Shulman wrote a column for the Minnesota Daily as well as pieces for Ski-U-Mah, the college humor magazine. His writing humorously exaggerated campus culture. Shortly after Shulman graduated in 1942, an agent from Doubleday persuaded Shulman to send him some clips, which resulted in the campus satire Barefoot Boy With Cheek, a surprise 1943 bestseller. In 1947 Shulman adapted Barefoot Boy into a musical of the same name.
|
What was written in Ski-U-Mah?
|
Ski-U-Mah was the college humor magazine for University of Minnesota. Shulman wrote pieces for Ski-U-Mah humorously exaggerating campus culture.
It is not clear from the text what else was written in Ski-U-Mah, but presumably there were other humorous pieces written about topics relating to University of Minnesota.
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Where are good places for a family to visit in Portland Oregon?
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1. The Oregon Museum of Science and Industry (OMSI) is a science and technology museum in Portland Oregon. The museum has many interactive exhibits, a theatre and a decommissioned naval submarine.
2. The Springwater Corridor is a pedestrian and bicycle pathway between Portland and Gresham Oregon. There are nature walks, bridges and scenic areas throughout the Portland Metro area.
3. The Oregon Zoo is home to Elephants, Lions, Polar Bears as well as Pacific Northwest Native species of mammals and birds. The Oregon Zoo is located in Washington Park. The zoo is the oldest zoo west of the Mississippi river.
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| 444
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Instead of training a scalar state-action estimator Q π (s, u), distributional RL represents an actionvalue as a random variable, denoted Z π (s, u). The distributional Bellman operator for policy evaluation in single-agent RL can then be expressed as follows:
where S ∼ P (s |s, u), U ∼ π(u|s), and A D = B denotes the two random variables which follow the same probability distribution.
IQN. Implicit Quantile Networks (IQN) is one of the widely-used single-agent distributional RL methods. IQN approximates the true action-value function using a quantile-based representation of the distributions. To be specific, each trained quantile represent a random variable Z(s, u, ω) which is a reparameterization of risk-level sample ω drawn from a uniform distribution U(0, 1). To train the IQN, one can use the following Huber quantile regression loss:
where Z target is the target network whose parameters are updated periodically from the original action-value estimator Z. (s, u, r, s ) is a tuple of experience transitions from a replay buffer. In distributional RL, weights are used to express level of rewards and next state transitions are generated from the randomness of the environment.
Distributional MARL. Recently, distributional RL has also been applied to the CTDE regime, but they lack the disentanglement of risk sources. RMIX and DFAC showed promising results by extending the agent-wise utility function from having deterministic variables to random variables. RMIX demonstrates the effectiveness of risk-sensitive MARL via distributional RL, but it is limited since it cannot represent policies which came from different risk sources (i.e., agent-wise risk-seeking yet environmentwise risk-averse policies). DFAC is another distributional MARL framework that uses mean-shape decomposition which separates the mean and variance parts of the utility functions to handle value function factorization. They propose DDN and DMIX as the DFAC variants of VDN and QMIX, respectively. DFAC extends IQN, a single-agent distributional RL algorithm, to learn the randomness of the environment. However, they do not consider the agent-wise risk that arises when learning networks with limited expressive power such as QMIX. Also, they only demonstrate risk-neutral policies, so it is questionable whether DFAC could handle risk-sensitive policies.
DFAC is another distributional MARL framework that uses mean-shape decomposition which separates the mean and variance parts of the utility functions to handle value
function factorization. They propose DDN and DMIX as the DFAC variants of VDN and QMIX, respectively. DFAC extends IQN, a single-agent distributional RL algorithm, to learn the randomness of the environment. However, they do not consider the agent-wise risk that arises when learning networks with limited expressive power such as QMIX. Also, they only demonstrate risk-neutral policies, so it is questionable whether DFAC could handle risk-sensitive policies.
|
What is the meaning of mean-shape decomposition?
|
To address your concern, we added a description of DFAC in Section 2.2 in the revised draft. For example, we added a description that DFAC separates mean and variance part of utility function to handle the value function factorization through mean-shape decomposition
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| 32
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We have described the NLP4IF@EMNLP-IJCNLP 2019 shared task on fine-grained propaganda identification. We received 25 and 12 submissions on the test set for the sentence-level classification and the fragment-level classification tasks, respectively. Overall, the sentence-level task was easier and most submitted systems managed to outperform the baseline. The fragment-level task proved to be much more challenging, with lower absolute scores, but most teams still managed to outperform the baseline.
We plan to make the schema and the dataset publicly available to be used beyond NLP4IF. We hope that the corpus would raise interest outside of the community of researchers studying propaganda: the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
As a kind of advertisement, Task 11 at SemEval 2020 is a follow up of this shared task. It features two complimentary tasks:
Given a free-text article, identify the propagandist text spans.
Given a text span already flagged as propagandist and its context, identify the specific propaganda technique it contains.
This setting would allow participants to focus their efforts on binary sequence labeling for Task 1 and on multi-class classification for Task 2.
We plan to make the schema and the dataset publicly available to be used beyond NLP4IF.
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What do the authors plan to do?
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They plan to make the schema and the dataset publicly available to be used beyond NLP4IF.
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Identify which animal species is alive or extinct: Albertonectes, African forest elephant
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Albertonectes is extinct, African forest elephant is alive.
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Why would an oil and gas exploration company utilize directional drilling in their drilling program?
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There are several reasons and oil and gas exploration company would need to utilize directional drilling techniques when drilling wells. If, for any reason, a drill site is not accessible from the surface (ground level) then the drilling rig will be offset from the target. Because the drilling rig is not physically able to locate on top of the geological target the oil and gas company must use directional drilling techniques to steer the well to the target from an offset position. Examples of this could include drilling for resources under Federal land or when drilling in highly populated areas. Another reason for utilizing directional drilling techniques is to maximize hydrocarbon production. Directional drillers can turn the well-bore to intersect with the target zone at the X-Y axis, instead of only through the Z-axis. This maximizes the amount of "zone" the well-bore has access to.
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Should I use a dynamic microphone or a condenser microphone for recording a podcast? What about streaming on twitch or making youtube videos? Should I ever use a ribbon microphone?
|
If you use a modern Apple laptop made after the year 2021, the built-in microphone has sufficient clarity and frequency response to make good recordings of your voice. It's not suitable for recording instruments, however.
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Should I buy a horse?
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Buying a horse has lots of pro's and cons. A horse is great to own as you are able to build a strong relationship. You will have freedom and can be at your horse anytime it suits you. And you can ride or walk your horse in ways you want to do this. You will also become best buddies as you will spend a lot of time together. Maintaining a horse however can be a lot of work, and its pretty costly, make sure you have some help and do not underestimate the responsibility and time you have to put in. Enjoy the ride.
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Qubes implements a Security by Isolation approach. The assumption is that there can be no perfect, bug-free desktop environment: such an environment counts millions of lines of code and billions of software/hardware interactions. One critical bug in any of these interactions may be enough for malicious software to take control of a machine.
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Should we all use Qubes OS for most daily task that involve the internet?
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I have been using Qubes OS for all my online activities for the last few years. Qubes' disposable VM concept fit nicely in this use case.
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Identify which instrument is string or woodwind: Tenor violin, Bass flute
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Bass flute is woodwind, Tenor violin is string.
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What are the different types of cancers?
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Cancers are classified in two ways: Histological type, or the type of tissue in which the cancer originated, and by primary site, or the location in the body where the cancer first developed.
From a histological standpoint, there are hundreds of different cancers, which are grouped into six major categories:
1. Carcinoma
2. Sarcoma
3. Myeloma
4. Leukemia
5. Lymphoma
6. Mixed Types
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Muse released their debut album, Showbiz, in 1999, showcasing Bellamy's falsetto and a melancholic alternative rock style. Their second album, Origin of Symmetry (2001), incorporated wider instrumentation and romantic classical influences and earned them a reputation for energetic live performances. Absolution (2003) saw further classical influence, with strings on tracks such as "Butterflies and Hurricanes", and was the first of seven consecutive UK number-one albums.
Black Holes and Revelations (2006) incorporated electronic and pop elements, displayed in singles such as "Supermassive Black Hole", and brought Muse wider international success. The Resistance (2009) and The 2nd Law (2012) explored themes of government oppression and civil uprising and cemented Muse as one of the world's major stadium acts. Topping the US Billboard 200, their seventh album, Drones (2015), was a concept album about drone warfare and returned to a harder rock sound. Their eighth album, Simulation Theory (2018), prominently featured synthesisers and was influenced by science fiction and the simulation hypothesis. Their ninth album, Will of the People (2022), which combined many genres and themes from their previous albums, was released in August 2022.
|
From the paragraph provided, extract the year of the release of the album Origin of Symmetry.
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The album Origin of Symmetry was released in 2001.
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Name some of John Grisham's bestselling novels.
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John Grisham is a famous American novelist and former lawyer. He is best known for writing crime procedural and legal thrillers. His first best-selling novel was A Time to Kill in 1989. The Firm novel was released in 1991 and became a movie starring Tom Cruise in 1993. Other famous bestselling books by John Grisham include: The Pelican Brief in 1992; The Runaway Jury in 1996; The Rainmaker in 1995; The Broker in 2004;
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By the first millennium BCE, ironworking had been introduced in Northern Africa. Around that time it also became established in parts of sub-Saharan Africa, either through independent invention there or diffusion from the north and vanished under unknown circumstances around 500 CE, having lasted approximately 2,000 years, and by 500 BCE, metalworking began to become commonplace in West Africa. Ironworking was fully established by roughly 500 BCE in many areas of East and West Africa, although other regions didn't begin ironworking until the early centuries CE. Copper objects from Egypt, North Africa, Nubia, and Ethiopia dating from around 500 BCE have been excavated in West Africa, suggesting that Trans-Saharan trade networks had been established by this date.
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How long did it take for ironworking to be commonplace in West Africa following its introduction in Northern Africa?
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Ironworking was introduced in Northern Africa around the first millennium BCE and became commonplace in West Africa by 500 BCE. This means it took 500 years (1000-500) for ironworking to become common in West Africa following its introduction in Northern Africa.
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Identify which instrument is string or percussion: Khol, Mondol
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Mondol is string, Khol is percussion.
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The Celtics–Lakers rivalry is a National Basketball Association (NBA) rivalry between the Boston Celtics and the Los Angeles Lakers. The Celtics and the Lakers are the two most storied franchises in the NBA, and the rivalry has been called the greatest in the NBA. The Boston Celtics and the Los Angeles Lakers have met a record 12 times in the NBA Finals, with their first such meeting being in 1959. They would both go on to dominate the league in the 1960s and 1980s, facing each other in the Finals six times in the 1960s, three times in the 1980s, and twice since the year 2000.
The Lakers and Celtics are tied for the highest number of championships in the NBA at 17 apiece, (12 as the L.A. Lakers and 5 as the Minneapolis Lakers). Together, they account for 34 of the 74 championships (or 45%) in NBA history. As of 2018, the Celtics and Lakers have a .590 and .596 all-time winning records respectively. As of the end of the 2020–21 season, Boston is the only team with a winning overall record against the Lakers.
The rivalry has been less intense since the retirements of Larry Bird and Magic Johnson in the early 1990s. In 2008 the two teams met in the NBA Finals for the first time since 1987, with the Celtics winning the series 4–2. They met again in the 2010 NBA Finals, which the Lakers won in 7 games.
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Give me an overview of the rivalry between the Celtics and Lakers.
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The Celtics-Lakers rivalry is an NBA(National Basketball Association) rivalry between the Boston Celtics and the Los Angeles Lakers. They are two of the most storied NBA franchises, and have met a record 12 times in the NBA finals. The first meeting in the finals between them was in 1959, and the latest was in 2010. Both teams are tied for the highest number of NBA championships at 17, and account for 34 of the 72 total NBA championships. The rivalry peaked in the 60s and 90, when the teams faced each other in the finals 6 times in the 60s and 3 times in the 90s.
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Grand-Lahou Department was created in 1988 as a first-level subdivision via a split-off from Abidjan Department.
In 1997, regions were introduced as new first-level subdivisions of Ivory Coast; as a result, all departments were converted into second-level subdivisions. Grand-Lahou Department was included in Lagunes Region.
In 2011, districts were introduced as new first-level subdivisions of Ivory Coast. At the same time, regions were reorganised and became second-level subdivisions and all departments were converted into third-level subdivisions. At this time, Grand-Lahou Department became part of Grands-Ponts Region in Lagunes District.
|
here is some text about Grand-Lahou Department, What year did Grand-Lahou become part of Lagunes District?
|
In 2011.
|
null | false
| null |
What are good activities for two year old boys?
|
Two year old boys tend to be active, curious and social. Enjoyable activities for this age include going to the park, dancing to music, reading a book, and taking a walk around the neighborhood. Local community organizations also commonly plan activities for toddlers at places like libraries, schools and parks.
|
|
1811.09786
| false
| null |
On the 16 review datasets (Table TABREF22 ) from BIBREF32 , BIBREF31 , our proposed RCRN architecture achieves the highest score on all 16 datasets, outperforming the existing state-of-the-art model - sentence state LSTMs (SLSTM) BIBREF31 . The macro average performance gain over BiLSTMs ( INLINEFORM0 ) and Stacked (2 X BiLSTM) ( INLINEFORM1 ) is also notable. On the same architecture, our RCRN outperforms ablative baselines BiLSTM by INLINEFORM2 and 3L-BiLSTM by INLINEFORM3 on average across 16 datasets.
On the same architecture, our RCRN outperforms ablative baselines BiLSTM by INLINEFORM2 and 3L-BiLSTM by INLINEFORM3 on average across 16 datasets.
|
By how much do they outperform BiLSTMs in Sentiment Analysis?
|
Proposed RCRN outperforms ablative baselines BiLSTM by +2.9% and 3L-BiLSTM by +1.1% on average across 16 datasets.
|
1812.06705
| true
| null |
FLOAT SELECTED: Table 2: Accuracies of different methods for various benchmarks on two classifier architectures. CBERT, which represents conditional BERT, performs best on two classifier structures over six datasets. “w/” represents “with”, lines marked with “*” are experiments results from Kobayashi(Kobayashi, 2018).
FLOAT SELECTED: Table 2: Accuracies of different methods for various benchmarks on two classifier architectures. CBERT, which represents conditional BERT, performs best on two classifier structures over six datasets. “w/” represents “with”, lines marked with “*” are experiments results from Kobayashi(Kobayashi, 2018).
|
Are other pretrained language models also evaluated for contextual augmentation?
|
No.
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null | false
| 163
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The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i.e., positive/negative). Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not., to rule out the Priming Effect (i.e., if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 ). The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention. This is done by setting the task to polarity annotation (instead of sarcasm detection). (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not.
The eye-tracking experiment is conducted by following the standard norms in eye-movement research BIBREF13 . At a time, one sentence is displayed to the reader along with the “aspect” with respect to which the annotation has to be provided. While reading, an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz) records several eye-movement parameters like fixations (a long stay of gaze) and saccade (quick jumping of gaze between two positions of rest) and pupil size.
The accuracy of polarity annotation varies between 72%-91% for sarcastic texts and 75%-91% for non-sarcastic text, showing the inherent difficulty of sentiment annotation, when sarcasm is present in the text under consideration. Annotation errors may be attributed to: (a) lack of patience/attention while reading, (b) issues related to text comprehension, and (c) confusion/indecisiveness caused due to lack of context.
For our analysis, we do not discard the incorrect annotations present in the database. Since our system eventually aims to involve online readers for sarcasm detection, it will be hard to segregate readers who misinterpret the text. We make a rational assumption that, for a particular text, most of the readers, from a fairly large population, will be able to identify sarcasm. Under this assumption, the eye-movement parameters, averaged across all readers in our setting, may not be significantly distorted by a few readers who would have failed to identify sarcasm. This assumption is applicable for both regular and multi-instance based classifiers explained in section SECREF6 .
Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not., to rule out the Priming Effect (i.e., if sarcasm is expected beforehand, processing incongruity becomes relatively easier (Gibbs, 1986)).
|
Why were the participants not instructed to annotate whether a sentence is sarcastic or not?
|
To rule out the Priming Effect.
|
null | false
| 144
|
The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token. With these characteristics, the document clusters are 15 times larger than typical DUC clusters of ten documents and five times larger than the 25-document-clusters (Table TABREF26 ). In addition, the documents are also more variable in terms of length, as the (length-adjusted) standard deviation is twice as high as in the other corpora. With these properties, the corpus represents an interesting challenge towards real-world application scenarios, in which users typically have to deal with much more than ten documents.
Because we used a large web crawl as the source for our corpus, it contains documents from a variety of genres. To further analyze this property, we categorized a sample of 50 documents from the corpus. Among them, we found professionally written articles and blog posts (28%), educational material for parents and kids (26%), personal blog posts (16%), forum discussions and comments (12%), commented link collections (12%) and scientific articles (6%).
In addition to the variety of genres, the documents also differ in terms of language use. To capture this property, we follow Zopf.2016 and compute, for every topic, the average Jensen-Shannon divergence between the word distribution of one document and the word distribution in the remaining documents. The higher this value is, the more the language differs between documents. We found the average divergence over all topics to be 0.3490, whereas it is 0.3019 in DUC 2004 and 0.3188 in TAC 2008A.
The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token.
|
How many topics does the corpus contain?
|
30 different topics.
|
null | false
| null |
Provide a few different ideas for games for 4 adults to play.
|
There are a number of popular board games that would be suitable for four adult players. Catan is a popular strategy and resource board game for two to four players, Scrabble is a good option and poker is always a popular game for adults and only requires a pack of playing cards and either poker chips or objects that can be used in place of betting chips, such as buttons.
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|
null | false
| null |
Is a hotdog a sandwich?
|
That’s a tricky question because most people would consider a hotdog to not be a sandwich; however, if a sandwich is considered to be meat between two pieces of bread, then it is a sandwich. There are several counter points to this argument. A burrito is usually not considered a sandwich but is very similar to a hotdog. An ice cream sandwich is called a sandwich but has no meat.
|
|
null | false
| 27
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In most other analyses and datasets, the idea of out-of-scope data is not considered, and instead the output classes are intended to cover all possible queries (e.g., TREC BIBREF7). Recent work by BIBREF8 considers a similar problem they call out-of-distribution detection. They use other datasets or classes excluded during training to form the out-of-distribution samples. This means that the out-of-scope samples are from a small set of coherent classes that differ substantially from the in-distribution samples. Similar experiments were conducted for evaluating unknown intent discovery models in BIBREF9. In contrast, our out-of-scope queries cover a broad range of phenomena and are similar in style and often similar in topic to in-scope queries, representing things a user might say given partial knowledge of the capabilities of a system.
Table TABREF20 compares our dataset with other short-query intent classification datasets. The Snips BIBREF0 dataset and the dataset presented in BIBREF10 are the most similar to the in-scope part of our work, with the same type of conversational agent requests. Like our work, both of these datasets were bootstrapped using crowdsourcing. However, the Snips dataset has only a small number of intents and an enormous number of examples of each. Snips does present a low-data variation, with 70 training queries per intent, in which performance drops slightly. The dataset presented in BIBREF10 has a large number of intent classes, yet also contains a wide range of samples per intent class (ranging from 24 to 5,981 queries per intent, and so is not constrained in all cases).
BIBREF11 created datasets with constrained training data, but with very few intents, presenting a very different type of challenge. We also include the TREC query classification datasets BIBREF7, which have a large set of labels, but they describe the desired response type (e.g., distance, city, abbreviation) rather than the action intents we consider. Moreover, TREC contains only questions and no commands. Crucially, none of the other datasets summarized in Table TABREF20 offer a feasible way to evaluate out-of-scope performance.
The Dialog State Tracking Challenge (DSTC) datasets are another related resource. Specifically, DSTC 1 BIBREF12, DSTC 2 BIBREF13, and DSTC 3 BIBREF14 contain “chatbot style" queries, but the datasets are focused on state tracking. Moreover, most if not all queries in these datasets are in-scope. In contrast, the focus of our analysis is on both in- and out-of-scope queries that challenge a virtual assistant to determine whether it can provide an acceptable response.
In most other analyses and datasets, the idea of out-of-scope data is not considered, and instead the output classes are intended to cover all possible queries (e.g., TREC (Li and Roth, 2002)).
|
Has the TREC considered the idea of out-of-scope data?
|
No, it hasn't.
|
null | false
| null |
The name Tagore is the anglicised transliteration of Thakur. The original surname of the Tagores was Kushari. They were Pirali Brahmin ('Pirali’ historically carried a stigmatized and pejorative connotation) originally belonged to a village named Kush in the district named Burdwan in West Bengal. The biographer of Rabindranath Tagore, Prabhat Kumar Mukhopadhyaya wrote in the first volume of his book Rabindrajibani O Rabindra Sahitya Prabeshak that
The Kusharis were the descendants of Deen Kushari, the son of Bhatta Narayana; Deen was granted a village named Kush (in Burdwan zilla) by Maharaja Kshitisura, he became its chief and came to be known as Kushari.
Life and events
Early life: 1861–1878
Main article: Early life of Rabindranath Tagore
Young Tagore in London, 1879
The last two days a storm has been raging, similar to the description in my song—Jhauro jhauro borishe baridhara [... amidst it] a hapless, homeless man drenched from top to toe standing on the roof of his steamer [...] the last two days I have been singing this song over and over [...] as a result the pelting sound of the intense rain, the wail of the wind, the sound of the heaving Gorai River, [...] have assumed a fresh life and found a new language and I have felt like a major actor in this new musical drama unfolding before me.
— Letter to Indira Devi.
The youngest of 13 surviving children, Tagore (nicknamed "Rabi") was born on 7 May 1861 in the Jorasanko mansion in Calcutta, the son of Debendranath Tagore (1817–1905) and Sarada Devi (1830–1875).
Black-and-white photograph of a finely dressed man and woman: the man, smiling, stands with the hand on the hip and elbow turned outward with a shawl draped over his shoulders and in Bengali formal wear. In front of him, the woman, seated, is in elaborate dress and shawl; she leans against a carved table supporting a vase and flowing leaves.
Tagore and his wife Mrinalini Devi, 1883
Tagore was raised mostly by servants; his mother had died in his early childhood and his father travelled widely. The Tagore family was at the forefront of the Bengal renaissance. They hosted the publication of literary magazines; theatre and recitals of Bengali and Western classical music featured there regularly. Tagore's father invited several professional Dhrupad musicians to stay in the house and teach Indian classical music to the children. Tagore's oldest brother Dwijendranath was a philosopher and poet. Another brother, Satyendranath, was the first Indian appointed to the elite and formerly all-European Indian Civil Service. Yet another brother, Jyotirindranath, was a musician, composer, and playwright. His sister Swarnakumari became a novelist. Jyotirindranath's wife Kadambari Devi, slightly older than Tagore, was a dear friend and powerful influence. Her abrupt suicide in 1884, soon after he married, left him profoundly distraught for years.
|
From the passage provided, extract Tagore's nickname
|
Rabi
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Oakland population in 2022 is estimated to be 439,483
|
How many people live in the city of Oakland in 2022?
|
About 450,000 people live in Oakland, Ca during the last census
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null | false
| null |
What is the most famous sport in the world
|
Soccer is one of the most famous sports . With Fan base of ~4 Billion soccer is one support which can be considered to be most viewed sport in the the world.
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null | false
| null |
Douglas Irvin Pederson (born January 31, 1968) is an American football coach and former quarterback who is the head coach for the Jacksonville Jaguars of the National Football League (NFL). Pederson spent most of his 13-season playing career as a backup to Brett Favre on the Green Bay Packers, where he was part of the team that won a Super Bowl title in Super Bowl XXXI. He was also a backup to Dan Marino on the Miami Dolphins and a starter for the Philadelphia Eagles and Cleveland Browns until retiring in 2004.
Pederson began his coaching career under Andy Reid, serving as an assistant for the Eagles from 2009 to 2012. After Reid became the head coach of the Kansas City Chiefs in 2013, Pederson followed him to serve as the Chiefs' offensive coordinator. He returned to the Eagles as their head coach in 2016, a position he held for five seasons. His greatest success was when he led the franchise to its first Super Bowl title in 2017's Super Bowl LII, making him one of four individuals to win a Super Bowl as a player and head coach.
|
Retrieve the teams Doug Pederson was affiliated with as a coach
|
Jacksonville Jaguars, Philadelphia Eagles, Kansas City Chiefs
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| null |
How can UV light be used?
|
UV light is electromagnetic radiation. It has a wavelength that is shorter than regular visible light. UV light can be used in water purification to kill viruses and bacteria in city water. Continuous flow UV lights can kill virus and bacteria up to 99.999%. This is very helpful because it keeps people from ingesting virus and bacteria in their water source. UV light has other applications such as tanning beds. UV light can also be used in disinfecting services too.
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null | false
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Wilford Rex Black (January 31, 1920 – December 12, 2012) was an American politician.
Wilford Rex Black represented the 2nd Utah Senate District from 1973 to 1997. W. Rex Black, as he was often referred to, was a member of the Democratic party and represented the party as the Minority leader in the Utah Senate for many years.
|
What does W stand for in the name W. Rex Black?
|
Wilford
|
null | false
| null |
Identify which instrument is string or percussion: Boomwhacker, Ukelin
|
Boomwhacker is percussion, Ukelin is string.
|
|
null | false
| null |
Racial categories have historically been used as a way to enable an oppressive figure or group to discriminate against other groups or individuals which were seen as different from that of the oppressor. In nineteenth and early twentieth century Europe, artwork was a common form of racialization which targeted countries in the Middle East and Asia. The artwork, predominantly paintings, were portrayed in order to instill prejudice in the Western populations through sexualizing and manipulating images. One of the most prominent examples of Orientalist work in art is a piece by Eugène Delacroix titled Women of Algiers in their Apartment. Dating back to 1834, it portrays three women resting in a harem in exotic clothing while an African woman is dressed in plain clothing, depicting her role as a servant. Fine textiles, hookahs, and other paraphernalia adorn the room, which represents a European fantasy of an exotic scene. Attempts to portray these cultures as strange, foreign and exotic through Orientalism led to intolerance towards the Arab and Asian communities in Europe and the United States. Others argue that Delacroix, who travelled in North Africa sketching extensively, was depicting a realistic scene of the era based on his first-hand knowledge and experience. In such an interpretation the clothing, for example, is consistent with the times, as Arab North Africans dressed differently from Europeans, and kept black slaves who would not have been treated as equals.
|
How was art used for manipulative purpose in racial discuss in the 20th century?
|
In the 19th to 20th century, Europeans commonly use artworks to portray prejudice towards Middle Eastern and Asian people.
|
null | false
| null |
Why do humans play sports?
|
Humans play sports for a variety of reasons but one reason is to have fun. Playing sports is also a good form of exercise which helps humans stay in shape. Not everyone enjoys playing sports but for those that do they often enjoy competing against one another.
|
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null | false
| null |
How long is a chukker in Polo?
|
A chukker is 7.5 minutes
|
|
1905.08949
| false
| null |
Question generation is an NLG task for which the input has a wealth of possibilities depending on applications. While a host of input modalities have been considered in other NLG tasks, such as text summarization BIBREF24 , image captioning BIBREF25 and table-to-text generation BIBREF26 , traditional QG mainly focused on textual inputs, especially declarative sentences, explained by the original application domains of question answering and education, which also typically featured textual inputs.
Recently, with the growth of various QA applications such as Knowledge Base Question Answering (KBQA) BIBREF27 and Visual Question Answering (VQA) BIBREF28 , NQG research has also widened the spectrum of sources to include knowledge bases BIBREF29 and images BIBREF10 . This trend is also spurred by the remarkable success of neural models in feature representation, especially on image features BIBREF30 and knowledge representations BIBREF31 . We discuss adapting NQG models to other input modalities in Section "Wider Input Modalities" .
While a host of input modalities have been considered in other NLG tasks, such as text summarization BIBREF24 , image captioning BIBREF25 and table-to-text generation BIBREF26 , traditional QG mainly focused on textual inputs, especially declarative sentences, explained by the original application domains of question answering and education, which also typically featured textual inputs.
Recently, with the growth of various QA applications such as Knowledge Base Question Answering (KBQA) BIBREF27 and Visual Question Answering (VQA) BIBREF28 , NQG research has also widened the spectrum of sources to include knowledge bases BIBREF29 and images BIBREF10 .
|
What are all the input modalities considered in prior work in question generation?
|
Textual inputs, knowledge bases, and images.
|
null | false
| null |
what is the number of US house of representatives?
|
435
|
|
null | false
| null |
Classify the following as TV Shows or movies: Ted Lasso, Schitt's Creek, The Office, Seinfeld, The Elephant Whisperers, Harry Potter and the Sorcerer's Stone, Delhi Crime, The Social Network.
|
The classification in order is: 'show', 'show', 'show', 'show', 'movie', 'movie', 'show', 'movie'.
|
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null | false
| 189
|
Automatically predicting the level of English of non-native speakers from their written text is an interesting text mining task. Systems that perform well in the task can be useful components for online, second-language learning platforms as well as for organisations that tutor students for this purpose. In this paper I present the system balikasg that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems. In order to achieve the best performance in the challenge, I decided to use a variety of features that describe an essay's readability and syntactic complexity as well as its content. For the prediction step, I found Gradient Boosted Trees, whose efficiency is proven in several data science challenges, to be the most efficient across a variety of classifiers.
The rest of the paper is organized as follows: in Section 2 I frame the problem of language level as an ordinal classification problem and describe the available data. Section 3 presents the feature extaction and engineering techniques used. Section 4 describes the machine learning algorithms for prediction as well as the achieved results. Finally, Section 5 concludes with discussion and avenues for future research.
In this paper I present the system balikasg that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems.
|
What is the performance of the system balikasg?
|
It achieved the state-of-the-art performance in the language-level prediction task of CAp 2018 data science challenge among 14 systems.
|
null | false
| null |
A lead or A manager?
|
Both are importent,
1. A manager is important to keep your work organized and get the timeline done
2. A leader is needed to guide you technically
|
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null | false
| 183
|
Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
Even though machine translation has improved considerably with the advent of neural machine translation (NMT) (Sutskever et al., 2014; Bahdanau et al., 2015), the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
|
Why the translation of pronouns is hard?
|
Becasue they often require context outside the current sentence.
|
2001.00137
| true
| null |
Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\%$ to 8$\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\%$ against BERT's 72$\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\%$ accuracy against BERT's 76$\%$, an improvement of 6$\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\%$ for our model and 74$\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.
We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\%$ against BERT's 72$\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy.
|
Should their approach be applied only when dealing with incomplete data?
|
No.
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null | false
| null |
Give me a list of pros and cons of buying a bigger house
|
Buying a bigger house is a major life decision involving many factors. Pros include
- room for more people, especially helpful if you’re growing a family or you like to host
- room for gear such as exercise equipment or a spa
- bigger homes may include more land which can provide solitude and a larger area for various outdoor activities
Cons include
- bigger homes may be more expensive to purchase
- bigger homes may be more challenging to maintain and clean
|
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