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A championship belt is a large, extravagantly designed belt used primarily in combat sports such as boxing, mixed martial arts, and professional wrestling to signify the champions of the promotion or company, much like a cup or trophy in other sports. There are several companies in the business of constructing championship belts.
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Which sports offer championship belts to their winners?
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Championship belts are usually awarded in combat sports, which include boxing, mixed martial arts, and professional wrestling. The belts are used to designate the champions of the promotion or the company.
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null | false
| 506
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As mentioned that the hyper-parameter λ can balance the weight of personalization and generalization in several state-of-the-art PFL algorithms, we also conduct experiments to compare the performance of our CGPFL and CGPFL-Heur with a typical PFL algorithm, pFedMe (T Dinh et al., 2020), on different values of λ. Specifically, the range of λ is properly chosen to avoid that divergence occurs in pFedMe. The experimental results in Table show that our methods can constantly achieve better performance than pFedMe despite λ varies, which demonstrates that CGPFL can constantly reach better personalization-generalization trade-off against the state-of-the-art PFL methods.
As mentioned that the hyper-parameter λ can balance the weight of personalization and generalization in several state-of-the-art PFL algorithms (T Dinh et al., 2020; Hanzely et al., 2020; Li et al., 2021), we also conduct experiments to compare the performance of our CGPFL and CGPFL-Heur with a typical PFL algorithm, pFedMe (T Dinh et al., 2020), on different values of λ. Specifically, the range of λ is properly chosen to avoid that divergence occurs in pFedMe. The experimental results in Table 2 show that our methods can constantly achieve better performance than pFedMe despite λ varies, which demonstrates that CGPFL can constantly reach better personalization-generalization trade-off against the state-of-the-art PFL methods.
|
In Table 2, what if the hyper-parameter lambda varies exponentially?
|
As regard to the hyperparameter λ in Table 2, we explain in Section 5.4 that the range λ is properly chosen to avoid that divergence occurs in pFedMe. We find that when the other hyperparameters are fixed, both too small and too large λ can cause the divergence of pFedMe. So the chosen range is almost the largest one.
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null | false
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What does BMW stand for?
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Bayerische Motoren Werke
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Is plastic bad for us?
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Have you ever thought about where your plastic water bottles go? It probably ends up in a landfill, where it will decompose for over 1000 years as it deposits toxic chemicals. Plastic is an invention that has done more harm than good. There are about 363,762,732,605 pounds of plastic covering the earth. Most of this plastic can’t be recycled. Although people think it is too expensive or inconvenient to get rid of plastics, we should reduce the amount of plastic used or increase the price of plastics. One reason is that when plastic invades a balanced ecosystem, it corrupts it completely. The marine biome is one that is affected by this product. The animals in the ocean die of suffocation, ingestion of plastics, and entanglement. The animals affected are seagulls, whales, seals, fish, and turtles, and that's just a few. The animals eat so much plastic that they die of constipation from it. Another reason is the presence of microplastics in food and people. About 99% of fish have microplastics in their stomachs, livers, and filets (the meat of fish). Approximately 80% of cows have microplastics in their meat and milk. A good point is that mealworms and waxworms are studied to digest plastics, and they should be added to landfills to break down plastics. There are about 50,000 particles of plastic in an adult person, and this number is rising fast. Third, most plastic is littered. There are about 10 million tons of plastic litter every year. Every week, you ingest about a credit card's worth of plastic. There are about 44 million pounds of plastic that are produced that is very deadly to the environment."The plastic in the environment is in your water, food, and body. These plastics should not be there; they can lead to cancer, according to a recent study. We need to stop using plastic in our lives. Some ways we can reduce the amount of plastic on earth are by using sustainable materials such as wood, plant fibers, and mushroom styrofoam. We can also try to buy recyclable plastics that have a triangle formed by three cycling arrows. Use a tote bag instead of a plastic one, or a metal and glass straw instead of a plastic one. You can also use a metal cutlery kit instead of plastic. These are all ways you can make a difference in your everyday life.
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When the modern Olympics began in 1896, the initiators and organizers were looking for a great popularizing event, recalling the glory of ancient Greece. The idea of a marathon race came from Michel Bréal, who wanted the event to feature in the first modern Olympic Games in 1896 in Athens. This idea was heavily supported by Pierre de Coubertin, the founder of the modern Olympics, as well as by the Greeks. The Greeks staged a selection race for the Olympic marathon on 22 March 1896 (Gregorian) that was won by Charilaos Vasilakos in 3 hours and 18 minutes (with the future winner of the introductory Olympic Games marathon, Spyridon "Spyros" Louis, coming in fifth at a second race two weeks later). The winner of the first Olympic marathon, on 10 April 1896 (a male-only race), was Spyridon Louis, a Greek water-carrier, in 2 hours 58 minutes and 50 seconds. The marathon of the 2004 Summer Olympics was run on the traditional route from Marathon to Athens, ending at Panathinaiko Stadium, the venue for the 1896 Summer Olympics. That men's marathon was won by Italian Stefano Baldini in 2 hours 10 minutes and 55 seconds, a record time for this route until the non-Olympics Athens Classic Marathon of 2014, when Felix Kandie lowered the course record to 2 hours 10 minutes and 37 seconds.
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Given this paragraph about the history of the marathon, who won the first official olympic marathon.
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Spyridon Louis was the first winner of the Olympic Marathon on 10 April 1896.
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Tell me something about the Dog Man comic
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Dog Man is a comic book series authored by Dav Pilkey, the creator of Captain Underpants. The main character is a half-man half dog policeman. Together with a few good friends, including a cat kid, a robot, a journalist, and another policeman, they defeat various powerful villains collaboratively.
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Why do people travel to Puerto Rico?
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Puerto Rico is an island in the Caribbean. Many travelers enjoy visiting the beach to relax or participate in water sports like surfing or jet skiing. Some enjoy visiting El Yunque, the only tropical rainforest in the U.S. National Park system. Others like to see old colonial architecture, such as forts like El Morro.
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Summer is traditionally associated with hot or warm weather. In Mediterranean climates, it is also associated with dry weather, while in other places (particularly in Eastern Asia because of the monsoon) it is associated with rainy weather. The wet season is the main period of vegetation growth within the savanna climate regime. Where the wet season is associated with a seasonal shift in the prevailing winds, it is known as a monsoon.
In the northern Atlantic Ocean, a distinct tropical cyclone season occurs from 1 June to 30 November. The statistical peak of the Atlantic hurricane season is 10 September. The Northeast Pacific Ocean has a broader period of activity, but in a similar timeframe to the Atlantic. The Northwest Pacific sees tropical cyclones year-round, with a minimum in February and March and a peak in early September. In the North Indian basin, storms are most common from April to December, with peaks in May and November. In the Southern Hemisphere, the tropical cyclone season runs from the start of November until the end of April with peaks in mid-February to early March.
Thunderstorm season in the United States and Canada runs in the spring through summer but sometimes can run as late as October or even November in the fall. These storms can produce hail, strong winds and tornadoes, usually during the afternoon and evening.
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Based on the following paragraph, list the weather conditions associated with summer around the world.
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1. Summer is traditionally associated with hot or warm weather.
2. In Mediterranean climates, it is also associated with dry weather.
3. In Eastern Asia because of the monsoon, it is associated with rainy weather.
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In theoretical computer science, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that any distributed data store can provide only two of the following three guarantees:
Consistency
Every read receives the most recent write or an error.
Availability
Every request receives a (non-error) response, without the guarantee that it contains the most recent write.
Partition tolerance
The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes.
When a network partition failure happens, it must be decided whether to do one of the following:
cancel the operation and thus decrease the availability but ensure consistency
proceed with the operation and thus provide availability but risk inconsistency.
Thus, if there is a network partition, one has to choose between consistency or availability. Note that consistency as defined in the CAP theorem is quite different from the consistency guaranteed in ACID database transactions.
Eric Brewer argues that the often-used "two out of three" concept can be somewhat misleading because system designers need only to sacrifice consistency or availability in the presence of partitions, but that in many systems partitions are rare.
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What is CAP theorem in computer science?
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The CAP theorem, also named Brewer's theorem, states that distributed data stores can provide only two of three guarantees: consistency, availability, and partition tolerance. When a network partition fails, it must be decided whether to cancel the operation or proceed with the operation. Eric Brewer argues that the "two out of three" concept can be misleading.
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What are some iconic Renaissance period artists?
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The Renaissance period had a lot of iconic artists. The following are some of the most iconic:
- Donatello (1386-1466). Born in Florence, he studied classical sculpture and used this to develop a complete Renaissance style in sculpture.
- Sandro Boticelli (1445-1510). His paintings have been seen to represent the linear grace of late Italian Gothic and some Early Renaissance painting, even though they date from the latter half of the Italian Renaissance period.
- Leonardo da Vinci (1452-1519). He was an Italian polymath of the High Renaissance who was active as a painter, draughtsman, engineer, scientist, theorist, sculptor, and architect.
- Michelangelo (1475-1564). He was an Italian sculptor, painter, architect and poet of the High Renaissance.
- Raphael (1483-1520). Raphael was an Italian painter and architect of the High Renaissance.
- Caravaggio (1571-1610). Caravaggio was an Italian painter active in Rome for most of his artistic life.
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1603.01417
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We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.
In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 .
the new DMN+ model does not require that supporting facts
In addition, we introduce a new input module to represent images.
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What improvements they did for DMN?
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The answers are shown as follows:
* the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training.
* In addition, we introduce a new input module to represent images.
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In chess, why is the queen valued at nine points instead of eight?
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In chess, typically chess pieces are valued based on their importance.
For example,
Pawn - 1 point
Knight - 3 points
Bishop - 3 points
Rook - 5 points
Queen - 9 points
Since the queen can move like the bishop and the rook, simple math would suggest that it is valued at 8 points (3 + 5).
However, the game of chess, involves complex scenarios and strategies and having a single piece that can move in all directions gives it a distinct advantage during various phases of the chess game.
This is why the queen is given a higher point valuation.
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The Adobe World Headquarters is the corporate headquarters of Adobe Systems, located in San Jose, California.
The buildings are known for their green design. The West Tower is listed as an Energy Star labeled building by the U.S. Environmental Protection Agency. In 2006, all three towers were awarded Leadership in Energy and Environmental Design (LEED) Platinum certification by the United States Green Building Council for environmental sustainability.
|
Where is the corporate headquarters of Adobe Systems?
|
San Jose, California
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What was Selective Service Act?
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The Selective Service Act was passed in 1917 (May). It enabled the country to enlist more men to the military
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What is a non fungible token?
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A non fungible token (NFT) is another form of digital asset. This asset can be exchanged with other NFT token that are similar. Similar NFTs could be art, music, or photography. The idea is that you can monetize your craft by putting it into a block chain. This ensure that the original author and original say art is one of a kind. NFT art can be worth a lot of money and usually the intrinsic value of NFT art is tied to the popularity of the artist who produces it and how well known they are.
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What is an Enzyme inhibitor
|
An enzyme inhibitor is a molecule that binds to an enzyme and blocks its activity. Enzymes are proteins that speed up chemical reactions necessary for life, in which substrate molecules are converted into products. An enzyme facilitates a specific chemical reaction by binding the substrate to its active site, a specialized area on the enzyme that accelerates the most difficult step of the reaction.
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What should I do if I'm feeling depressed?
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When you're feeling depressed, it's important to communicate your feelings to the outside world instead of keeping everything to yourself. You can talk to friends, family, or a therapist. Even if you don't have someone to talk to or are not ready to share your feelings, you can seek answers from books, online forums, etc. The important thing is to keep an open mind and be able to listen to or accept other perspectives, instead of being stuck in your own mindset and not willing to hear other opinions.
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How are odds different from probability?
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Both are measurements for how likely it is for an event to occur. Odds are commonly used in gambling, which probability is a measure that is commonly used in mathematics/statistics. In statistics, probability measures the number of outcomes in which a particular event occurred divided by the total number of possible outcomes. For example, the probability of rain on a given day would be based on, historically with similar conditions, the number of rainy days divided by a total number of days. The odds are measured as the number of events divided by the number of non-events. To contrast, probability would be the number of events divided by the sum of events and non-events.
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Identify each of the following whether it is a type of food: noodle, grass, pizza, tree, rice, rose, steak.
|
noodle: Yes
grass: No
pizza: Yes,
tree: No
rice: Yes
rose: No
steak: Yes
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| 98
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Support Vector Machines (SVM) is a supervised machine learning method which considers dataset as a set of vectors and tries to classify them into specific classes. Basically, SVM is a binary classifier. however, most classification tasks are multi-class classifiers. When applying SVMs, the method has been extended to classify three or more classes. Particular NLP tasks, like word segmentation and Part-of-speech task, each token/word in documents will be used as a feature vector. For the word segmentation task, each token and its features are considered as a vector for the whole document, and the SVM model will classify this vector into one of the three tags (B-IO).
This technique is applied for Vietnamese word segmentation in several studies BIBREF7 , BIBREF24 . Nguyen et al. applied on a segmented corpus of 8,000 sentences and got the result at 94.05% while Ngo et al. used it with 45,531 segmented sentences and get the result at 97.2%. It is worth to mention that general SVM libraries (such as LIBSVM, LIBLINEAR, SVMlight, Node-SVM, and TreeSVM ), YamCha is an opened source SVM library that serves several NLP tasks: POS tagging, Named Entity Recognition, base NP chunking, Text Chunking, Text Classification and event Word Segmentation.
It is worth to mention that general SVM libraries (such as LIBSVM4, LIBLINEAR5, SVMlight6, Node-SVM7, and TreeSVM8 ), YamCha9 is an opened source SVM library that serves several NLP tasks: POS tagging, Named Entity Recognition, base NP chunking, Text Chunking, Text Classification and event Word Segmentation.
|
Is YamCha9 an opened source SVM library?
|
Yes.
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Why is March Madness so popular in the United States?
|
March Madness is an annual college basketball tournament played in March. It consists of 68 teams that play in a single elimination tournament until a winner is determined. There are both men's and women's tournaments, though the men's tournament has been taking place for longer, since 1939. Interestingly, the first tournament had only 8 teams.
The tournament is popular with basketball fans and non-fans alike. Some people enjoy the tournament because their college or university's team is participating in the tournament. Other fans enjoy the unpredictability of the results and cheer for a team that they adopt for the tournament. Many people enjoy rooting for an underdog; a team that is ranked lower overall but can gain a lot of attention by winning against teams ranked higher. This is one reason it is called 'March Madness.'
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| 262
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Training Language Models (LMs) is a straightforward way to collect a set of rules by utilizing the fact that words do not appear in an arbitrary order; we in fact can gain useful information about a word by knowing the company it keeps BIBREF7 . A statistical language model estimates the probability of a sequence of words or an upcoming word. An N-gram is a contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence. For example, in the tweet
tears in Ramen #SingleLifeIn3Words
“tears”, “in”, “Ramen” and “#SingleLifeIn3Words” are unigrams; “tears in”, “in Ramen” and “Ramen #SingleLifeIn3Words” are bigrams and “tears in Ramen” and “in Ramen #SingleLifeIn3Words” are trigrams.
An N-gram model can predict the next word from a sequence of N-1 previous words. A trigram Language Model (LM) predicts the conditional probability of the next word using the following approximation: DISPLAYFORM0
The assumption that the probability of a word depends only on a small number of previous words is called a Markov assumption BIBREF8 . Given this assumption the probability of a sentence can be estimated as follows: DISPLAYFORM0
In a study on how phrasing affects memorability, BIBREF9 take a language model approach to measure the distinctiveness of memorable movie quotes. They do this by evaluating a quote with respect to a “common language” model built from the newswire sections of the Brown corpus BIBREF10 . They find that movie quotes which are less like “common language” are more distinctive and therefore more memorable. The intuition behind our approach is that humor should in some way be memorable or distinct, and so tweets that diverge from a “common language” model would be expected to be funnier.
In order to evaluate how funny a tweet is, we train language models on two datasets: the tweet data and the news data. Tweets that are more probable according to the tweet data language model are ranked as being funnier. However, tweets that have a lower probability according to the news language model are considered the funnier since they are the least like the (unfunny) news corpus. We relied on both bigrams and trigrams when training our models.
We use KenLM BIBREF11 as our language modeling tool. Language models are estimated using modified Kneser-Ney smoothing without pruning. KenLM also implements a back-off technique so if an N-gram is not found, KenLM applies the lower order N-gram's probability along with its back-off weights.
An N-gram is a contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence.
|
What's the meaning of N-gram?
|
A contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence.
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1909.06434
| false
| null |
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. Words are split into subwords units with a joint vocabulary of 32K tokens. BLEU scores are computed on the tokenized output with multi-bleu.perl from Moses BIBREF10.
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.
|
Are this techniques used in training multilingual models, on what languages?
|
English to French and English to German
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null | false
| 339
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For an ever increasing spectrum of applications (e.g., medical text analysis, opinion mining, sentiment analysis, social media text analysis, customer intelligence, fraud analytics etc.) mining and analysis of unstructured natural language text data is necessary BIBREF0, BIBREF1, BIBREF2.
One of key challenge while designing such text analytics (TA) applications is to identify right set of features. For example, for text classification problem, different sets of features have been considered in different works (spanning a history of more than twenty years) including `bag of words', `bag of phrases', `bag of n-grams', `WordNet based word generalizations', and `word embeddings' BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. Even for recent end-to-end designs using deep neural networks, specification of core features remains manually driven BIBREF8, BIBREF9. During feature engineering, often data scientists manually determine which features to use based upon their experience and expertise with respect to the underlying application domain as well as state-of-the-art tools and techniques. Different tools (e.g., NLTK BIBREF10, Mallet BIBREF11, Stanford CoreNLP BIBREF12, Apache OpenNLP BIBREF13, Apache Lucene BIBREF14, etc.) available to a NLP data scientist for TA application design and development often differ in terms of support towards extraction of features, level of granularity at which feature extraction process is to be specified; and these tools often use different programing vocabularies to specify semantically equivalent features.
Currently, there is no generic method or approach, which can be applied during TA application's design process to define and extract features for any arbitrary application in an automated or semi-automated manner. Even there is no single way to express wide range of NLP features, resulting into increased efforts during feature engineering which has to start new for each data scientist and automated reuse of features across semantically similar or related applications designed by different data scientists is difficult. This also hinders foundational studies on NLP feature engineering including why certain features are more critical than others.
In this paper, we aim to present an approach towards automating NLP feature engineering. We start with an outline of a language for expressing NLP features abstracting over the feature extraction process, which often implicitly captures intent of the NLP data scientist to extract specific features from given input text. We next discuss a method to enable automated reuse of features across semantically related applications when a corpus of feature specifications for related applications is available. Proposed language and system would help achieving reduction in manual effort towards design and extraction of features, would ensure standardization in feature specification process, and could enable effective reuse of features across similar and/or related applications.
Currently, there is no generic method or approach, which can be applied during TA application’s design process to define and extract features for any arbitrary application in an automated or semi-automated manner.
|
Is there a generic method or approach, which can be applied during the TA application's design process to define and extract features for any arbitrary application in an automated or semi-automated manner?
|
No, there isn't.
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null | false
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Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas.
|
Instead of jelly, try one of the following with peanut butter in a sandwich:
1. Honey
2. Raisins
3. Fruit preserves
4. Bananas
5. Sliced apples
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null | false
| 359
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Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. Recently, they have gained tremendous popularity in Natural Language Processing (NLP) and have been used in tasks as diverse as text similarity BIBREF0 , part-of-speech tagging BIBREF1 , sentiment analysis BIBREF2 , and machine translation BIBREF3 . Although word embeddings are widely used across NLP, their stability has not yet been fully evaluated and understood. In this paper, we explore the factors that play a role in the stability of word embeddings, including properties of the data, properties of the algorithm, and properties of the words. We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
Using the overlap between nearest neighbors in an embedding space as a measure of stability (see sec:definingStability below for more information), we observe that many common embedding spaces have large amounts of instability. For example, Figure FIGREF1 shows the instability of the embeddings obtained by training word2vec on the Penn Treebank (PTB) BIBREF4 . As expected, lower frequency words have lower stability and higher frequency words have higher stability. What is surprising however about this graph is the medium-frequency words, which show huge variance in stability. This cannot be explained by frequency, so there must be other factors contributing to their instability.
In the following experiments, we explore which factors affect stability, as well as how this stability affects downstream tasks that word embeddings are commonly used for. To our knowledge, this is the first study comprehensively examining the factors behind instability.
We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
|
How about the instability of the word embeddings they find?
|
Word embeddings exhibit substantial instabilities.
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The final of Duleep Trophy was held in
|
SNR College, Coimbatore, India
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null | false
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What are some cardio machines at the gym?
|
Gyms often had a variety of cardio machines, including the treadmill, elliptical, rowing machine, stair climber, and spin bike.
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C# (pronounced C sharp) is a general-purpose high-level programming language supporting multiple paradigms. C# encompasses static typing, strong typing, lexically scoped, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines.
The C# programming language was designed by Anders Hejlsberg from Microsoft in 2000 and was later approved as an international standard by Ecma (ECMA-334) in 2002 and ISO/IEC (ISO/IEC 23270) in 2003. Microsoft introduced C# along with .NET Framework and Visual Studio, both of which were closed-source. At the time, Microsoft had no open-source products. Four years later, in 2004, a free and open-source project called Mono began, providing a cross-platform compiler and runtime environment for the C# programming language. A decade later, Microsoft released Visual Studio Code (code editor), Roslyn (compiler), and the unified .NET platform (software framework), all of which support C# and are free, open-source, and cross-platform. Mono also joined Microsoft but was not merged into .NET.
As of November 2022, the most recent stable version of the language is C# 11.0, which was released in 2022 in .NET 7.0.
|
From the passage provided, extract the most recent stable version of C#
|
As of November 2022, the most recent stable version of the language is C# 11.0
|
1612.09113
| false
| null |
In our model we represent linguistically motivated hierarchies in a multi-task Bi-Directional Recurrent Neural Network where junior tasks in the hierarchy are supervised at lower layers.This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags. In addition to sogaard2016deep.
This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags.
|
What is the unsupervised task in the final layer?
|
The answers are shown as follows:
* Language Modeling
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null | false
| 485
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Learning efficient representations that generalize well is a long standing problem of machine learning, and particularly of deep learning. Algorithms that exploit structure in the real world through effective inductive biases are key to solving this problem. Several inductive biases have been successfully used in the past, from early work using translational invariance for developing, to recent data augmentations for developing multiple self-supervised algorithms. A key inductive bias in multiple works is sparsity of concepts. Essentially, it refers to how objects in the real world tend to interact and affect the dynamics of other objects only in a small neighbourhood. Consider for example picking up a pen. This simple task only changes the dynamics of a very small part of the world, namely the pen and the hand of the person grasping it. Exploiting such structure allows learning representations that can generalize better in the real world.
In this paper, we develop a method that exploits sparsity by learning minimal representations. The well known saying 'neurons that fire together wire together' points to the phenomenon that that neurons that have similar output for a given input also have strong weights between them. Ideally, given the same set of neurons to model certain concepts, we would want neurons that relate to a particular concept to have strengthened weights while those relating to another concept to have weakened weights. Therefore, low correlation in the outputs of the representation would lead to less redundancy in modelling concepts and thus better generalization. This is precisely the motivation behind minimality leading to sparse representations and improved generalization. Minimal representations refer to ones that have sufficient but minimal information w.r.t. the task at hand. Therefore, it must be only possible to solve the task in hand and no other task, thus leading to better generalization for the given task. Another viewpoint is that minimal representations also lead to a reduction in spurious correlations. Since there is minimal noise present in the representation to cause spurious correlations, this again results in better generalization.
Faster LR Standard LR Slower LR Figure: Model Invariance (MODINV) A common representation f θ supports multiple predictor heads, each with a different learning rate. Each head is optimized based on the task objective (e.g., SSL loss) and captures different spurious correlations. As diversification in learning of predictor heads increases, the common representation must be optimal to reduce the loss for all predictors. 'Red' denotes the head being trained for the current sample.
Our approach, which we call model invariance (MODINV), learns minimal representations by incorporating multiple predictors over a single, common representation for a given task (see Figure). Each predictor head is then trained independently (a given sample only trains one head) and with a learning rate different to other heads. Theoretically, this can be looked at as a manifestation of the information bottleneck principle, which balances between sufficiency and minimality. Intuitively, this can be looked at as providing robustness to spurious correlations. Since each head learns a different prediction (based on its own optimization and learning on different parts of data set), each head is susceptible to different spurious correlations. However, since there is only one common representation to support all the predictor heads, this representation must be robust to any spurious correlations as training progresses.
We evaluate our method on two separate settings; 1) Reinforcement Learning: The DeepMind Control Suite(DMC) benchmark with distractors, which involves natural video playing in the background while the agent is controlled in the foreground and 2) Vision: The CIFAR-10 ( datasets, where no labels are used for learning the representations, followed by the standard linear probing evaluation protocol to test generalization.
We introduce MODINV, a simple method motivated by incorporating sparsity in representations, while showing connections to the information bottleneck principle. MODINV is conceptually simple, easy to implement, and improves representation learning performance in both reinforcement learning (from pixels) and vision settings. In reinforcement learning, MODINV achieves stronger than state-of-the-art performance on the DMC Suite with distractors benchmark, both with augmentations and without. In vision, MODINV leads to better performance than the baseline method of SIMSIAM when using a linear predictor on both CIFAR-10 and STL-10 datasets.
Our approach, which we call model invariance (MODINV), learns minimal representations by incorporating multiple predictors over a single, common representation for a given task (see Figure 1). Each predictor head is then trained independently (a given sample only trains one head) and with a learning rate different to other heads. Theoretically, this can be looked at as a manifestation of the information bottleneck principle, which balances between sufficiency and minimality
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In Section 6.1, the authors suggest that ModInv is similar to DIB in terms of instantiation. It’s unclear to me how that’s true. Can the authors comment on this?
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**DIB** uses the information bottleneck principle but for a particular family of predictors only. Here, we use the same idea (same architecture for all predictors) and thus it is closely related to DIB. Note that DIB was using fake labels and reversing gradients for the K predictors so as to enforce minimality. However, that creates a min-max optimization problem which could be hard to solve in practice. We don’t face that issue but of course need to pay in terms of more effort to make the predictors as diverse as possible.
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The Space Needle is an observation tower in Seattle, Washington, United States. Considered to be an icon of the city, it has been designated a Seattle landmark. Located in the Lower Queen Anne neighborhood, it was built in the Seattle Center for the 1962 World's Fair, which drew over 2.3 million visitors.
The Space Needle was once the tallest structure west of the Mississippi River, standing at 605 ft (184 m). The tower is 138 ft (42 m) wide, weighs 9,550 short tons (8,660 metric tons), and is built to withstand winds of up to 200 mph (320 km/h) and earthquakes of up to 9.0 magnitude, as strong as the 1700 Cascadia earthquake.
The Space Needle features an observation deck 520 ft (160 m) above ground, providing views of the downtown Seattle skyline, the Olympic and Cascade Mountains, Mount Rainier, Mount Baker, Elliott Bay, and various islands in Puget Sound. Visitors can reach the top of the Space Needle by elevators, which take 41 seconds. On April 19, 1999, the city's Landmarks Preservation Board designated the tower a historic landmark.
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From the paragraph about Space Needle, tell me where is located, when it was built, how tall the monument is and one more tourist fact about it.
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The Space Needle was built in 1962 and it is located in Lower Queen Anne area in the city of Seattle. The tower has a height of 605 feet and it takes 41 seconds for visitors to reach the top of it with an elevator.
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Tell me whether these are European countries or Asian countries: Spain, France, Norway, the Czech Republic, Denmark, Portugal, Brazil, South Africa, Canada, China, Japan, Malaysia, Singapore, Sri Lanka, The Philippines
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Not all of them are European countries or Asian countries! Brazil, South Africa, Canada are not part of Europe or Asia.
These are the European countries: Spain, France, Norway, the Czech Republic, Denmark, Portugal
And these are Asian countries: China, Japan, Malaysia, Singapore, Sri Lanka, The Philippines
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1610.00879
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FLOAT SELECTED: Table 5: Performance of human evaluators and our classifiers (trained on all features), for Dataset-H as the test set
FLOAT SELECTED: Table 5: Performance of human evaluators and our classifiers (trained on all features), for Dataset-H as the test set
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What baseline model is used?
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Human evaluators
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Harry Potter is a series of seven fantasy novels written by British author J. K. Rowling. The novels chronicle the lives of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley, all of whom are students at Hogwarts School of Witchcraft and Wizardry. The main story arc concerns Harry's conflict with Lord Voldemort, a dark wizard who intends to become immortal, overthrow the wizard governing body known as the Ministry of Magic and subjugate all wizards and Muggles (non-magical people).
The series was originally published in English by Bloomsbury in the United Kingdom and Scholastic Press in the United States. All versions around the world are printed by Grafica Veneta in Italy. A series of many genres, including fantasy, drama, coming-of-age fiction, and the British school story (which includes elements of mystery, thriller, adventure, horror, and romance), the world of Harry Potter explores numerous themes and includes many cultural meanings and references. According to Rowling, the main theme is death. Other major themes in the series include prejudice, corruption, and madness.
Since the release of the first novel, Harry Potter and the Philosopher's Stone, on 26 June 1997, the books have found immense popularity, positive reviews, and commercial success worldwide. They have attracted a wide adult audience as well as younger readers and are widely considered cornerstones of modern literature. As of February 2023, the books have sold more than 600 million copies worldwide, making them the best-selling book series in history, and have been available in 85 languages. The last four books consecutively set records as the fastest-selling books in history, with the final instalment selling roughly 2.7 million copies in the United Kingdom and 8.3 million copies in the United States within twenty-four hours of its release.
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From the passage note down the various themes on which Harry Potter series is based upon. List the results in comma separated format.
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death, prejudice, corruption, madness
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In this section, we present the results of out models for two zero-shot cross lingual transfer tasks: XNLI and dependency parsing.
Our approach shows the accuracy gains when adapting from a better pretrained model.
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When does the authors' approach show the accuracy gains?
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When adapting from a better pretrained model.
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How do you generate power in a roundhouse kick in Muay Thai?
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The power comes from a coordinated full body motion. You pivot on the ball of your foot, turn your hips and torso, and throw your leg forward in a sideways motion resembling the swinging of a baseball bat.
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1705.00108
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We evaluate our approach on two well benchmarked sequence tagging tasks, the CoNLL 2003 NER task BIBREF13 and the CoNLL 2000 Chunking task BIBREF14 . We report the official evaluation metric (micro-averaged INLINEFORM0 ). In both cases, we use the BIOES labeling scheme for the output tags, following previous work which showed it outperforms other options BIBREF15 . Following BIBREF8 , we use the Senna word embeddings BIBREF2 and pre-processed the text by lowercasing all tokens and replacing all digits with 0.
We evaluate our approach on two well benchmarked sequence tagging tasks, the CoNLL 2003 NER task BIBREF13 and the CoNLL 2000 Chunking task BIBREF14 .
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what are the evaluation datasets?
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The answers are shown as follows:
* CoNLL 2003
* CoNLL 2000
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Does platonic love hurt?
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Friendship breakups truly are not talked about enough. I feel like there's plenty of advice for how to deal with exes, but nearly none for how to recover from a very important friendship in your life ending. It hurts like hell and if you were really attached to the person, it hurts just as much--if not even more. Platonic love is no less profound than romantic love.
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The need to access and digest large amounts of textual data has provided strong impetus to develop automatic summarization systems aiming to create shorter versions of one or more documents, whilst preserving their information content. Much effort in automatic summarization has been devoted to sentence extraction, where a summary is created by identifying and subsequently concatenating the most salient text units in a document.
Most extractive methods to date identify sentences based on human-engineered features. These include surface features such as sentence position and length BIBREF0 , the words in the title, the presence of proper nouns, content features such as word frequency BIBREF1 , and event features such as action nouns BIBREF2 . Sentences are typically assigned a score indicating the strength of presence of these features. Several methods have been used in order to select the summary sentences ranging from binary classifiers BIBREF3 , to hidden Markov models BIBREF4 , graph-based algorithms BIBREF5 , BIBREF6 , and integer linear programming BIBREF7 .
In this work we propose a data-driven approach to summarization based on neural networks and continuous sentence features. There has been a surge of interest recently in repurposing sequence transduction neural network architectures for NLP tasks such as machine translation BIBREF8 , question answering BIBREF9 , and sentence compression BIBREF10 . Central to these approaches is an encoder-decoder architecture modeled by recurrent neural networks. The encoder reads the source sequence into a list of continuous-space representations from which the decoder generates the target sequence. An attention mechanism BIBREF11 is often used to locate the region of focus during decoding.
We develop a general framework for single-document summarization which can be used to extract sentences or words. Our model includes a neural network-based hierarchical document reader or encoder and an attention-based content extractor. The role of the reader is to derive the meaning representation of a document based on its sentences and their constituent words. Our models adopt a variant of neural attention to extract sentences or words. Contrary to previous work where attention is an intermediate step used to blend hidden units of an encoder to a vector propagating additional information to the decoder, our model applies attention directly to select sentences or words of the input document as the output summary. Similar neural attention architectures have been previously used for geometry reasoning BIBREF12 , under the name Pointer Networks.
One stumbling block to applying neural network models to extractive summarization is the lack of training data, i.e., documents with sentences (and words) labeled as summary-worthy. Inspired by previous work on summarization BIBREF7 , BIBREF13 and reading comprehension BIBREF9 we retrieve hundreds of thousands of news articles and corresponding highlights from the DailyMail website. Highlights usually appear as bullet points giving a brief overview of the information contained in the article (see Figure 1 for an example). Using a number of transformation and scoring algorithms, we are able to match highlights to document content and construct two large scale training datasets, one for sentence extraction and the other for word extraction. Previous approaches have used small scale training data in the range of a few hundred examples.
Our work touches on several strands of research within summarization and neural sequence modeling. The idea of creating a summary by extracting words from the source document was pioneered in bankoetal00 who view summarization as a problem analogous to statistical machine translation and generate headlines using statistical models for selecting and ordering the summary words. Our word-based model is similar in spirit, however, it operates over continuous representations, produces multi-sentence output, and jointly selects summary words and organizes them into sentences. A few recent studies BIBREF14 , BIBREF15 perform sentence extraction based on pre-trained sentence embeddings following an unsupervised optimization paradigm. Our work also uses continuous representations to express the meaning of sentences and documents, but importantly employs neural networks more directly to perform the actual summarization task.
rush2015neural propose a neural attention model for abstractive sentence compression which is trained on pairs of headlines and first sentences in an article. In contrast, our model summarizes documents rather than individual sentences, producing multi-sentential discourse. A major architectural difference is that our decoder selects output symbols from the document of interest rather than the entire vocabulary. This effectively helps us sidestep the difficulty of searching for the next output symbol under a large vocabulary, with low-frequency words and named entities whose representations can be challenging to learn. Gu:ea:16 and gulcehre2016pointing propose a similar “copy” mechanism in sentence compression and other tasks; their model can accommodate both generation and extraction by selecting which sub-sequences in the input sequence to copy in the output.
We evaluate our models both automatically (in terms of Rouge) and by humans on two datasets: the benchmark DUC 2002 document summarization corpus and our own DailyMail news highlights corpus. Experimental results show that our summarizers achieve performance comparable to state-of-the-art systems employing hand-engineered features and sophisticated linguistic constraints.
rush2015neural propose a neural attention model for abstractive sentence compression which is trained on pairs of headlines and first sentences in an article. In contrast, our model summarizes documents rather than individual sentences, producing multi-sentential discourse.
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What model do they propose for abstractive sentence compression?
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A neural attention model.
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1908.06606
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Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.
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How big is dataset of pathology reports collected from Ruijing Hospital?
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The answers are shown as follows:
* 17,833 sentences, 826,987 characters and 2,714 question-answer pairs
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To illustrate that semantically different yet related applications may have significant potential for reuse of features, let us consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.
Towards that, we analysed published works on three different types of events in different domains as described next:
Objective of this study is to design a ML model for identifying if there exist mentions of one of the nine types of bio-molecular interactions in (publicly available) Biomedical data. To train SVM based classifier, authors use GENETAG database, which is a tagged corpus for gene/protein named entity recognition. BioNLP 2009 shared task test-set was used to estimate performance of the system. Further details can be found at BIBREF22.
Objective of the study was to design ML model for enabling automated detection of specific financial events in the news text. Ten different types of financial events were considered including announcements regarding CEOs, presidents, products, competitors, partners, subsidiaries, share values, revenues, profits, and losses. To train and test SVM and CRF based ML models, authors used data set consisting of 200 news messages extracted from the Yahoo! Business and Technology newsfeeds, having financial events and relations manually annotated by 3 domain experts. Further details can be found at BIBREF23.
Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data. 38 different types of events were considered for designing the system. To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets. Further details can be found at BIBREF24.
Table TABREF21 below depicts classes of features selected by authors of these works (as described in the corresponding references above) to highlight the point that despite domain differences, these applications share similar sets of features. Since authors of these works did not cite each other, it is possible that that these features might have been identified independently. This, in turn, supports the hypothesis that if adequate details of any one or two of these applications are fed to a system described in this work, which is designed to estimate semantic similarities across applications, system can automatically suggest potential features for consideration for the remaining applications to start with without requiring manual knowledge of the semantically related applications.
To train SVM based classifier, authors use GENETAG database, which is a tagged corpus for gene/protein named entity recognition.
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What is the GENETAG database?
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It is a tagged corpus for gene/protein named entity recognition.
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In the Impeachment proceeding against Donald Trump, the motion was passed by?
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229 votes against 197 in the House of Representatives
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Does Delta Lake support writes or reads using the Spark Streaming DStream API?
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Delta does not support the DStream API. We recommend Table streaming reads and writes.
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Who is the best shooter in the NBA?
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Jose Harris has the highest 3-pointers percentage, but Stephen Curry is THE best shooter in the NBA all time. He broke NBA 3-pointers field goals record in 2022.
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Which of the following is a database or Streaming solution? Oracle, Kinesis, Event hub, SQL Server
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Oracle- Database, Kinesis- Streaming solution, Event hub- Streaming solution, SQL Server- Database
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What is the largest amount of money in coins without being able to make change for a dollar?
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To make change for a dollar, you would need 4 quarters or 10 dimes or 20 nickels or 100 pennies or a combination of those that add up to a dollar. The largest amount of money in coins without being able to make change for a dollar would be $1.19. If you have 3 quarters, 4 dimes, and 4 pennies, you would have $1.19, but you wouldn't be able to combine those to an even one dollar.
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He was born at Queenstown, County Cork. He was the third child of Bob Stawell Ball, a customs official, and Mary Ball (née Green). The Ball family lived in Youghal, County Cork. Robert had two sisters who shared his interest in nature, Anne, a well-known phycologist, and Mary, an entomologist. He also had a brother, Bent (7 March 1806 - 19 May 1860), who did not appear to pursue any interest in these fields. He initially attended a school Clonakilty, before attending a Quaker school in Ballitore, County Kildare where his interest for natural history was developed with encouragement from his schoolmaster James White. He returned to Youghal in 1824 to take up a post as a magistrate which involved travel and was sometimes dangerous, even escaping an assassination attempt, but aided his natural history specimen collecting. He left Youghal for Dublin in 1827, and as he was unable to afford medical studies he worked for 20 years in the civil service, firstly as clerk in the Constabulary and Yeomanry Office, Dublin, and later assistant librarian and keeper of records at the same. He considered the work as "soul-subduing slavery" and pursued his natural history interests, actively associating with the local scientific circles. Ball left the civil service in 1852 with a small pension, as it was deemed he spent too much of his time on scientific pursuits than was suitable for a public servant. He went on natural history excursions with William Todhunter, William Thompson, Robert Patterson, George Hyndman, and Edward Forbes apart from travels to museums in Paris and meetings in Great Britain.
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Extract all the names of people from this article about Robert Ball.
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Bob Stawell Ball, Mary Ball, Robert Ball, Anne Ball, Mary Ball, Bent Ball, James White, William Todhunter, William Thompson, Robert Patterson, George Hyndman, Edward Forbes
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"Holding Out for a Hero" (often erroneously titled I Need a Hero) is a song recorded by Welsh singer Bonnie Tyler for the soundtrack to the 1984 film Footloose. It later featured on her sixth studio album, Secret Dreams and Forbidden Fire (1986). The track was produced by Jim Steinman, who co-wrote the song with Dean Pitchford and was a Top 40 hit in several European countries, as well as Canada and the United States. Its 1985 re-release in the United Kingdom reached number two (remaining there for three weeks) and topped the singles chart in Ireland.
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After the song "Holding Out for a Hero" appeared in Footloose, what album did it appear in?
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Secret Dreams and Forbidden Fire
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Which cities should a perfect trip to Germany include.
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The perfect trip to Germany includes three major cities. If you plan to travel to Germany via plane, it is best to fly into either Berlin, Munich, or Frankfurt airport. These are the biggest airports in the country. From there, you can either travel around by high-speed train or you can take a car and drive on the famous German 'Autobahn.' If you want to experience driving without a speed limit, I would recommend the latter. A perfect trip to Germany must include visiting Munich, Berlin, and Hamburg. All three cities are amongst the largest cities and Germany, with over 1 million people living there, but have each unique cultural as well as historical characteristics. Berlin's present is still influenced by its division into East and West Berlin during the cold war. The city with its two centers is a world city of culture, politics, media, and science. Berlin offers great nightlife, world-class museums, and a lot of sights of historic relevance. Hamburg is known for its port, playing a historic role in trade as a member of the medieval Hanseatic League. The city lies on the River Elbe, and its many canals are crossed by around 2,500 bridges, making it the city with the highest number of bridges in Europe. Munich is the last city you must include on your trip. The city is the largest city in the German state of Bavaria and is well known for its world famous October Fest. Munich is often ranked as one of the most liveable cities in Germany, and attracts visitors with its food culture, parks, and is proximity to the German Alps.
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Categorize the below names into fishes and insects.
salmon, ant, bee, rohu, pike, wasp, beetle, carp, butterfly, cod, moth
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Insects - ant, bee, wasp, beetle, butterfly, moth
Fish - salmon, rohu, carp, pike, cod
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In our notation, a sentence of length $l$ tokens is indicated as ${\rm \textbf {X}}=\lbrace x_1, x_2, ... ,x_l\rbrace $. Each token is concatenated by word embeddings and position embeddings. Word embeddings $w_i$ of token $x_i$ are a $d_w$-dimensional vector obtained by pre-trained Word2Vec model BIBREF30, i.e., $w_i \in \mathbb {R}^{d_w}$. Position embeddings refer to vectorization representations of position information of words in a sentence. We employ one-hot encoding to represent position embeddings $p_i$ of token $x_i$, where $p_i \in \mathbb {R}^{d_p}$, $d_p$ is the positional embedding dimension. Therefore, the embeddings of a sentence are represented as $ {\rm \textbf {E}}=\lbrace [w_1;p_1 ], [w_2;p_2], ..., [w_l;p_l]\rbrace , {\rm \textbf {E}}\in \mathbb {R}^{l \times (d_p+d_w)}$. In particular, we adopt one-hot encoding to embed positions of tokens, rather than sinusoidal position encoding recommended in BERT model BIBREF31. The reason is that our experiments show that compared with one-hot encoding, sinusoidal position encoding not only increases the complexity of models but also performs poorly on relatively small datasets.
In particular, we adopt one-hot encoding to embed positions of tokens, rather than sinusoidal position encoding recommended in BERT model (Devlin et al., 2018). The reason is that our experiments show that compared with one-hot encoding, sinusoidal position encoding not only increases the complexity of models but also performs poorly on relatively small datasets.
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What encoding do they adopt to embed positions of tokens?
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One-hot encoding.
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Morocco has a coast by the Atlantic Ocean that reaches past the Strait of Gibraltar into the Mediterranean Sea. It is bordered by Spain to the north (a water border through the Strait and land borders with three small Spanish-controlled exclaves, Ceuta, Melilla, and Peñón de Vélez de la Gomera), Algeria to the east, and Western Sahara to the south. Since Morocco controls most of Western Sahara, its de facto southern boundary is with Mauritania.
The internationally recognised borders of the country lie between latitudes 27° and 36°N, and longitudes 1° and 14°W. Adding Western Sahara, Morocco lies mostly between 21° and 36°N, and 1° and 17°W (the Ras Nouadhibou peninsula is slightly south of 21° and west of 17°).
The geography of Morocco spans from the Atlantic Ocean, to mountainous areas, to the Sahara desert. Morocco is a Northern African country, bordering the North Atlantic Ocean and the Mediterranean Sea, between Algeria and the annexed Western Sahara. It is one of only three nations (along with Spain and France) to have both Atlantic and Mediterranean coastlines.
A large part of Morocco is mountainous. The Atlas Mountains are located mainly in the centre and the south of the country. The Rif Mountains are located in the north of the country. Both ranges are mainly inhabited by the Berber people. At 446,550 km2 (172,414 sq mi), Morocco excluding Western Sahara is the fifty-seventh largest country in the world. Algeria borders Morocco to the east and southeast, though the border between the two countries has been closed since 1994.
Spanish territory in Northwest Africa neighbouring Morocco comprises five enclaves on the Mediterranean coast: Ceuta, Melilla, Peñón de Vélez de la Gomera, Peñón de Alhucemas, the Chafarinas islands, and the disputed islet Perejil. Off the Atlantic coast the Canary Islands belong to Spain, whereas Madeira to the north is Portuguese. To the north, Morocco is bordered by the Strait of Gibraltar, where international shipping has unimpeded transit passage between the Atlantic and Mediterranean.
The Rif mountains stretch over the region bordering the Mediterranean from the north-west to the north-east. The Atlas Mountains run down the backbone of the country, from the northeast to the southwest. Most of the southeast portion of the country is in the Sahara Desert and as such is generally sparsely populated and unproductive economically.
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Can you give me some details about the moroccan geography ?
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Morocco has a coast by the Atlantic ocean and the mediterranean sea. It's bordered by Spain, Algeria and Mauritania. The geography of Morocco spans from the Atlantic ocean , to mountainous areas to the Sahara desert. The Rif mountains are located in the North and The Atlas mountains are located mainly in the center and south of the country.
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1902.09393
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FLOAT SELECTED: Table 1: Accuracy on the ListOps dataset. All models have 128 dimensions. Results for models with * are taken from Nangia and Bowman (2018).
FLOAT SELECTED: Table 1: Accuracy on the ListOps dataset. All models have 128 dimensions. Results for models with * are taken from Nangia and Bowman (2018).
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What are the baseline systems that are compared against?
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The system is compared to baseline models: LSTM, RL-SPINN and Gumbel Tree-LSTM
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The Island Packet 27 is a recreational keelboat, built predominantly of fiberglass, with teak wood trim and a teak and holly cabin sole. It is a Cutter-rigged sloop; with a spooned raked stem; a vertical transom; a rudder mounted to the keel, controlled by a wheel and a fixed long keel or optional long keel and centerboard. It displaces 8,000 lb (3,629 kg) and carries 3,000 lb (1,361 kg) of ballast.
The keel-equipped version of the boat has a draft of 3.70 ft (1.13 m), while the centerboard-equipped version has a draft of 6.0 ft (1.8 m) with the centerboard extended and 2.67 ft (0.81 m) with it retracted.
The boat is fitted with a Japanese Yanmar diesel engine of 18 hp (13 kW). The fuel tank holds 19 U.S. gallons (72 L; 16 imp gal) and the fresh water tank has a capacity of 31 U.S. gallons (120 L; 26 imp gal).
The mainsheet traveler is on the rear taffrail. The cockpit features two jib winches and two halyard winches. The cockpit has room for eight adults and includes an icebox. There is a bowsprit with an anchor roller.
The galley is located on the port side of the boat and includes a two-burner alcohol-fired stove and optional pressure water supply. The head is located just aft of the forward "V"-berth, on the starboard side. Additional sleeping accommodation is provided by a cabin settee, which converts to a double berth, plus a single berth to port. The cabin table folds up against the bulkhead.
Cabin ventilation consists of six opening ports and a forward hatch.
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Extract all the weights and measures from the text
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8000 lb / 3629 kg
3000 lb / 1361 kg
3.70 ft / 1.13 m
6.0 ft / 1.8 m
2.67 ft / 0.81m
18 hp / 13 kW
19 US gallons / 72 L / 16 imp gal
31 US gallons / 120 L / 26 imp gal
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1910.03891
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Figure FIGREF30 shows the test accuracy with increasing epoch on DBP24K and Game30K. We can see that test accuracy first rapidly increased in the first ten iterations, but reaches a stable stages when epoch is larger than 40. Figure FIGREF31 shows test accuracy with different embedding size and training data proportions. We can note that too small embedding size or training data proportions can not generate sufficient global information. In order to further analysis the embeddings learned by our method, we use t-SNE tool BIBREF27 to visualize the learned embedding. Figure FIGREF32 shows the visualization of 256 dimensional entity's embedding on Game30K learned by KANE, R-GCN, PransE and TransE. We observe that our method can learn more discriminative entity's embedding than other other methods.
In order to further analysis the embeddings learned by our method, we use t-SNE tool BIBREF27 to visualize the learned embedding.
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What further analysis is done?
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The answers are shown as follows:
* we use t-SNE tool BIBREF27 to visualize the learned embedding
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How long do you need to train before running a marathon?
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Experts recommend at least 3-6 months of training before running a marathon for the first time. This also depends on your physical fitness when you start training.
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| 241
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The proliferation of opinions expressed in online reviews, blogs, internet forums, and social media has created a pressing need for automated systems which enable customers, companies, or service providers to make informed decisions without having to absorb large amounts of opinionated text. Opinion summarization is the task of automatically generating summaries for a set of opinions about a specific target BIBREF0. Figure FIGREF1 shows various reviews about the movie “Coach Carter” and example summaries generated by humans and automatic systems. The vast majority of previous work BIBREF1 views opinion summarization as the final stage of a three-step process involving: (1) aspect extraction (i.e., finding features pertaining to the target of interest, such as battery life or sound quality); (2) sentiment prediction (i.e., determining the sentiment of the extracted aspects); and (3) summary generation (i.e., presenting the identified opinions to the user). Textual summaries are created following mostly extractive methods which select representative segments (usually sentences) from the source text BIBREF2, BIBREF3, BIBREF4, BIBREF5. Despite being less popular, abstractive approaches seem more appropriate for the task at hand as they attempt to generate summaries which are maximally informative and minimally redundant without simply rearranging passages from the original opinions BIBREF6, BIBREF7, BIBREF8, BIBREF9. General-purpose summarization approaches have recently shown promising results with end-to-end models which are data-driven and take advantage of the success of sequence-to-sequence neural network architectures. Most approaches BIBREF10, BIBREF11, BIBREF12, BIBREF13 encode documents and then decode the learned representations into an abstractive summary, often by attending to the source input BIBREF14 and copying words from it BIBREF15. Under this modeling paradigm, it is no longer necessary to identify aspects and their sentiment for the opinion summarization task, as these are learned indirectly from training data (i.e., sets of opinions and their corresponding summaries). These models are usually tested on domains where the input is either one document or a small set of documents. However, the number of opinions tends to be very large (150 for the example in Figure FIGREF1). It is therefore practically unfeasible to train a model in an end-to-end fashion, given the memory limitations of modern hardware. As a result, current approaches BIBREF16, BIBREF17, BIBREF18, BIBREF19 sacrifice end-to-end elegance in favor of a two-stage framework which we call Extract-Abstract: an extractive model first selects a subset of opinions and an abstractive model then generates the summary while conditioning on the extracted subset (see Figure FIGREF5). The extractive pass unfortunately has two drawbacks. Firstly, on account of having access to a subset of opinions, the summaries can be less informative and inaccurate, as shown in Figure FIGREF1. And secondly, user preferences cannot be easily taken into account (e.g., the reader may wish to obtain a summary focusing on the acting or plot of a movie as opposed to a general-purpose summary) since more specialized information might have been removed.
In this paper, we propose Condense-Abstract, an alternative two-stage framework which uses all input documents when generating the summary (see Figure FIGREF5). We view the opinion summarization problem as an instance of multi-source transduction BIBREF20; we first represent the input documents as multiple encodings, aiming to condense their meaning and distill information relating to sentiment and various aspects of the target being reviewed. These condensed representations are then aggregated using a multi-source fusion module based on which an opinion summary is generated using an abstractive model. We also introduce a zero-shot customization technique allowing users to control important aspects of the generated summary at test time. Our approach enables controllable generation while leveraging the full spectrum of opinions available for a specific target. We perform experiments on a dataset consisting of movie reviews and opinion summaries elicited from the Rotten Tomatoes website (BIBREF16; see Figure FIGREF1). Our framework outperforms state-of-the-art models by a large margin using automatic metrics and in a judgment elicitation study. We also verify that our zero-shot customization technique can effectively generate need-specific summaries.
These condensed representations are then aggregated using a multi-source fusion module based on which an opinion summary is generated using an abstractive model.
|
What model do they use for generating an opinion summary?
|
An abstractive model.
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| 297
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The past several years have witnessed a significant progress in Neural Machine Translation (NMT). Most NMT methods are based on the encoder-decoder architecture BIBREF0 , BIBREF1 , BIBREF2 and can achieve promising translation performance in a variety of language pairs BIBREF3 , BIBREF4 , BIBREF5 .
However, recent studies BIBREF6 , BIBREF7 show that NMT often generates words that make target sentences fluent, but unfaithful to the source sentences. In contrast, traditional Statistical Machine Translation (SMT) methods tend to rarely make this kind of mistakes. Fig. 1 shows an example that NMT makes mistakes when translating the phrase “jinkou dafu xiahua (the sharp decline in imports)” and the phrase “maoyi shuncha (the trade surplus)”, but SMT can produce correct results when translating these two phrases. BIBREF6 argues that the reason behind this is the use of distributed representations of words in NMT makes systems often generate words that seem natural in the context, but do not reflect the content of the source sentence. Traditional SMT can avoid this problem as it produces the translations based on phrase mappings.
Therefore, it will be beneficial to combine SMT and NMT to alleviate the previously mentioned problem. Actually, researchers have made some effective attempts to achieve this goal. Earlier studies were based on the SMT framework, and have been deeply discussed in BIBREF8 . Later, the researchers transfers to NMT framework. Specifically, coverage mechanism BIBREF9 , BIBREF10 , SMT features BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 and translation lexicons BIBREF6 , BIBREF16 , BIBREF17 have been fully explored. In contrast, phrase translation table, as the core of SMT, has not been fully studied. Recently, BIBREF18 and BIBREF19 explore the possibility of translating phrases in NMT. However, the “phrase” in their approaches are different from that used in phrase-based SMT. In BIBREF18 's models, the phrase pair must be a one-to-one mapping with a source phrase having a unique target phrase (named entity translation pairs). In BIBREF19 's models, the source side of a phrase pair must be a chunk. Therefore, it is still a big challenge to incorporate any phrase pair in the phrase table into NMT system to alleviate the unfaithfulness problem.
In this paper, we propose an effective method to incorporate a phrase table as recommendation memory into the NMT system. To achieve this, we add bonuses to the words in recommendation set to help NMT make better predictions. Generally, our method contains three steps. 1) In order to find out which words are worthy to recommend, we first derive a candidate target phrase set by searching the phrase table according to the input sentence. After that, we construct a recommendation word set at each decoding step by matching between candidate target phrases and previously translated target words by NMT. 2) We then determine the specific bonus value for each recommendable word by using the attention vector produced by NMT and phrase translation probability extracted from phrase table. 3) Finally we integrate the word bonus value into the NMT system to improve the final results.
In this paper, we make the following contributions:
1) We propose a method to incorporate the phrase table as recommendation memory into NMT system. We design a novel approach to find from the phrase table the target words worthy of recommendation, calculate their recommendation scores and use them to promote NMT to make better predictions.
2) Our empirical experiments on Chinese-English translation and English-Japanese translation tasks show the efficacy of our methods. For Chinese-English translation, we can obtain an average improvement of 2.23 BLEU points. For English-Japanese translation, the improvement can reach 1.96 BLEU points. We further find that the phrase table is much more beneficial than bilingual lexicons to NMT.
In this paper, we propose an effective method to incorporate a phrase table as recommendation memory into the NMT system.
|
What method do they propose in their paper?
|
An effective method to incorporate a phrase table as recommendation memory into Neural Machine Translation (NMT) system.
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What is the best part of staying in a vacation rental on the lake?
|
Our vacation rental is on Lake James in Indiana. This lake is pristine, and the water is warm during the summer. You can go boating, tubing, skiing, or check out the local sandbar, where people come together to hang out. The nearby town has go-carting, local restaurants, and a lovely donut shop that opens first thing on Saturday mornings and may even sell out. If you like the appeal of a laid-back and low-key vacation, Lake James is your destination.
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India's Last 10 Prime Ministers
|
Narendra Modi
Manmohan Singh
Atal Bihari Vajpayee
Inder Kumar Gujral
H. D. Deve Gowda
Atal Bihari Vajpayee
P. V. Narasimha Rao
Chandra Shekhar
Vishwanath Pratap Singh
Rajiv Gandhi
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In 208, Cao Cao marched south with his army hoping to quickly unify the empire. Liu Biao's son Liu Cong surrendered Jing Province and Cao was able to capture a sizable fleet at Jiangling. Sun Quan, the successor to Sun Ce in the lower Yangtze, continued to resist. His advisor Lu Su secured an alliance with Liu Bei, himself a recent refugee from the north, and Zhou Yu was placed in command of Sun Quan's navy, along with a veteran general who served the Sun family, Cheng Pu. Their combined armies of 50,000 met Cao Cao's fleet and 200,000-strong force at Red Cliffs that winter. Cao Cao had to cross the mighty Yangtze River. So when he along with his fleet sailed down the river, many of his men fell to exhaustion, disease and the northerners were not very comfortable on ships and the marshy terrain. So when the two sides met for war at Wulin, Cao Cao wasn’t able to inflict any defeat on the Allied Force. On the other hand, General Huang Gai of Sun Quan’s army came up with a sneaky plan. He sent a forged letter to Cao Cao, stating that he and others wanted to inflict to his side and they would also bring some of the finest ships of the allies. But little did Cao know that the ships were full of flammable reeds. So when they sailed and approached his fleet, they were set alight and they destroyed Cao Cao’s fleet and camps. Seeing the hopeless situation, Cao Cao ordered his army for a difficult retreat through the Marshlands, during which more of his men fell to disease and exhaustion. The allied victory at Red Cliffs ensured the survival of Liu Bei and Sun Quan, and provided the basis for the states of Shu and Wu.
|
Could you tell me how Cao Cao's fleet got destroyed during the battle of Red Cliffs based on this paragraph?
|
Cao Cao's fleet got set on fire by the General Huang Gai. Huang Gai initially informed Cao Cao that he'd betray the Allied Force formed by Liu Bei and Sun Quan. But Huang Gai was actually trying to take this chance to send ships loaded with flammable reeds to Cao Cao's fleet. Cao Cao took the bait and accepted the offer from Huang Gai. Then, when Huang Gai's ships arrived, the Allied Force set Cao Cao's fleet and camps on fire and essentially destroyed Cao Cao's army.
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null | false
| null |
When did Florida become a state?
|
Florida became a state on March 3, 1845.
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I plan a trip to Italy, what is the top 2 cities to visit?
|
Italy has many popular destinations featuring historical landmarks, vibrant local culture and beautiful architecture. While exact choice will depend on your specific interests, Rome and Florence are one of the most popular destinations for tourists.
In Rome you can visit Coliseum, Vatican and numerous other architectural landmarks, while Florence is considered the birthplace of European Renaissance.
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| null |
What are some examples of popular sitcoms on television in the 1980s?
|
Popular sitcoms that were on television in the 1980s include Cheers, Family Ties, the Cosby Show, Diff'rent Strokes, and the Facts of Life.
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| 110
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We apply our adaptively sparse Transformers on four machine translation tasks. For comparison, a natural baseline is the standard Transformer architecture using the softmax transform in its multi-head attention mechanisms. We consider two other model variants in our experiments that make use of different normalizing transformations:
1.5-entmax: a Transformer with sparse entmax attention with fixed $\alpha =1.5$ for all heads. This is a novel model, since 1.5-entmax had only been proposed for RNN-based NMT models BIBREF14, but never in Transformers, where attention modules are not just one single component of the seq2seq model but rather an integral part of all of the model components.
$\alpha $-entmax: an adaptive Transformer with sparse entmax attention with a different, learned $\alpha _{i,j}^t$ for each head.
The adaptive model has an additional scalar parameter per attention head per layer for each of the three attention mechanisms (encoder self-attention, context attention, and decoder self-attention), i.e.,
and we set $\alpha _{i,j}^t = 1 + \operatornamewithlimits{\mathsf {sigmoid}}(a_{i,j}^t) \in ]1, 2[$. All or some of the $\alpha $ values can be tied if desired, but we keep them independent for analysis purposes.
We apply our adaptively sparse Transformers on four machine translation tasks.
|
How many tasks do they set in their experiments?
|
Four.
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| null |
Which of Shakespeare’s plays is the longest?
|
Hamlet.
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null | false
| null |
Who was Nikola Tesla?
|
Nikola Tesla was a Serbian-American inventor known for his innovations around electricity. He discovered the rotating magnetic field.
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null | false
| null |
Identify which car manufacturer is German or American: Porsche, Chrysler
|
Porsche is German, Chrysler is American
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| null |
How many episodes of the first season of Game of Thrones did Alan Taylor direct?
|
Alan Taylor directed the last two episodes of the first season of Game of Thrones: Baelor and Fire and Blood.
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null | false
| null |
How much money comes in a game of Monopoly?
|
Newer (September 2008 and later) U.S. editions provide a total of $20,580—30 of each denomination
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| null |
Name four colleges in the Boston area.
|
Four colleges in the Boston Area are Harvard, MIT, Boston University, and Boston College.
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| null |
What are the ten best restaurants in London?
|
- Behind; Dalston
- Manteca; Shoreditch
- Restaurant St. Barts; Smithfield
- St. John; Farringdon
- F.K.A.B.A.M; Highbury
- Sabor; Mayfair
- Blacklock; Soho
- Planque; Haggerston
- Cycene; Shoreditch
- Brat; Shoreditch
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null | false
| null |
Tell me the country of these cities: Tokyo, London, New York, Taipei
|
These are the corresponding countries of the cities you listed out:
Tokyo: Japan
London: United Kingdom
New York: United States
Taipei: Taiwan
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null | false
| null |
Who designed Augusta National?
|
Augusta National was designed by Bobby Jones and Alister MacKenzie. Bobby Jones had heard about MacKenzie's prowess for designing golf courses and loved his design of Pasatiempo in Santa Cruz, CA. He visited MacKenzie at his course in Santa Cruz and asked him to partner with him in designing the world renowned Augusta National.
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| null |
From the passage provided, extract the total product revenue and % YoY growth using format: {$x} product revenue with {x%} YoY growth
Product revenue of $555.3 million in the fourth quarter, representing 54% year-over-year growth
Remaining performance obligations of $3.7 billion, representing 38% year-over-year growth
7,828 total customers
Net revenue retention rate of 158%
330 customers with trailing 12-month product revenue greater than $1 million
Authorized a $2.0 billion stock repurchase program
|
$555.3m product revenue with 54% YoY growth
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|
1906.03538
| false
| null |
(Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 . We use this system to retrieve a ranked list of best matching perspective/evidence from the corresponding index.
(Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 .
|
Which machine baselines are used?
|
The answers are shown as follows:
* Information Retrieval
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null | false
| null |
Thomas attended the local Medicine Lodge school through the 11th grade, the last grade the school offered. He moved to Pasadena, California and finished high school at John Muir High School. He attended Pasadena Junior College for two years and then obtained a Bachelor of Science degree in Forestry in February, 1941, from the University of Idaho, at Moscow, Idaho.
In 1950, he enrolled in Texas A&M University, at College Station, Texas, where he obtained a MS degree in Wildlife Management in 1951 and a Ph.D in Wildlife Management in 1954 (called Range Management then).
|
From the passage provided, extract where Thomas finished high school.
|
Thomas finished high school at John Muir High School in Pasadena, California.
|
1910.05608
| false
| null |
For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.
The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.
|
What is private dashboard?
|
Private dashboard is leaderboard where competitors can see results after competition is finished - on hidden part of test set (private test set).
|
1912.10806
| false
| null |
However, news reports are not all objective. We may increase bias because of some non-objective reports, if we rely on the information that is extracted from the news for prediction fully. Therefore, in order to enhance the prediction model's robustness, we will adopt differential privacy (DP) method. DP is a system for sharing information about a dataset publicly by describing groups' patterns within the dataset while withholding information about individuals in the dataset. DP can be achieved if the we are willing to add random noise to the result. For example, rather than simply reporting the sum, we can inject noise from a Laplace or gaussian distribution, producing a result that’s not quite exact, that masks the contents of any given row.
Differential privacy is one of privacy's most popular definitions today, which is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. It intuitively requires that the mechanism that outputs information about an underlying dataset is robust to one sample's any change, thus protecting privacy. A mechanism ${f}$ is a random function that takes a dataset $\mathcal {N}$ as input, and outputs a random variable ${f}(\mathcal {N})$. For example, suppose $\mathcal {N}$ is a news articles dataset, then the function that outputs compound score of articles in $\mathcal {N}$ plus noise from the standard normal distribution is a mechanism [7].
Therefore, in order to enhance the prediction model's robustness, we will adopt differential privacy (DP) method. DP is a system for sharing information about a dataset publicly by describing groups' patterns within the dataset while withholding information about individuals in the dataset. DP can be achieved if the we are willing to add random noise to the result. For example, rather than simply reporting the sum, we can inject noise from a Laplace or gaussian distribution, producing a result that’s not quite exact, that masks the contents of any given row.
It intuitively requires that the mechanism that outputs information about an underlying dataset is robust to one sample's any change, thus protecting privacy. A mechanism ${f}$ is a random function that takes a dataset $\mathcal {N}$ as input, and outputs a random variable ${f}(\mathcal {N})$. For example, suppose $\mathcal {N}$ is a news articles dataset, then the function that outputs compound score of articles in $\mathcal {N}$ plus noise from the standard normal distribution is a mechanism [7].
|
How does the differential privacy mechanism work?
|
The answers are shown as follows:
* A mechanism ${f}$ is a random function that takes a dataset $\mathcal {N}$ as input, and outputs a random variable ${f}(\mathcal {N})$.
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| null |
Mount Shakspere is a 12,174-foot-elevation (3,711 meter) summit located in Fresno County, California, United States.
Description
The mountain is set four miles west of the crest of the Sierra Nevada mountain range, in the Palisades area of Kings Canyon National Park. It is situated 4.5 miles (7.2 km) southwest of Middle Palisade, 3.5 miles (5.6 km) southeast of Giraud Peak, and one mile north-northwest of line parent Observation Peak. Mt. Shakspere ranks as the 384th-highest summit in California, and topographic relief is significant as the summit rises over 3,500 feet (1,100 meters) above Palisade Creek in approximately one mile (1.6 km). The first ascent of the summit was made July 20, 1930, by Francis P. Farquhar, Mary Lou Michaels, Doris Drust, Lorna Kilgariff, and Robert L. Lipman. This mountain's name has been officially adopted by the United States Board on Geographic Names.
|
How tall is Mt. Shakspere?
|
Mt. Shakespere is 12,174 feet (3,711 meters) tall.
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Why is the sky blue?
|
All the light in our daytime sky comes from the sun, and our sun produces light in many different colors. To understand this, just think of a prism. White light enters, and a rainbow of colors exits. Our sun is like that. Its light may seem uniform but it is actually emitting many different colors.
Our sky though is not a mix of blue and red and green because the earth's atmosphere is sandwiched between the sun and us, getting in the way. Atmospheric gases and particles scatter the incoming colors, bouncing them in all different directions. The amount of scattering increases as the wavelength of light decreases. Red light which has a long wavelength is scattered much less than blue light which has a short wavelength. As blue is scattered more, it fills our daytime sky.
As the sun traverses the sky and moves low on the horizon, the light that reaches our eyes must actually travel further through the atmosphere. This has the effect of completely bouncing away all the blue light. We are left with the reds and yellows and oranges in wonderful mixture, making sunrises and sunsets so beautiful.
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Jon Rahm Rodríguez (born 10 November 1994) is a Spanish professional golfer. He was the number one golfer in the World Amateur Golf Ranking for a record 60 weeks and later became world number one in the Official World Golf Ranking, first achieving that rank after winning the Memorial Tournament in July 2020. In June 2021, Rahm became the first Spanish golfer to win the U.S. Open. In 2023, he won the Masters Tournament, for his second major championship.
|
Where is Jon Rahm from?
|
Spain
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null | false
| 121
|
To evaluate the effectiveness of our model, and demonstrate its flexibility at handling heavy data-structure made of several types of entities, we used the RotoWire dataset BIBREF10. It includes basketball games statistical tables paired with journalistic descriptions of the games, as can be seen in the example of Figure FIGREF1. The descriptions are professionally written and average 337 words with a vocabulary size of $11.3$K. There are 39 different record keys, and the average number of records (resp. entities) in a single data-structure is 628 (resp. 28). Entities are of two types, either team or player, and player descriptions depend on their involvement in the game. We followed the data partitions introduced with the dataset and used a train/validation/test sets of respectively $3,398$/727/728 (data-structure, description) pairs.
It includes basketball games statistical tables paired with journalistic descriptions of the games, as can be seen in the example of Figure 1.
|
What does the dataset contain?
|
Basketball games statistical tables paired with journalistic descriptions of the games.
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| null |
Hickory wood is hard, stiff, dense and shock resistant. There are woods stronger than hickory and woods that are harder, but the combination of strength, toughness, hardness, and stiffness found in hickory wood is not found in any other commercial wood. It is used for tool handles, pickaxe handles, bows, wheel spokes, carts, drumsticks, lacrosse stick handles, golf club shafts (sometimes still called hickory stick, even though made of steel or graphite), the bottoms of skis, walking sticks, and for punitive use as a switch (like hazel), and especially as a cane-like hickory stick in schools and use by parents. Paddles are often made from hickory. This property of hickory wood has left a trace in some Native American languages: in Ojibwe, hickory is called mitigwaabaak, a compound of mitigwaab "bow" and the final -aakw "hardwood tree". Due to its grain structure, hickory is more susceptible to moisture absorption than other species of wood, and is therefore more prone to shrinkage, warping or swelling with changes in humidity.
|
Given this paragraph about hickory wood, tell me what sporting equipment it might be used for.
|
Hickory wood may be used to make lacrosse stick handles, golf club shafts, and the bottom of skis.
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| null |
What's so great about St Martin?
|
St Martin is a Caribbean island that has so much to offer! A fun fact about the island is that it has a "Dutch side" and a "French side." On the Dutch side of the island, most people speak English. However, on the French side many speak French and also the labels on things in the grocery stores are in French. The fun part about it is, should you want to go from one side to the other, no passport required, you drive between the two like you would drive from one state to the other in the U.S. there is only a sign telling you you've entered the other side! Outside of this fun feature, there are beautitful beaches, great food, and an airport that allows you to get so close to the planes when they land you feel as though you could touch the bottom.
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|
1806.02908
| false
| null |
To explore how helpful these transformations are, we incorporated 20 simple transformations and 15 additional sequences of transformations in our experiment to see their effect on different type of metrics on four different ML models (See Figure FIGREF3 ).
FLOAT SELECTED: Fig. 1: List of transformations.
To explore how helpful these transformations are, we incorporated 20 simple transformations and 15 additional sequences of transformations in our experiment to see their effect on different type of metrics on four different ML models (See Figure FIGREF3 ).
FLOAT SELECTED: Fig. 1: List of transformations.
|
What preprocessing techniques are used in the experiments?
|
The answers are shown as follows:
* See Figure FIGREF3
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null | false
| null |
What is the company zoom?
|
Zoom is a video communication protocol used by many companies today. Zoom has been used by school to conduct at home schooling during the COVID-19 pandemic. Zoom is also used by big Fortune 500 corporations to conduct business meetings internally with employees and externally with customers and other accounts. The virtual interaction offered by zoom is a great alternative to save costs on in-person meetings where physical travel is required. The company was founded in 2011 and has about 8500 employees as of 2023.
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| null |
What is the setting of Snow Crash?
|
The story opens in Los Angeles in the 21st century, an unspecified number of years after a worldwide economic collapse. Los Angeles is no longer part of the United States since the federal government has ceded most of its power and territory to private organizations and entrepreneurs. Franchising, individual sovereignty, and private vehicles reign supreme. Mercenary armies compete for national defense contracts, while private security guards preserve the peace in sovereign gated housing developments.: 45 Highway companies compete to attract drivers to their roads,: 7 and all mail delivery is by hired courier.: 306 The remnants of government maintain authority only in isolated compounds, where they do tedious make-work that is, by and large, irrelevant to the society around them.: 176 Much of the world's territory has been carved up into sovereign enclaves known as Franchise-Organized Quasi-National Entities (FOQNEs),: 14 each run by its own big business franchise (such as "Mr. Lee's Greater Hong Kong", or the corporatized American Mafia), or various residential burbclaves (quasi-sovereign gated communities). In this future, American institutions are far different from those in the actual United States at the time the book was published; for example, a for-profit organization, the CIC, has evolved from the CIA's merger with the Library of Congress.: 22
|
Snow Crash is set in the Los Angeles of the future in which the government has collapsed and been replaced by corporations, mercenary armies, and a new type of organization, the Franchise-Organized Quasi-National Entity. Examples of the latter include Mr. Lee's Greater Hong Kong and the corporatized American Mafia. Government functions like road building, policing, and mail delivery have been taken over by corporations.
|
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| null |
On September 26, 2019, Peloton became a public company via an initial public offering, raising $1.16 billion and valuing the company at $8.1 billion. After reaching a market valuation of $50 billion in January 2021, the company's valuation was back to around $8 billion by April 2022.
|
When did Peloton IPO?
|
Peloton became a public company via an initial public offering on September 26, 2019.
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null | false
| null |
Did Neo take the blue pill or the red pill?
|
Neo took the red pill in the first The Matrix movie.
|
|
1908.06006
| false
| null |
We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp). Dataset statistics are shown in Table TABREF29. Classes are perfectly balanced, for all datasets.
We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp).
|
What are the datasets used
|
The answers are shown as follows:
* large-scale document classification datasets introduced by BIBREF14
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null | false
| null |
The Cycle Messenger World Championships, or CMWCs, are an annual urban cycling competition whereby cycle messengers and cycling enthusiasts showcase their skills in an array of events, many of which simulate everyday tasks for a cycle messenger. Each year, the CMWCs are held in a different city around the world, with each host city designing its own unique course. The highlight of the CMWCs is the main race in which cyclists are given manifests and must pickup and deliver items to various locations around the host city as quickly and efficiently as possible. The total number of pickups and deliveries, or "drop offs" is unknown to the messengers before the race, but has been known to reach up to 100 stops and usually takes between 3–4 hours to complete. The cyclist who completes all of the assigned deliveries in the least amount of time wins the race. In addition to the main race there are also several side events which have become increasingly popular over the past few years. These events change yearly depending on the discretion of the host city but usually include backward circles, bike polo, bunnyhop, cargo race, longest skid, sprints, and trackstand.
|
What does CMWCs stand for?
|
The Cycle Messenger World Championships
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null | false
| null |
What are some fun activities to do with my coworkers if we all work remotely?
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Organizing virtual events would be a great way to spend time with your coworkers. You could participate in a virtual escape room, attend a virtual wine tasting, play online games together, host a weekly coffee-and-chat meeting, or create a virtual book club.
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null | false
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Write a description detailing why Soccer is better than Basketball.
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Soccer is a better sport than Basketball because it is a sport with a minimal ruleset making it easier to teach and reach a greater audience. Soccer also does not require a well built out infrastructure in order to play it, all you need is a ball, field, and something to act as goals.
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null | false
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Is an avocado a fruit or a vegetable?
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An avocado is a single-seeded berry fruit
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null | false
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Write a sarcastic apology letter to a neighbor after they complained about you being too loud in the middle of the day. Make it no longer than three sentences.
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Dear neighbor. I apologize for being loud during the daytime when everyone is awake. It surely was a huge burden for you to know that I exist. I'll try to be less perceivable in the future. Sincerely, You Noisy Neighbor.
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1909.06762
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We compare our model with several baselines including:
Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.
Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.
KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.
Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.
DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding.
We compare our model with several baselines including:
Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.
Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.
KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.
Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.
DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding.
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What were the baseline systems?
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The answers are shown as follows:
* Attn seq2seq
* Ptr-UNK
* KV Net
* Mem2Seq
* DSR
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null | false
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What is the Culture Against Man
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Culture Against Man is a 1963 book-length ethnography by anthropologist Jules Henry of his native United States culture. The book is presented in three parts: American life and its institutions, discussion on child-rearing, and discussion on nursing homes.
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null | false
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What are regarded as some of the best hip-hop albums of all time?
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Some hip hop albums that are widely regarded as some of the best of all time are:
Illmatic - Nas
My Beautiful Dark Twisted Fantasy - Kanye West
Aquemini - Outkast
The Blueprint - Jay-Z
ATLiens - Outkast
To Pimp a Butterfly - Kendrick Lamar
The Chronic - Dr. Dre
Graduation - Kanye West
good kid, m.A.A.d city - Kendrick Lamar
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