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Finally, we generate the output state of ModeCell as Ht=ot⊙tanh(Bt). Ht is taken as inputs by the next ModeCell at the upper level when multiple ModeCells are being stacked in ModeRNN. In other words, ModeCell is to ModeRNN what LSTM is to the stacked LSTM network
|
Do the higher layers receive only the cell output or do they also have access to the slot bus?
|
As described in Section 3.2.3 in the revision, Ht is taken as inputs by the next ModeCell at the upper level when multiple ModeCells are being stacked in ModeRNN. In other words, ModeCell is to ModeRNN what LSTM is to the stacked LSTM network*.
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null | false
| 145
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Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.
Transformer-XL improves upon the perplexity score to 73.58 which is 27% better than the LSTM model.
|
What is Transformer-XL's perplexity score?
|
73.58.
|
1901.10826
| true
| null |
In the epoch 96, the proposed method has already an FER more than INLINEFORM0 better than the original SincNet for almost every value of INLINEFORM1 excluding INLINEFORM2 . The difference keeps increasing over the epochs, and at epoch 352 the proposed method has an FER of INLINEFORM3 ( INLINEFORM4 ) against INLINEFORM5 from SincNet, which means that at this epoch AM-SincNet has a Frame Error Rate approximately INLINEFORM6 better than traditional SincNet. The Figure FIGREF7 plots the Frame Error Rate on the test data for both methods along the training epochs. For the AM-SincNet, we used the margin parameter INLINEFORM7 .
FLOAT SELECTED: Fig. 3. Comparison of Frame Error Rate (%) from SincNet and AM-SincNet (m=0.50) over the training epochs for TIMIT dataset.
The Figure FIGREF7 plots the Frame Error Rate on the test data for both methods along the training epochs.
FLOAT SELECTED: Fig. 3. Comparison of Frame Error Rate (%) from SincNet and AM-SincNet (m=0.50) over the training epochs for TIMIT dataset.
|
Do they visualize the difference between AM-Softmax and regular softmax?
|
Yes.
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null | false
| null |
When cooking pizza, classify each item as being "Useful" or "Not Useful": Tomato Sauce, Dough, Sun, Rain, Cheese, Oven, Shower
|
Tomato Sauce: Useful
Dough: Useful
Sun: Not Useful
Rain: Not Useful
Cheese: Useful
Oven: Useful
Shower: Not Useful
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null | false
| null |
Give a list of reasons I should get a dog.
|
1) Dogs are great companions. 2) It will teach my kids responsibility. 3) Dogs can serve as an alarm clock to wake my kids for school. 4) Dogs can replace a mop and broom in the kitchen for dropped food. 5) Dogs are cuddly. 6) Dogs provide constant love and affection. 7) Dogs are protective and can guard our house. 8) Dogs are built in playmates for my kids.
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null | false
| null |
Identify which animal species is alive or extinct: Scutosaurus, Yellowfin Tuna
|
Yellowfin Tuna is alive, Scutosaurus is extinct.
|
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null | false
| null |
Einstein was born in the German Empire, but moved to Switzerland in 1895, forsaking his German citizenship (as a subject of the Kingdom of Württemberg)[note 1] the following year. In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich, graduating in 1900. In 1901, he acquired Swiss citizenship, which he kept for the rest of his life, and in 1903 he secured a permanent position at the Swiss Patent Office in Bern. In 1905, he was awarded a PhD by the University of Zurich. In 1914, Einstein moved to Berlin in order to join the Prussian Academy of Sciences and the Humboldt University of Berlin. In 1917, Einstein became director of the Kaiser Wilhelm Institute for Physics; he also became a German citizen again, this time Prussian.
In 1933, while Einstein was visiting the United States, Adolf Hitler came to power in Germany. Einstein, as a Jew, objected to the policies of the newly elected Nazi government; he settled in the United States and became an American citizen in 1940. On the eve of World War II, he endorsed a letter to President Franklin D. Roosevelt alerting him to the potential German nuclear weapons program and recommending that the US begin similar research. Einstein supported the Allies but generally denounced the idea of nuclear weapons.
|
Extract the key dates and events in the life of Albert Einstein in a bulleted list in the format {date} - {event}
|
1895 - Moved to Switzerland
1897 - Enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich
1900 - Graduated from the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school
1901 - Became a Swiss citizen
1903 - Started a permanent position at the Swiss Patent Office in Bern
1905 - Awarded a PhD by the University of Zurich
1914 - Moved to Berlin to join the Prussian Academy of Sciences and the Humboldt University of Berlin
1917 - Became the director of the Kaiser Wilhelm Institute for Physics
1917 - Became a German citizen again
1933 - Settled in the United States
1940 - Became an American citizen
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null | false
| null |
Can you tell me the root of these language? Hebrew. English, Spanish, Portuguese, Romanian, Arabic, Farsi, Italian
|
Yes sure!
Hebrew root is Northwest Semitic language
English is of Germanic origins
Spanish is of Latin origins
Portuguese is of Latin origins
Romanian is of Latin origins
Arabic is a Semitic language
Farsi is of west Iranian origins
Italian is of Latin origins
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null | false
| null |
Give me a list of the great synth-pop artists and a song for each that I might not have heard.
|
Sure, here is a short list of Synth-pop artists with songs that you may not have heard yet:
- Blondie – Heart Of Glass (1978)
- Kraftwerk – The Model (1978)
- Gary Numan – Cars (1979)
- OMD – Electricity (1979)
- John Foxx – Underpass (1980).
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null | false
| null |
Jo-Anne Polak (born 1959) was the general manager of the Ottawa Rough Riders from 1989 to 1991. Upon her appointment, Polak became the first woman executive in the Canadian Football League and the first woman general manager to lead a North American sports team. After leaving the Rough Riders, Polak primarily worked at Hill & Knowlton Canada as their vice president between the 1990s to 2000s. After becoming a general manager for Canada Post in 2008, she was named Vice President of Communications in 2011.
|
Can you list down some points about Jo-Anne Polak from the given text?
|
1. From 1989 to 1991, Jo-Anne Polak served as the general manager of the Ottawa Rough Riders.
2. Her birth year was 1959.
3. In the Canadian Football League, Polak was the league's first female executive.
4. Polak was the first female general manager of a professional sports organisation in North America.
5. From the 1990s until the 2000s, after her time with Rough Riders, Polak worked as vice president of Hill & Knowlton Canada.
6. After joining Canada Post as a manager in 2008, she was named vice president of communications in 2011.
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null | false
| null |
Pre-colonial Africa possessed perhaps as many as 10,000 different states and polities characterized by many different sorts of political organization and rule. These included small family groups of hunter-gatherers such as the San people of southern Africa; larger, more structured groups such as the family clan groupings of the Bantu-speaking peoples of central, southern, and eastern Africa; heavily structured clan groups in the Horn of Africa; the large Sahelian kingdoms; and autonomous city-states and kingdoms such as those of the Akan; Edo, Yoruba, and Igbo people in West Africa; and the Swahili coastal trading towns of Southeast Africa.
|
What types of political organizations did pre-colonial Africa have?
|
Pre-colonial Africa had many political organizations including: small family groups, larger family clan groupings, heavily structured clan groups, and autonomous city-states and kingdoms.
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null | false
| null |
Cricket is a bat-and-ball game played between two teams of eleven players on a field at the centre of which is a 22-yard (20-metre) pitch with a wicket at each end, each comprising two bails balanced on three stumps. The batting side scores runs by striking the ball bowled at one of the wickets with the bat and then running between the wickets, while the bowling and fielding side tries to prevent this (by preventing the ball from leaving the field, and getting the ball to either wicket) and dismiss each batter (so they are "out"). Means of dismissal include being bowled, when the ball hits the stumps and dislodges the bails, and by the fielding side either catching the ball after it is hit by the bat, but before it hits the ground, or hitting a wicket with the ball before a batter can cross the crease in front of the wicket. When ten batters have been dismissed, the innings ends and the teams swap roles. The game is adjudicated by two umpires, aided by a third umpire and match referee in international matches. They communicate with two off-field scorers who record the match's statistical information.
Forms of cricket range from Twenty20, with each team batting for a single innings of 20 overs (each "over" being a set of 6 fair opportunities for the batting team to score) and the game generally lasting three hours, to Test matches played over five days. Traditionally cricketers play in all-white kit, but in limited overs cricket they wear club or team colours. In addition to the basic kit, some players wear protective gear to prevent injury caused by the ball, which is a hard, solid spheroid made of compressed leather with a slightly raised sewn seam enclosing a cork core layered with tightly wound string.
|
What is Cricket in sports?
|
Cricket is highly popular game that's originated in south-eastern counties of England and now prominent sport in a number of countries in particular south asian countries like India, Pakistan, Sri Lanka and Bangladesh.
It is in essence a bat-and-ball game played between two teams of eleven players on a field. The batting side scores runs by striking the ball with the bat and then running between the wickets, while the bowling and fielding side tries to prevent this and dismiss each batter.
Forms of cricket range from Twenty20, one day to Test match lasting up to 5 days. The game originated as children's game is now a highly competitive professional sport followed by billions of people and in last decade commercialised at club level with the introduction of Indian Premier League (IPL).
|
null | false
| 112
|
It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn’t imply fear. A similar pattern can be found in the 4th example of Anger and 3rd example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3rd tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context.
|
What are the flaws in the system?
|
It is difficult for the system to capture the overall sentiment and it leads to amplifying or vanishing intensity signals. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context.
|
null | false
| null |
What are the rules changes made to baseball this year?
|
The MLB (major league baseball) association made several rule adjustments to the 2023 season. First, the bases have increased in sizes by 3 inches for a total of 18 inches squared. Second, a pitch clock/time has been introduced. Third, the defensive shift has been banned.
|
|
1912.00864
| false
| null |
NAGMWA is much better than the other methods except NAGM, since it generates answers whose conclusions and supplements as well as their combinations closely match the questions. Thus, conclusions and supplements in the answers are consistent with each other and avoid confusion made by several different conclusion-supplement answers assigned to a single non-factoid questions. Finally, NAGM is consistently superior to the conventional attentive encoder-decoders regardless of the metric. Its ROUGE-L and BLEU-4 scores are much higher than those of CLSTM. Thus, NAGM generates more fluent sentences by assessing the context from conclusion to supplement sentences in addition to the closeness of the question and sentences as well as that of the question and sentence combinations.
The experts asked questions, which were not included in our training datasets, to the AI system and rated the answers; one answer per question. The experts rated the answers as follows: (1) the content of the answer matched the question, and the grammar was okay; (2) the content was suitable, but the grammar was poor; (3) the content was not suitable, but the grammar was okay; (4) both the content and grammar were poor. Note that our evaluation followed the DUC-style strategy. Here, we mean “grammar” to cover grammaticality, non-redundancy, and referential clarity in the DUC strategy, whereas we mean the “content matched the questions” to refer to “focus” and “structure and coherence” in the DUC strategy. The evaluators were given more than a week to carefully evaluate the generated answers, so we consider that their judgments are reliable. Each expert evaluated 50 questions. We combined the scores of the experts by summing them. They did not know the identity of the system in the evaluation and reached their decisions independently.
These results indicate that the experts were much more satisfied with the outputs of NAGM than those of CLSTM. This is because, as can be seen in Table 7, NAGM generated longer and better question-related sentences than CLSTM did. NAGM generated grammatically good answers whose conclusion and supplement statements are well matched with the question and the supplement statement naturally follows the conclusion statement.
FLOAT SELECTED: Table 4: ROUGE-L/BLEU-4 for nfL6.
FLOAT SELECTED: Table 6: Human evaluation (nfL6).
Finally, NAGM is consistently superior to the conventional attentive encoder-decoders regardless of the metric. Its ROUGE-L and BLEU-4 scores are much higher than those of CLSTM.
The experts asked questions, which were not included in our training datasets, to the AI system and rated the answers; one answer per question. The experts rated the answers as follows: (1) the content of the answer matched the question, and the grammar was okay; (2) the content was suitable, but the grammar was poor; (3) the content was not suitable, but the grammar was okay; (4) both the content and grammar were poor.
These results indicate that the experts were much more satisfied with the outputs of NAGM than those of CLSTM.
FLOAT SELECTED: Table 4: ROUGE-L/BLEU-4 for nfL6.
FLOAT SELECTED: Table 6: Human evaluation (nfL6).
|
How much more accurate is the model than the baseline?
|
For the Oshiete-goo dataset, the NAGM model's ROUGE-L score is higher than the highest performing conventional model, Trans, by 0.021, and its BLEU-4 score is higher than the highest performing model CLSTM by 0.037. For the nfL6 dataset, the NAGM model's ROUGE-L score is higher than the highest performing conventional model, CLSTM, by 0.028, and its BLEU-4 score is higher than the highest performing model CLSTM by 0.040. Human evaluation of the NAGM's generated outputs for the Oshiete-goo dataset had 47% ratings of (1), the highest rating, while CLSTM only received 21% ratings of (1). For the nfL6 dataset, the comparison of (1)'s was NAGM's 50% to CLSTM's 30%.
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null | false
| null |
Taekwondo, Tae Kwon Do or Taekwon-Do is a Korean form of martial arts involving punching and kicking techniques, with emphasis on head-height kicks, spinning jump kicks, and fast kicking techniques. The literal translation for tae kwon do is "kicking", "punching", and "the art or way of". They are a kind of martial arts in which one attacks or defends with hands and feet anytime or anywhere, with occasional use of weapons. The physical training undertaken in Taekwondo is purposeful and fosters strength of mind through mental armament.
|
What does kwon mean in taekwondo?
|
'kwon' in Korean means punching or striking with the hand
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null | false
| null |
Given this series of navigation instructions, determine whether one would end up back at the starting point: Take 1 step. Take 2 steps. Take 3 steps. Turn around. Take 6 steps. Turn right.
|
Well, I'd say yes and no. While the person would be at the starting point, he wouldn't be facing the same direction.
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null | false
| 158
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Many machine learning models in question answering tasks often involve matching mechanism. For example, in factoid question answering such as SQuAD BIBREF1 , one needs to match between query and corpus in order to find out the most possible fragment as answer. In multiple choice question answering, such as MC Test BIBREF2 , matching mechanism can also help make the correct decision.
The easiest way of matching is to calculate the cosine similarity between two vectors. It is generally done by two step: First, encode text into word vectors, sentence vectors or paragraph vectors. Second, simply calculate the cosine similarity between target vectors. This method performs well when applied to word-level matching. However, as for matching between sentences or paragraphs, a single vector is not sufficient to encode all the important information. In order to solve this problem, Wang and Jiang proposed a “compare-aggregate” BIBREF3 framework that performs word-level matching using multiple techniques followed by aggregation with convolutional neural network. In their work, they show that compare-aggregate framework can effectively match two sequences through a wide range.
Although "compare-aggregate" matching mechanism performs well on multiple question answering tasks, it has two deficiencies. First, it tends to aggregate passively through the sequence rather than take the importance of each element into account. That is, "compare aggregate" model considers all the sequential contents equally. Second, "compare aggregate" can only take few neighboring elements into account at the same time because of the limitation of CNN kernel size.
In this paper, we propose Query-based Attention CNN (QACNN) to deal with the deficiencies above. First, we add query-based attention mechanism into original "compare aggregate" model. Moreover, We re-design the aggregation mechanism in "compare aggregate" to a two-staged CNN architecture which comprises word-level aggregation and sentence-level aggregation. In this way, QACNN can efficiently extract features cross sentences.
Our model consists of three components: 1) The similarity mapping layer which converts the input passage, query and choice into feature representation and perform a similarity operation to each other. 2) The attention-based CNN matching network composed of a two-staged CNN focusing on word-level and sentence-level matching respectively. 3) The prediction layer which makes the final decision.
The main contributions of this work are three-fold. First, we introduce a two-staged CNN architecture which integrates information from word-level to sentence-level, and then from sentence-level to passage-level. Second, we introduce attention mechanism into this net. We use specially designed CNN structure and attention mechanism to recognize the pattern of similarity map and eventually identify specific syntactic structure of queries. By transforming passage-query feature into attention maps and applying it to passage-choice matching result, we reasonably give weight to every word in the passage. Lastly, our model reaches 79.99% accuracy on the MovieQA dataset which yields top 1 result on this dataset.
First, we add query-based attention mechanism into original "compare aggregate" model. Moreover, We re-design the aggregation mechanism in "compare aggregate" to a two-staged CNN architecture which comprises word-level aggregation and sentence-level aggregation. In this way, QACNN can efficiently extract features cross sentences.
|
How to extract features across sentences by QACNN?
|
First, the authors add query-based attention mechanism into original "compare aggregate" model. Second, the authors re-design the aggregation mechanism in "compare aggregate" to a two-staged CNN architecture which comprises word-level aggregation and sentence-level aggregation.
|
null | false
| null |
What is the best sport?
|
It depends on who you ask, but as an AI language model, I cannot determine which sport is the best. The best sport for a person may differ compared to another person based on the level of athleticism and the complexity of the game required. Some of the popular sports around the world include soccer, basketball, baseball, football, tennis, cricket, and others.
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null | false
| null |
How many squares does a chessboard have?
|
64
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|
1904.05584
| false
| null |
To face the previous problem, we tested our methods in a wide variety of datasets, including some that explicitly model relatedness (WS353R), some that explicitly consider similarity (WS353S, SimLex999, SimVerb3500), and some where the distinction is not clear (MEN, MTurk287, MTurk771, RG, WS353). We also included the RareWords (RW) dataset for evaluating the quality of rare word representations. See appendix:datasets for a more complete description of the datasets we used.
To face the previous problem, we tested our methods in a wide variety of datasets, including some that explicitly model relatedness (WS353R), some that explicitly consider similarity (WS353S, SimLex999, SimVerb3500), and some where the distinction is not clear (MEN, MTurk287, MTurk771, RG, WS353).
|
Which similarity datasets do they use?
|
The answers are shown as follows:
* WS353S
* SimLex999
* SimVerb3500
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null | false
| null |
What are some good beginner songs to learn on guitar?
|
The following are good songs a beginner can learn on guitar:
- Smoke on the Water by Deep Purple
- Brown Eyed Girl by Van Morrison
- Heart of Gold by Neil Young
- Seven Nation Army by The White Stripes
- What's Up? by 4 Non Blondes
- Wild Thing by The Troggs
- Wonderwall by Oasis
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null | false
| null |
Classify the following as indoor, outdoor, activities or both: basketball, hiking, soccer, rock climbing, mountaineering, swimming.
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Basketball, soccer, rock climbing and swimming are both indoor and outdoor activities. Mountaineering and hiking are outdoor activities.
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|
1909.00361
| false
| null |
We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
Note that, since the test and challenge sets are preserved by CMRC 2018 official to ensure the integrity of the evaluation process, we submitted our best-performing systems to the organizers to get these scores. The resource in source language was chosen as SQuAD BIBREF4 training data. The settings of the proposed approaches are listed below in detail.
Translation: We use Google Neural Machine Translation (GNMT) system for translation. We evaluated GNMT system on NIST MT02/03/04/05/06/08 Chinese-English set and achieved an average BLEU score of 43.24, compared to previous best work (43.20) BIBREF17, yielding state-of-the-art performance.
FLOAT SELECTED: Table 1: Statistics of CMRC 2018 and DRCD.
We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
The resource in source language was chosen as SQuAD BIBREF4 training data.
We evaluated GNMT system on NIST MT02/03/04/05/06/08 Chinese-English set and achieved an average BLEU score of 43.24, compared to previous best work (43.20) BIBREF17, yielding state-of-the-art performance.
FLOAT SELECTED: Table 1: Statistics of CMRC 2018 and DRCD.
|
How big are the datasets used?
|
Evaluation datasets used:
CMRC 2018 - 18939 questions, 10 answers
DRCD - 33953 questions, 5 answers
NIST MT02/03/04/05/06/08 Chinese-English - Not specified
Source language train data:
SQuAD - Not specified
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null | false
| null |
Give me a list of ways to express your love to someone
|
1. Write them a card
2. Text them a beautiful message
3. Send them some flowers
4. Organise a date out
5. Take a walk and tell them how you feel
6. Spend quality time with them
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null | false
| null |
What are some of the most well-known castles in the Loire Valley in France?
|
The list of castles includes Château de Cheverny, Château de Chambord, Château de Chenonceau, Château d'Azay-le-Rideau, Château de Villandry, Château d'Amboise, and Château de Chaumont.
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2001.01589
| false
| null |
The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time. The problem with BPE is that it do not consider the morpheme boundaries inside words, which might cause a loss of morphological properties and semantic information. Hence, on the analyses of the above popular word segmentation methods, we propose the morphologically motivated segmentation strategy that combines the morpheme segmentation and BPE for further improving the translation performance of NMT.
Compared with the sentence of word surface forms, the corresponding sentence of stem units only contains the structure information without considering morphological information, which can make better generalization over inflectional variants of the same word and reduce data sparseness BIBREF8. Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation.
The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time.
Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation.
|
How is morphology knowledge implemented in the method?
|
A BPE model is applied to the stem after morpheme segmentation.
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null | false
| null |
How do you play chess?
|
Chess is a game that is played on a checkerboard, which is composed of 64 alternating black and white squares that indicate where a player's pieces can be placed. There are six different types of pieces in chess, including: Pawns, Rooks, Bishops, Knights, a Queen, and a King. Each of these pieces can move on the board in different ways. The goal of the game is to utilize your pieces to capture your opponent's king. In a game of chess, players alternate turns moving one piece at a time. When a player moves a piece, they are either positioning the piece on an open square where no other piece is currently on, or they are capturing an opponent's piece. To win a game of chess, players will balance strategically positioning their pieces and capturing their opponent's pieces. Doing so will allow a player to have the opportunity to put their opponent in a position called "Checkmate". When a player is checkmated, it means that their King is in an attacking line of one or more of their opponent's pieces and they have no legal moves to escape this attack. If a player is checkmated, they have lost the game.
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null | false
| null |
The Tata Group (/ˈtɑːtɑː/) is an Indian multinational conglomerate headquartered in Mumbai. Established in 1868, it is India's largest conglomerate, with products and services in over 150 countries, and operations in 100 countries across six continents. Acknowledged as the founder of the Tata Group, Jamsetji Tata is sometimes referred to as the "father of Indian industry".
Each Tata company operates independently under the guidance and supervision of its own board of directors and shareholders. Philanthropic trusts control over 66% of the Tata holding company Tata Sons, while the Tata family is a very small shareholder.
The group's annual revenue for fiscal year 2021–22 was reported to be US$128 billion. There are 29 publicly-listed Tata Group companies with a combined market capitalisation of $311 billion as of March 2022. The company has operations across Asia, Africa, America, and Middle East. Significant Tata Group affiliates include Tata Consultancy Services, Tata Consumer Products, Tata Motors, Tata Power, Tata Steel, Voltas, Titan Company, Tanishq, Tata Chemicals, Tata Communications, Trent, Tata Elxsi, Indian Hotels Company, Air India, TajAir, Tata Cliq, Tata Advanced Systems, Tata Capital, Cromā, BigBasket and Tata Starbucks.
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Name the affiliates of the Tata Group.
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Significant Tata Group affiliates include Tata Consultancy Services, Tata Consumer Products, Tata Motors, Tata Power, Tata Steel, Voltas, Titan Company, Tanishq, Tata Chemicals, Tata Communications, Trent, Tata Elxsi, Indian Hotels Company, Air India, TajAir, Tata Cliq, Tata Advanced Systems, Tata Capital, Cromā, BigBasket and Tata Starbucks.
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Is my children's school information available to anyone on the Internet?
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Although the website is accessible over the internet, access to your student’s information is secured by a username and password. Parents can view information about their children only. They cannot access the records of other students. The application is hosted within your children's school district infrastructure – no third-party vendor is hosting this.
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In this work, we have presented a simple and effective approach for rapidly building a bilingual LM under a limited computational budget. Using BERT as the starting point, we demonstrate that our approach produces better than mBERT on two cross-lingual zero-shot sentence classification and dependency parsing. We find that the performance of our bilingual LM, RAMEN, correlates with the performance of the original pre-trained English models. We also find that RAMEN is also a powerful feature extractor in supervised dependency parsing. Finally, we hope that our work sparks of interest in developing fast and effective methods for transferring pre-trained English models to other languages.
We find that the performance of our bilingual LM, RAMEN, correlates with the performance of the original pretrained English models.
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What does the performance of RAMEN correlate with?
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The performance of the original pretrained English models.
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Why is Stephen Curry the best player in the NBA?
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Stephen Curry is arguably the best player in the NBA today. Stephen Curry is a transcendent NBA superstar and an all-time great. Despite being relatively undersized by NBA standards at 6 foot 2 inches and 185 pounds, he has been a highly productive NBA player. Stephen Curry possesses the greatest 3-point shooting ability in NBA history. In 2022, he surpassed Ray Allen to become the all-time leader in 3-pointers made. That said, Stephen Curry's highly accurate 3-point shooting is not the only unique skill Curry brings to the court. He also possesses the uncanny ability to use off-ball movement, ball-handling skills, and a lightning fast release to create quality shots. The results of Stephen Curry's amazing skillset are evident through his career accolades. He is a 4-time NBA champion, 2-time MVP, and an NBA Finals MVP.
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Write a mother’s negative review for a dentist office with a dirty waiting room.
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Would not recommend!! I would give zero stars if I could. I took my children to this dentist to have their teeth cleaned and was absolutely disgusted by the state of the waiting room. The seats were stained and it looked like the floor hadn’t been cleaned in weeks. How hard it is to vacuum every now and then? I was afraid to let the kids touch anything and this is simply unacceptable for any kind of medical clinic. There are many other places in the area that keep their facility clean. Avoid this business!
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Although dogs are the most abundant and widely distributed terrestrial carnivores, feral and free-ranging dogs' potential to compete with other large carnivores is limited by their strong association with humans. For example, a review of the studies in dogs' competitive effects on sympatric carnivores did not mention any research on competition between dogs and wolves. Although wolves are known to kill dogs, they tend to live in pairs or in small packs in areas where they are highly persecuted, giving them a disadvantage facing large dog groups.
Wolves kill dogs wherever they are found together. In some instances, wolves have displayed an uncharacteristic fearlessness of humans and buildings when attacking dogs to the extent that they have to be beaten off or killed. Although the numbers of dogs killed each year are relatively low, it induces a fear of wolves entering villages and farmyards to take dogs and losses of dogs to wolves have led to demands for more liberal wolf hunting regulations.
Coyotes and big cats have also been known to attack dogs. In particular, leopards are known to have a preference for dogs and have been recorded to kill and consume them, no matter what their size. Siberian tigers in the Amur River region have killed dogs in the middle of villages. This indicates that the dogs were targeted. Amur tigers will not tolerate wolves as competitors within their territories, and the tigers could be considering dogs in the same way. Striped hyenas are known to kill dogs in their range.
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Please list the main dog predators according to the passage
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wolves, Coyotes, leopards, Siberian tigers, Striped hyenas
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2003.12932
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Let us discuss the results from the above mentioned experiments. We show the plots of accuracy vs noise for each of the tasks. For IMDB, we fine tune the model for the sentiment analysis task. We plot F1 score vs % of error, as shown in Figure FIGREF6. Figure FIGREF6imdba shows the performance after fine tuning for 10 epochs, while Figure FIGREF6imdbb shows the performance after fine tuning for 50 epochs.
Similarly, Figure FIGREF9ssta and Figure FIGREF9sstb) shows F1 score vs % of error for Sentiment analysis on SST-2 dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 3: F1 score vs % of error for Sentiment analysis on IMDB dataset
We show the plots of accuracy vs noise for each of the tasks. For IMDB, we fine tune the model for the sentiment analysis task. We plot F1 score vs % of error, as shown in Figure FIGREF6. Figure FIGREF6imdba shows the performance after fine tuning for 10 epochs, while Figure FIGREF6imdbb shows the performance after fine tuning for 50 epochs.
Similarly, Figure FIGREF9ssta and Figure FIGREF9sstb) shows F1 score vs % of error for Sentiment analysis on SST-2 dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 3: F1 score vs % of error for Sentiment analysis on IMDB dataset
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Which sentiment analysis data set has a larger performance drop when a 10% error is introduced?
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SST-2 dataset
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Minister for Food, Agriculture and Fisheries of Denmark (Danish: Minister for Fødevarer, Landbrug og Fiskeri) is the politically appointed head of the Danish Ministry of Food, Agriculture and Fisheries, and a member of the Cabinet. Since 19 November 2020, Rasmus Prehn, from the social democratic party Socialdemokratiet, has served as Minister for Food, Agriculture and Fisheries in the Mette Frederiksen I cabinet.
The ministry and the minister post was created in 1896; the ministry was originally named the Ministry of Agriculture. In 1929 the Directorate of Fishing was spun off into the separate Ministry of Shipping and Fishing, but that ministry was reabsorbed in 1935, and the name changed to Ministry of Agriculture and Fishing. In 1947 the Ministry of Fishing was created again, and the name changed to the Ministry of Agriculture, and this time it lasted until 1994 where it was again folded into the Ministry of Agriculture and Fishing. On 30 December 1996 the name of the ministry was changed to the current Ministry of Food, Agriculture and Fisheries, often just called the Ministry of Food.
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Given a reference text about Minister for Food, Agriculture and Fisheries of Denmark, when was the position created and was was it named?
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The Minister for Food, Agriculture and Fisheries of Denmark position was created in 1896 and was originally named the Ministry of Agriculture.
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1911.06192
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We first evaluate our model on MultiWOZ 2.0 dataset as shown in Table TABREF16. We compare with five published baselines. TRADE BIBREF3 is the current published state-of-the-art model. It utilizes an encoder-decoder architecture that takes dialogue contexts as source sentences, and takes state annotations as target sentences. SUMBT BIBREF17 fine-tunes a pre-trained BERT model BIBREF11 to learn slot and utterance representations. Neural Reading BIBREF18 learns a question embedding for each slot, and predicts the span of each slot value. GCE BIBREF7 is a model improved over GLAD BIBREF6 by using a slot-conditioned global module. Details about baselines are in Section SECREF6.
Table TABREF24 shows the results on WOZ $2.0$ dataset. We compare with four published baselines. SUMBT BIBREF17 is the current state-of-the-art model on WOZ 2.0 dataset. It fine-tunes a pre-trained BERT model BIBREF11 to learn slot and utterance representations. StateNet PSI BIBREF5 maps contextualized slot embeddings and value embeddings into the same vector space, and calculate the Euclidean distance between these two. It also learns a joint model of all slots, enabling parameter sharing between slots. GLAD BIBREF6 proposes to use a global module to share parameters between slots and a local module to learn slot-specific features. Neural Beflief Tracker BIBREF4 applies CNN to learn n-gram utterance representations. Unlike prior works that transfer knowledge between slots by sharing parameters, our model implicitly transfers knowledge by formulating each slot as a question and learning to answer all the questions. Our model has a $1.24\%$ relative joint accuracy improvement over StateNet PSI. Although SUMBT achieves higher joint accuracy than DSTQA on WOZ $2.0$ dataset, DSTQA achieves better performance than SUMBT on MultiWOZ $2.0$ dataset, which is a more challenging dataset.
We first evaluate our model on MultiWOZ 2.0 dataset as shown in Table TABREF16. We compare with five published baselines. TRADE BIBREF3 is the current published state-of-the-art model.
Table TABREF24 shows the results on WOZ $2.0$ dataset. We compare with four published baselines. SUMBT BIBREF17 is the current state-of-the-art model on WOZ 2.0 dataset.
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What is current state-of-the-art model?
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The answers are shown as follows:
* SUMBT BIBREF17 is the current state-of-the-art model on WOZ 2.0
* TRADE BIBREF3 is the current published state-of-the-art model
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To assess the performance of binary paragraph vectors, we carried out experiments on three datasets: 20 Newsgroups, a cleansed version (also called v2) of Reuters Corpus Volume 1 (RCV1) and English Wikipedia. As paragraph vectors can be trained with relatively large vocabularies, we did not perform any stemming of the source text. However, we removed stop words as well as words shorter than two characters and longer than 15 characters. Results reported by BIBREF15 indicate that performance of PV-DBOW can be improved by including n-grams in the model. We therefore evaluated two variants of Binary PV-DBOW: one predicting words in documents and one predicting words and bigrams. Since 20 Newsgroups is a relatively small dataset, we used all words and bigrams from its documents. This amounts to a vocabulary with slightly over one million elements. For the RCV1 dataset we used words and bigrams with at least 10 occurrences in the text, which gives a vocabulary with approximately 800 thousands elements. In case of English Wikipedia we used words and bigrams with at least 100 occurrences, which gives a vocabulary with approximately 1.5 million elements.
The 20 Newsgroups dataset comes with reference train/test sets. In case of RCV1 we used half of the documents for training and the other half for evaluation. In case of English Wikipedia we held out for testing randomly selected 10% of the documents. We perform document retrieval by selecting queries from the test set and ordering other test documents according to the similarity of the inferred codes. We use Hamming distance for binary codes and cosine similarity for real-valued representations. Results are averaged over queries. We assess the performance of our models with precision-recall curves and two popular information retrieval metrics, namely mean average precision (MAP) and the normalized discounted cumulative gain at the 10th result (NDCG@10) BIBREF16 . The results depend, of course, on the chosen document relevancy measure. Relevancy measure for the 20 Newsgroups dataset is straightforward: a retrieved document is relevant to the query if they both belong to the same newsgroup. In RCV1 each document belongs to a hierarchy of topics, making the definition of relevancy less obvious. In this case we adopted the relevancy measure used by BIBREF3 . That is, the relevancy is calculated as the fraction of overlapping labels in a retrieved document and the query document. Overall, our selection of test datasets and relevancy measures for 20 Newsgroups and RCV1 follows BIBREF3 , enabling comparison with semantic hashing codes. To assess the relevancy of articles in English Wikipedia we can employ categories assigned to them. However, unlike in RCV1, Wikipedia categories can have multiple parent categories and cyclic dependencies. Therefore, for this dataset we adopted a simplified relevancy measure: two articles are relevant if they share at least one category. We also removed from the test set categories with less than 20 documents as well as documents that were left with no categories. Overall, the relevancy is measured over more than INLINEFORM0 categories, making English Wikipedia harder than the other two benchmarks.
We use AdaGrad BIBREF17 for training and inference in all experiments reported in this work. During training we employ dropout BIBREF18 in the embedding layer. To facilitate models with large vocabularies, we approximate the gradients with respect to the softmax logits using the method described by BIBREF9 . Binary PV-DM networks use the same number of dimensions for document codes and word embeddings.
Performance of 128- and 32-bit binary paragraph vector codes is reported in Table TABREF8 and in Figure FIGREF7 . For comparison we also report performance of real-valued paragraph vectors. Note that the binary codes perform very well, despite their far lower capacity: on 20 Newsgroups and RCV1 the 128-bit Binary PV-DBOW trained with bigrams approaches the performance of the real-valued paragraph vectors, while on English Wikipedia its performance is slightly lower. Furthermore, Binary PV-DBOW with bigrams outperforms semantic hashing codes: comparison of precision-recall curves from Figures FIGREF7 a and FIGREF7 b with BIBREF3 shows that 128-bit codes learned with this model outperform 128-bit semantic hashing codes on 20 Newsgroups and RCV1. Moreover, the 32-bit codes from this model outperform 128-bit semantic hashing codes on the RCV1 dataset, and on the 20 Newsgroups dataset give similar precision up to approximately 3% recall and better precision for higher recall levels. Note that the difference in this case lies not only in retrieval precision: the short 32-bit Binary PV-DBOW codes are more efficient for indexing than long 128-bit semantic hashing codes.
We also compared binary paragraph vectors against codes constructed by first inferring short, real-valued paragraph vectors and then using a separate hashing algorithm for binarization. When the dimensionality of the paragraph vectors is equal to the size of binary codes, the number of network parameters in this approach is similar to that of Binary PV models. We experimented with two standard hashing algorithms, namely random hyperplane projection BIBREF19 and iterative quantization BIBREF20 . Paragraph vectors in these experiments were inferred using PV-DBOW with bigrams. Results reported in Table TABREF9 show no benefit from using a separate algorithm for binarization. On the 20 Newsgroups and RCV1 datasets Binary PV-DBOW yielded higher MAP than the two baseline approaches. On English Wikipedia iterative quantization achieved MAP equal to Binary PV-DBOW, while random hyperplane projection yielded lower MAP. Some gain in precision of top hits can be observed for iterative quantization, as indicated by NDCG@10. However, precision of top hits can also be improved by querying with Real-Binary PV-DBOW model (Section SECREF15 ). It is also worth noting that end-to-end inference in Binary PV models is more convenient than inferring real-valued vectors and then using another algorithm for hashing.
BIBREF15 argue that PV-DBOW outperforms PV-DM on a sentiment classification task, and demonstrate that the performance of PV-DBOW can be improved by including bigrams in the vocabulary. We observed similar results with Binary PV models. That is, including bigrams in the vocabulary usually improved retrieval precision. Also, codes learned with Binary PV-DBOW provided higher retrieval precision than Binary PV-DM codes. Furthermore, to choose the context size for the Binary PV-DM models, we evaluated several networks on validation sets taken out of the training data. The best results were obtained with a minimal one-word, one-sided context window. This is the distributed memory architecture most similar to the Binary PV-DBOW model.
Results reported in Table 2 show no benefit from using a separate algorithm for binarization.
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Is there any benefit from using a separate algorithm for binarization?
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No, there isn't.
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Why is Pickleball sport so popular
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Pickleball is so popular because anyone can get started fairly easily, as the equipment is not expensive, and there are lot of freely available public courts to play. It is also an easy sport to quickly pick up and able to play at a decent level recreationally and competitively.
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Rafael Nadal Parera (Catalan: [rəf(ə)ˈɛl nəˈðal pəˈɾeɾə], Spanish: [rafaˈel naˈðal paˈɾeɾa]; born 3 June 1986) is a Spanish professional tennis player. He is currently ranked world No. 14 in singles by the Association of Tennis Professionals (ATP). He has been ranked world No. 1 for 209 weeks, and has finished as the year-end No. 1 five times. He also holds the record for most consecutive weeks in the Top 10 of the ATP Rankings, having never left the Top 10 from April 2005 to March 2023, a total of 912 weeks. Nadal has won a joint-record 22 Grand Slam men's singles titles, including a record 14 French Open titles. He has won 92 ATP singles titles, including 36 Masters titles, with 63 of these on clay courts. Nadal is one of only two men to complete the Career Golden Slam in singles. His 81 consecutive wins on clay is the longest single-surface win streak in the Open Era.
For over a decade, Nadal has dominated men's tennis along with Roger Federer and Novak Djokovic as the Big Three, collectively considered by many to be the three greatest male tennis players of all time. At the start of his professional career, Nadal became one of the most successful teenagers in ATP Tour history, reaching the world No. 2 ranking and winning 16 titles before turning 20, including his first French Open and six Masters events. Nadal became the world No. 1 for the first time in 2008 after defeating Federer in a historic Wimbledon final, his first major victory off clay. He followed up his win with an Olympic singles gold at the 2008 Beijing Olympics. After defeating Djokovic in the 2010 US Open final, then-24-year-old Nadal became the youngest man in the Open Era to achieve the Career Grand Slam, and the first man to win majors on three different surfaces (hard, grass, and clay) in the same year (Surface Slam).
After two injury-plagued seasons, Nadal made a stellar return in one of the greatest comeback seasons of all time in 2013; reaching 14 finals, winning two majors and five Masters events including the US Open Series sweep (Summer Slam). He continued his dominance at the French Open, securing six titles, two US Open titles, an Australian Open title, and an Olympic doubles gold at the 2016 Rio Olympics with Marc López. Nadal surpassed his joint-record with Djokovic and Federer for the most major men's singles titles at the 2022 Australian Open, and became one of four men in history to complete the double Career Grand Slam in singles. He appeared in the Top 10 of the ATP rankings continuously from April 2005 to March 2023 – a record spanning 18 years.
As a vigorous left-handed player, one of Nadal's main strengths is his forehand, which he hits with extremely heavy topspin at difficult angles. He is one of the best at breaking serve, regularly appearing among the tour leaders in percentage of return games, return points, and break points won. Nadal has won the Stefan Edberg Sportsmanship Award five times, and was the Laureus World Sportsman of the Year in 2011 and 2021. He is also a recipient of the Grand Cross of the Order of Dos De Mayo, the Grand Cross of Naval Merit, and the Medal of the City of Paris. Representing Spain, he has won two Olympic gold medals, and led the nation to four Davis Cup titles. Nadal has also opened a tennis academy in Mallorca, and is an active philanthropist.
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Given the reference text about Rafael Nadal, tell me how many singles grand slam titles he has won, which grand slam tournament he has won the most, and the main strength of his tennis game.
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Rafael Nadal has won 22 singles grand slam titles. The majority of his grand slam wins have been at the French Open, where he has won 14 times.
The main strength of his tennis game is his forehand because of his ability to hit the ball with extremely heavy topspin at difficult angles.
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Prior work has suggested that persistent learning is the most effective method for learning a valid EBM density. Furthermore, works that learn a valid density have avoided rejuvenation because the incorporation of newly rejuvenated samples into the persistent bank ensures that EBM updates will always include samples that are not at the steady-state. However, persistent learning without rejuvenation has shortcomings mentioned in Section 3. We present hypothesized conditions for learning a valid density that motivate the design of our MCMC initialization:
• After a certain point in training, all samples used to update the EBM must be approximate steadystate samples of the current model p(x; θ). • Persistent samples that are newly rejuvenated (up to about 50K Langevin steps since rejuvenation, and possibly many more) cannot be approximate steady-state samples for any known rejuvenation sources, including data, generators, and noise. • Persistent samples that have undergone sufficiently many lifetime Langevin updates for a model whose weights are changing very slowly can be approximate steady-state samples.
Figure: Visualization of our longrun initializatin procedure. Newly rejuvenated samples must remain the the burnin bank until they have approach the model steady-state, at which point they move to the update bank to be used for model gradients.
Both the second and third point are corroborated by prior work as well as our own observations. The third point means that persistent states updated with shortrun Langevin can eventually act as longrun Langevin samples if the optimizer learning rate is small, because the EBM samples in previous timesteps are essentially samples from the current EBM.
Learning a valid density that includes rejuvenation while satisfying the conditions above requires separating the newly rejuvenated samples from samples that are used to update the EBM. This leads us to introduce two persistent image banks: one for newly rejuvenated samples, and one for samples that will be used to update the EBM. Samples in the newly rejuvenated bank that have been updated sufficiently many times will eventually replace samples from the bank used to update the EBM, at which point newly rejuvenated states will be added to the burn-in bank. Figure shows a visualization of the MCMC initialization method. As in Section 3, we will use a pre-trained generator to efficiently obtain high-quality rejuvenated samples so that the generation process is fully synthetic, although data samples could be used as well. Our goal is to preserve the sample quality for an arbitrary number of MCMC steps. We note that this is a sufficient but not necessary condition for learning a valid EBM density. Nonetheless, stable sampling is an important step towards rigorous probabilistic EBMs. Even with our improved initialization, we find that extremely longrun trajectories of 1 million or more MCMC steps still tend to oversaturate, although to a much lesser degree. To further stabilize the appearance of extremely longrun Langevin samples, we include prior energy terms in the model. Our longrun EBMs have the form
where U (x; θ) is the model whose weights are updated, U 0 (x; θ 0 ) is a prior EBM with fixed weights θ 0 and σ is a parameter controlling the strength of a Gaussian prior. We used a prior EBM in a shortrun manner. The role of the prior EBM is to provide some stability but also to provide a tendency to oversaturate at longer trajectories so that the current EBM learns to correct oversaturation. The Gaussian prior is meant to discourage unbounded activations outside of the image hypercube. Further discussion is in Appendix H. We find that including both of these terms significantly improves the ability to learn quality synthesis over long trajectories.
Table 4: FID for 5K samples after 100K Langevin and 1M Langevin steps. FID remains stable over long trajectories.****Figure 13: Longrun samples at 100,000 steps and extremely longrun samples at 1 million steps
for EBMs trained on three datasets. Our initialization is able to preserve a high degree of realism over the first 100K steps, and a reasonable degree of realism over very long trajectories. While oversaturation and distortion is noticeable for some samples using 1M steps, many samples have reasonable appearance and there is high diversity. Our method makes significant progress towards aligning longrun samples with high-quality samples from training to ensure that the model is a valid
density
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Where is the density estimation result for longrun EBM?
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Our density estimation results are the longrun FID scores in Table 4 and visualizations of longrun samples in Figure 11. Please refer to the general statement for our explanation of why we believe these are an appropriate evaluation to investigate density modeling.
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What are the most important metrics for a Sales Leader?
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Pipeline, Pipeline Coverage, Salescycle time, conversion rate, MQOs, SQOs, MQLs, Quota Attainment %
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In order to further investigate the robustness of GraphANGEL, we randomly generate some noises and add them into the training graphs. Concretely, for heterogeneous graphs and knowledge graphs shown in Tables A1 and A2, we randomly change the source or target node of each triplet h s, r, ti with probability 5%. Since the heterogeneous graphs have the node types, our changes are made by replacing the current source or target node with a randomly sampled node in the same node type. We compare our model with all the baselines and report the results for heterogeneous graph recommendation and knowledge graph completion tasks in Tables A13 and A14. These results show that our method can consistently outperform these baseline methods, sometimes, even are less inflfluenced by the noises. One explanation is that if we introduce noises in the labels y, then the learnable subgraph embeddings s+ and s− will simultaneously be inflfluenced by the noises. These inflfluences might be counteracted according to Eq. (3).
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What about the graphs with noise since the majority of the real-world graphs have errors or noise?
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We devised the noisy graphs by modifying the source or target nodes with probability 5%, and compared GraphANGEL to baselines on the graphs. Results reported in Tables A12 and A13 show the superiority of GraphANGEL against all the baselines in the context of noisy graphs in both heterogeneous graph recommendation and knowledge graph completion tasks. Please see Appendix 6.5 for details.
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The 2021 Valletta Cup was a Twenty20 International (T20I) cricket tournament held in Malta between 21 and 24 October 2021. It was the second edition of the Valletta Cup. The matches were played at the Marsa Sports Club in Marsa. The participating teams were the hosts Malta along with Bulgaria, Gibraltar and Switzerland.
This was the first international tournament for Switzerland since the introduction of global T20I status in January 2019, and their re-admittance to the International Cricket Council (ICC) in July 2021. Cricket Switzerland (previously known as the Swiss Cricket Association), had been suspended by the ICC in 2012 due to non-compliance following the formation of a second organization also claiming to oversee cricket in the country. They were one of three nations to gain Associate membership after the ICC's annual general meeting in July 2021.
Switzerland finished on top of the round-robin stage with three wins. Malta defeated Switzerland in the final, after Bulgaria defeated Gibraltar in the third-place playoff. After the tournament, Malta and Gibraltar played a rain-affected two-match bilateral T20I series that was shared after one game was lost to the weather and the other finished as a DLS method tie.
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What teams were a part of the 2021 Valletta Cup?
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Malta, Bulgaria, Gibraltar & Switzerland were of the participating teams in the 2021 Valletta Cup.
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1904.06941
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To assess the predictive capability of this and other models, we require some method by which we can compare the models. For that purpose, we use receiver operating characteristic (ROC) curves as a visual representation of predictive effectiveness. ROC curves compare the true positive rate (TPR) and false positive rate (FPR) of a model's predictions at different threshold levels. The area under the curve (AUC) (between 0 and 1) is a numerical measure, where the higher the AUC is, the better the model performs.
We cross-validate our model by first randomly splitting the corpus into a training set (95% of the corpus) and test set (5% of the corpus). We then fit the model to the training set, and use it to predict the response of the documents in the test set. We repeat this process 100 times. The threshold-averaged ROC curve BIBREF13 is found from these predictions, and shown in Figure 3 . Table 1 shows the AUC for each model considered.
To assess the predictive capability of this and other models, we require some method by which we can compare the models. For that purpose, we use receiver operating characteristic (ROC) curves as a visual representation of predictive effectiveness. ROC curves compare the true positive rate (TPR) and false positive rate (FPR) of a model's predictions at different threshold levels. The area under the curve (AUC) (between 0 and 1) is a numerical measure, where the higher the AUC is, the better the model performs.
We cross-validate our model by first randomly splitting the corpus into a training set (95% of the corpus) and test set (5% of the corpus). We then fit the model to the training set, and use it to predict the response of the documents in the test set. We repeat this process 100 times. The threshold-averaged ROC curve BIBREF13 is found from these predictions, and shown in Figure 3 . Table 1 shows the AUC for each model considered.
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How is performance measured?
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they use ROC curves and cross-validation
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Tell me whether these are movies about AI or other topics: , "Ex Machina", "Her", "Transcendence", "I, Robot", "Ghost in the Shell", "The Godfather", "Pulp Fiction", "The Shawshank Redemption", "The Dark Knight", "12 Angry Men"
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Out of the ten films you mentioned, only "Ex Machina" , "Her", "Transcendence", "I, Robot" and "Ghost in the Shell" are about AI.
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Name different corporate messaging applications companies use.
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Corporate messaging applications companies use include Slack, Teams, and Google Chat.
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1808.07625
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Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable:
It is impractical to rely solely on a neural decoder to find the most likely logical form at run time in the weakly-supervised setting. One reason is that although the decoder utilizes global utterance features for generation, it cannot leverage global features of the logical form since a logical form is conditionally generated following a specific tree-traversal order. To this end, we follow previous work BIBREF21 and introduce a ranker to the system. The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ :
Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable
The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ :
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How does the model compute the likelihood of executing to the correction semantic denotation?
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By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.
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The Bud Freeman All-Stars featuring Shorty Baker
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AllMusic reviewer Scott Yanow stated: "Tenor-sax great Bud Freeman, who is often associated with the Eddie Condon school of Nicksieland, is heard heading an excellent swing quintet for this 1960 studio session. Trumpeter Harold "Shorty" Baker (best known for his periods with Duke Ellington) made too few small-group recordings throughout his life so this is one of his best ... the group plays superior standards and a couple of originals on this fine swing date"
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Skarsgård was born on 25 August 1976 in Stockholm, Sweden. He is the eldest son of actor Stellan Skarsgård and physician My Skarsgård. His parents are divorced. He has five younger siblings: Gustaf, Sam, Bill, Eija and Valter, and two half-brothers from his father's second wife, Megan Everett. Gustaf, Bill and Valter are also actors.
A friend of his father, a director, gave Skarsgård his first film role when he was seven years old, playing Kalle Nubb in Åke and His World (Åke och hans värld). In 1989, his lead role in the Swedish television production Hunden som log (The Dog That Smiled) earned him praise and brought him to prominence in Sweden at age 13. Uncomfortable with the fame, he quit acting at that same age.
At age 19, Skarsgård applied to perform his national service. He served in the Swedish Armed Forces, in the SäkJakt ("protect and hunt") unit that dealt with anti-sabotage and anti-terrorism in the Stockholm archipelago, for 18 months. After completing his service in 1996, he left Sweden and attended Leeds Metropolitan University in England for six months. He enrolled to study English but admits he did not study much and "had a blast" instead. While there, he considered pursuing an architecture career, but chose acting instead. In 1997, he enrolled in a theatre course at Marymount Manhattan College and moved to New York City. He returned to Stockholm after six months, but his time studying theatre confirmed to him that he wanted to act.
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Was Alexander Skarsgård destined to become an actor?
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Alexander Skarsgård was born into an actors family, his father Stellan as well as several of his brothers are successful actors. Furthermore, he got off to an early start when a friend of his father got him a role at seven years old, which seemed to suggest he was always destined to become an actor. However, along the way, several diversions could have set him on another path. At 13 years old he quit acting as he got uncomfortable with the fame. Later on, he joined the Swedish national service, went off to study English in Leeds, and considered pursuing an architecture career. But in the end, he chooses the path of acting.
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1908.05763
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In this section, we analyze the Hamming distance between the projections of the sentences from the enwik9 dataset and the corresponding projections of the same sentences after applying character level perturbations. We experiment with three types of character level perturbation BIBREF11 and two types of word level perturbation operations.
Perturbation Study ::: Character Level Perturbation Operations
insert(word, n) : We randomly choose n characters from the character vocabulary and insert them at random locations into the input word. We however retain the first and last characters of the word as is. Ex. transformation: $sample \rightarrow samnple$.
swap(word, n): We randomly swap the location of two characters in the word n times. As with the insert operation, we retain the first and last characters of the word as is and only apply the swap operation to the remaining characters. Ex. transformation: $sample \rightarrow sapmle$.
duplicate(word, n): We randomly duplicate a character in the word by n times. Ex. transformation: $sample \rightarrow saample$.
Perturbation Study ::: Character Level Perturbation Operations ::: Word Level Perturbation Operations
drop(sentence, n): We randomly drop n words from the sentence. Ex. transformation: This is a big cat. $\rightarrow $ This is a cat.
duplicate(sentence, n): Similar to duplicate(word, n) above, we randomly duplicate a word in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This is a big big cat.
swap(sentence, n): Similar to swap(word, n), we randomly swap the location of two words in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This cat is big.
In this section, we analyze the Hamming distance between the projections of the sentences from the enwik9 dataset and the corresponding projections of the same sentences after applying character level perturbations. We experiment with three types of character level perturbation BIBREF11 and two types of word level perturbation operations.
Perturbation Study ::: Character Level Perturbation Operations
insert(word, n) : We randomly choose n characters from the character vocabulary and insert them at random locations into the input word. We however retain the first and last characters of the word as is. Ex. transformation: $sample \rightarrow samnple$.
swap(word, n): We randomly swap the location of two characters in the word n times. As with the insert operation, we retain the first and last characters of the word as is and only apply the swap operation to the remaining characters. Ex. transformation: $sample \rightarrow sapmle$.
duplicate(word, n): We randomly duplicate a character in the word by n times. Ex. transformation: $sample \rightarrow saample$.
Perturbation Study ::: Character Level Perturbation Operations ::: Word Level Perturbation Operations
drop(sentence, n): We randomly drop n words from the sentence. Ex. transformation: This is a big cat. $\rightarrow $ This is a cat.
duplicate(sentence, n): Similar to duplicate(word, n) above, we randomly duplicate a word in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This is a big big cat.
swap(sentence, n): Similar to swap(word, n), we randomly swap the location of two words in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This cat is big.
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How does their perturbation algorihm work?
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The answers are shown as follows:
* same sentences after applying character level perturbations
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Lyndon Baines Johnson (/ˈlɪndən ˈbeɪnz/; August 27, 1908 – January 22, 1973), often referred to by his initials LBJ, was an American politician who served as the 36th president of the United States from 1963 to 1969. He previously served as the 37th vice president from 1961 to 1963 under President John F. Kennedy, and was sworn in shortly after Kennedy's assassination. A Democrat from Texas, Johnson also served as a U.S. representative, U.S. Senator and the Senate's majority leader. He holds the distinction of being one of the few presidents who served in all elected offices at the federal level.
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Who is LBJ?
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Lyndon Baines Johnson (/ˈlɪndən ˈbeɪnz/; August 27, 1908 – January 22, 1973), often referred to by his initials LBJ, was an American politician who served as the 36th president of the United States from 1963 to 1969. He previously served as the 37th vice president from 1961 to 1963 under President John F. Kennedy, and was sworn in shortly after Kennedy's assassination. A Democrat from Texas, Johnson also served as a U.S. representative, U.S. Senator and the Senate's majority leader. He holds the distinction of being one of the few presidents who served in all elected offices at the federal level.
Born in a farmhouse in Stonewall, Texas, to a local political family, Johnson worked as a high school teacher and a congressional aide before winning election to the U.S. House of Representatives in 1937. In 1948, he won the 1948 Senate election in Texas after a narrow and controversial victory in the Democratic Party's primary. He was appointed to the position of Senate Majority Whip in 1951. He became the Senate Democratic leader in 1953 and majority leader in 1954. In 1960 Johnson ran for the Democratic nomination for president. Ultimately, Senator Kennedy bested Johnson and his other rivals for the nomination, then surprised many by offering to make Johnson his vice presidential running mate. The Kennedy-Johnson ticket won in the 1960 presidential election. Vice President Johnson assumed the presidency on November 22, 1963, after President Kennedy was assassinated. The following year Johnson was elected to the presidency when he won in a landslide against Arizona Senator Barry Goldwater, receiving 61.1% of the popular vote in the 1964 presidential election, the largest share won by any presidential candidate since the 1820 election.
Johnson's domestic policy was aimed at expanding civil rights, public broadcasting, access to healthcare, aid to education and the arts, urban and rural development, and public services. In 1964 Johnson coined the term the "Great Society" to describe these efforts. In addition, he sought to create better living conditions for low-income Americans by spearheading a campaign unofficially called the "War on Poverty". As part of these efforts, Johnson signed the Social Security Amendments of 1965, which resulted in the creation of Medicare and Medicaid. Johnson followed his predecessor's actions in bolstering NASA and made the Apollo Program a national priority. He enacted the Higher Education Act of 1965 which established federally insured student loans. Johnson signed the Immigration and Nationality Act of 1965 which laid the groundwork for U.S. immigration policy today. Johnson's opinion on the issue of civil rights put him at odds with other white, southern Democrats. His civil rights legacy was shaped by signing the Civil Rights Act of 1964, the Voting Rights Act of 1965, and the Civil Rights Act of 1968. During his presidency, the American political landscape transformed significantly, as white southerners who were once staunch Democrats began moving to the Republican Party and black voters began moving to the Democratic Party. Because of his domestic agenda, Johnson's presidency marked the peak of modern liberalism in the United States.
Johnson's presidency took place during the Cold War, thus his foreign policy prioritized containment of communism. Prior to his presidency, the U.S. was already involved in the Vietnam War, supporting South Vietnam against the communist North. Following a naval skirmish in 1964 between the United States and North Vietnam, Congress passed the Gulf of Tonkin Resolution, which granted Johnson the power to launch a full-scale military intervention in South East Asia. The number of American military personnel in Vietnam increased dramatically, and casualties soared among U.S. soldiers and Vietnamese civilians. Johnson also expanded military operations in neighboring Laos to destroy North Vietnamese supply lines. In 1968, the communist Tet Offensive inflamed the anti-war movement, especially among draft-age students on university campuses, and public opinion turned against America's involvement in the war. In Europe, Johnson's administration continued to promote and foster Western European political and economic cooperation and integration as his predecessors had.
At home, Johnson faced further troubles with race riots in major cities and increasing crime rates. His political opponents seized the opportunity and raised demands for "law and order" policies. Johnson began his presidency with near-universal support, but his approval declined throughout his presidency as the public became frustrated with both the Vietnam War and domestic unrest. Johnson initially sought to run for re-election; however, following disappointing results in the New Hampshire primary he withdrew his candidacy. The war was a major election issue and the 1968 presidential election saw Republican candidate Richard Nixon defeat Johnson's vice president Hubert Humphrey. At the end of his presidency in 1969, Johnson returned to his Texas ranch, published his memoirs, and in other respects kept a low profile until he died of a heart attack in 1973.
Johnson is one of the most controversial presidents in American history. Public opinion and academic assessments of his legacy have fluctuated greatly ever since his death. Historians and scholars rank Johnson in the upper tier because of his accomplishments regarding domestic policy. His administration passed many major laws that made substantial changes in civil rights, health care, welfare, and education. Conversely, Johnson is strongly criticized for his foreign policy, namely escalating American involvement in the Vietnam War.
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What is Beggar's Chicken?
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In Chinese, Beggar’s Chicken is called “叫化鸡” (Jiào huā jī). Made with a whole chicken, the bird is marinated, seasoned, and then stuffed, after which it is wrapped in lotus leaves, then coated in clay, and finally baked.
The best part of this recipe is that when it has finished cooking, you take a hammer and crack the clay to reveal a perfectly cooked chicken inside.
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After being expelled from Malaysia, Singapore became independent as the Republic of Singapore on 9 August 1965, with Lee Kuan Yew and Yusof bin Ishak as the first prime minister and president respectively. In 1967, the country co-founded the Association of Southeast Asian Nations (ASEAN). Race riots broke out once more in 1969. Lee Kuan Yew's emphasis on rapid economic growth, support for business entrepreneurship, and limitations on internal democracy shaped Singapore's policies for the next half-century. Economic growth continued throughout the 1980s, with the unemployment rate falling to 3% and real GDP growth averaging at about 8% up until 1999. During the 1980s, Singapore began to shift towards high-tech industries, such as the wafer fabrication sector, in order to remain competitive as neighbouring countries began manufacturing with cheaper labour. Singapore Changi Airport was opened in 1981 and Singapore Airlines was formed. The Port of Singapore became one of the world's busiest ports and the service and tourism industries also grew immensely during this period.
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When did Singapore gain independence, and who was the first President?
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Singapore became independent on 9 August 1965, and the first president was Yusof bin Ishak.
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Fidel Alejandro Castro Ruz (/ˈkæstroʊ/; American Spanish: [fiˈðel aleˈxandɾo ˈkastɾo ˈrus]; 13 August 1926 – 25 November 2016) was a Cuban revolutionary and politician who was the leader of Cuba from 1959 to 2008, serving as the prime minister of Cuba from 1959 to 1976 and president from 1976 to 2008. Ideologically a Marxist–Leninist and Cuban nationalist, he also served as the first secretary of the Communist Party of Cuba from 1961 until 2011. Under his administration, Cuba became a one-party communist state; industry and business were nationalized, and socialist reforms were implemented throughout society.
Born in Birán, the son of a wealthy Spanish farmer, Castro adopted leftist and anti-imperialist ideas while studying law at the University of Havana. After participating in rebellions against right-wing governments in the Dominican Republic and Colombia, he planned the overthrow of Cuban President Fulgencio Batista, launching a failed attack on the Moncada Barracks in 1953. After a year's imprisonment, Castro travelled to Mexico where he formed a revolutionary group, the 26th of July Movement, with his brother Raúl Castro and Ernesto "Che" Guevara. Returning to Cuba, Castro took a key role in the Cuban Revolution by leading the Movement in a guerrilla war against Batista's forces from the Sierra Maestra. After Batista's overthrow in 1959, Castro assumed military and political power as Cuba's prime minister. The United States came to oppose Castro's government and unsuccessfully attempted to remove him by assassination, economic embargo, and counter-revolution, including the Bay of Pigs Invasion of 1961. Countering these threats, Castro aligned with the Soviet Union and allowed the Soviets to place nuclear weapons in Cuba, resulting in the Cuban Missile Crisis – a defining incident of the Cold War – in 1962.
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Given the reference text about Fidel Castro, tell me where he was born, where he studied, and when he was the leader of Cuba.
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Fidel Castro was born in Birán and studied law at the University of Havana. He was the leader of Cuba from 1959 to 2008.
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Identify which instrument is string or percussion: Monkey stick, Tzouras
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Monkey stick is percussion, Tzouras is string.
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The Sound of Music is a musical with music by Richard Rodgers, lyrics by Oscar Hammerstein II, and a book by Howard Lindsay and Russel Crouse. It is based on the 1949 memoir of Maria von Trapp, The Story of the Trapp Family Singers. Set in Austria on the eve of the Anschluss in 1938, the musical tells the story of Maria, who takes a job as governess to a large family while she decides whether to become a nun. She falls in love with the children, and eventually their widowed father, Captain von Trapp. He is ordered to accept a commission in the German navy, but he opposes the Nazis. He and Maria decide on a plan to flee Austria with the children
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Whose life is the 1965 film 'The Sound of Music' based on?
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It is based on the 1949 memoir of Maria von Trapp
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The Verve were an English rock band formed in Wigan, Greater Manchester in 1990 by lead vocalist Richard Ashcroft, guitarist Nick McCabe, bass guitarist Simon Jones and drummer Peter Salisbury. Guitarist and keyboard player Simon Tong later became a member in their first reunion only.
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Who was the guitarist and drummer of The Verve?
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Nick McCabe and Peter Salisbury
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William P. Bolton (July 2, 1885 – November 22, 1964) was a one-term U.S. Congressman who represented the second district of Maryland from 1949 to 1951 until defeated by Republican General James Devereux.
Born near Whiteford, Maryland, Bolton attended the public schools and St. Francis Parochial School in Baltimore County, Maryland. He graduated from the University of Baltimore Law School in 1909, and became a lawyer in private practice. He served as trial magistrate in Towson, Maryland from 1941-1946.
Bolton served in the Maryland State Senate from 1946 to 1948. He was elected as a Democrat to the Eighty-first Congress, serving from January 3, 1949 to January 3, 1951. He was an unsuccessful candidate for reelection to the Eighty-second Congress in 1950 and again in 1954.
During his short time in the Maryland Senate he proposed a far reaching amendment to the Maryland Constitution that was approved by the city of Baltimore voters to limit the city's ability to annex portions of Baltimore County, something that was previously a right of the city. This amendment, Question 5, was approved on November 2, 1948 by a majority of the city voters: 139,974 to 103,687.
After Congress, Bolton served as director of Baltimore County Civil Defense in 1951, and died in Baltimore in 1964. He is interred in Mount Maria Cemetery of Towson.
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Who was William P. Bolton
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William P. Bolton (July 2, 1885 – November 22, 1964) was a one-term U.S. Congressman who represented the second district of Maryland from 1949 to 1951 until defeated by Republican General James Devereux.
After Congress, Bolton served as director of Baltimore County Civil Defense in 1951, and died in Baltimore in 1964. He is interred in Mount Maria Cemetery of Towson.
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The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English.
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What language is involved in the research of this article?
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Bengali.
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The BLEU scores in Table TABREF30 show a moderate improvement for most context-aware systems. This suggests that the architectural changes for the context-aware models do not degrade overall translation quality. The contrastive evaluation on our test set on the other hand shows a clear increase in the accuracy of pronoun translation: The best model s-hier-to-2.tied achieves a total of +16 percentage points accuracy on the test set over the baseline, see Table TABREF31 .
Table TABREF32 shows that context-aware models perform better than the baseline when the antecedent is outside the current sentence. In our experiments, all context-aware models consider one preceding sentence as context. The evaluation according to the distance of the antecedent in Table TABREF35 confirms that the subset of sentences with antecedent distance 1 benefits most from the tested context-aware models (up to +20 percentage points accuracy). However, we note two surprising patterns:
The first observation can be explained by the distribution of German pronouns in the test set. The further away the antecedent, the higher the percentage of it INLINEFORM0 es cases, which are the majority class, and thus the class that will be predicted most often if evidence for other classes is lacking. We speculate that this is due to our more permissive extraction heuristics for it INLINEFORM1 es.
We attribute the second observation to the existence of coreference chains where the preceding sentence contains a pronoun that refers to the same nominal antecedent as the pronoun in the current sentence. Consider the example in Table TABREF36 : The nominal antecedent of it in the current sentence is door, Tür in German with feminine gender. The nominal antecedent occurs two sentences before the current sentence, but the German sentence in between contains the pronoun sie, which is a useful signal for the context-aware models, even though they cannot know the nominal antecedent.
Note that only models aware of target-side context can benefit from such circumstances: The s-hier models as well as the Transformer model by BIBREF8 only see source side context, which results in lower accuracy if the distance to the antecedent is INLINEFORM0 1, see Table TABREF35 .
While such coreference chains complicate the interpretation of the results, we note that improvements on inter-sentential anaphora with antecedent distance INLINEFORM0 are relatively small (compared to distance 1), and that performance is still relatively poor (especially for the minority classes er and sie). We encourage evaluation of wider-context models on this subset, which is still large thanks to the size of the full test set.
Regarding the comparison of different context-aware architectures, our results demonstrate the effectiveness of parameter sharing between the main encoder (or decoder) and the contextual encoder. We observe an improvement of 5 percentage points from s-hier-to-2 to s-hier-to-2.tied, and 4 percentage points from s-t-hier to s-t-hier.tied. Context encoders introduce a large number of extra parameters, while inter-sentential context is only relevant for a relatively small number of predictions. We hypothesize that the training signal is thus too weak to train a strong contextual encoder in an end-to-end fashion without parameter sharing. Our results also confirm the finding by BIBREF9 that multi-encoder architectures, specifically s-hier-to-2(.tied), can outperform a simple concatenation system in the translation of coreferential pronouns.
The Transformer-based models perform strongest on pronouns with intra-segmental antecedent, outperforming the recurrent baseline by 9–18 percentage points. This is likely an effect of increased model depth and the self-attentional architecture in this set of experiments. The model by BIBREF8 only uses source context, and outperforms the most comparable RNN system, s-hier.tied. However, the Transformer-based concat22 slightly underperforms the RNN-based concat22, and we consider it future research how to better exploit target context with Transformer-based models.
However, the Transformerbased concat22 slightly underperforms the RNN-based concat22, and we consider it future research how to better exploit target context with Transformer-based models.
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Does the Transformerbased concat22 outperform the RNN-based concat22?
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No, it doesn't.
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1911.09419
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WN18RR dataset consists of two kinds of relations: the symmetric relations such as $\_similar\_to$, which link entities in the category (b); other relations such as $\_hypernym$ and $\_member\_meronym$, which link entities in the category (a). Actually, RotatE can model entities in the category (b) very well BIBREF7. However, HAKE gains a 0.021 higher MRR, a 2.4% higher H@1, and a 2.4% higher H@3 against RotatE, respectively. The superior performance of HAKE compared with RotatE implies that our proposed model can better model different levels in the hierarchy.
FB15k-237 dataset has more complex relation types and fewer entities, compared with WN18RR and YAGO3-10. Although there are relations that reflect hierarchy in FB15k-237, there are also lots of relations, such as “/location/location/time_zones” and “/film/film/prequel”, that do not lead to hierarchy. The characteristic of this dataset accounts for why our proposed models doesn't outperform the previous state-of-the-art as much as that of WN18RR and YAGO3-10 datasets. However, the results also show that our models can gain better performance so long as there exists semantic hierarchies in knowledge graphs. As almost all knowledge graphs have such hierarchy structures, our model is widely applicable.
YAGO3-10 datasets contains entities with high relation-specific indegree BIBREF18. For example, the link prediction task $(?, hasGender, male)$ has over 1000 true answers, which makes the task challenging. Fortunately, we can regard “male” as an entity at higher level of the hierarchy and the predicted head entities as entities at lower level. In this way, YAGO3-10 is a dataset that clearly has semantic hierarchy property, and we can expect that our proposed models is capable of working well on this dataset. Table TABREF19 validates our expectation. Both ModE and HAKE significantly outperform the previous state-of-the-art. Notably, HAKE gains a 0.050 higher MRR, 6.0% higher H@1 and 4.6% higher H@3 than RotatE, respectively.
WN18RR dataset consists of two kinds of relations: the symmetric relations such as $\_similar\_to$, which link entities in the category (b); other relations such as $\_hypernym$ and $\_member\_meronym$, which link entities in the category (a). Actually, RotatE can model entities in the category (b) very well BIBREF7. However, HAKE gains a 0.021 higher MRR, a 2.4% higher H@1, and a 2.4% higher H@3 against RotatE, respectively.
FB15k-237 dataset has more complex relation types and fewer entities, compared with WN18RR and YAGO3-10. Although there are relations that reflect hierarchy in FB15k-237, there are also lots of relations, such as “/location/location/time_zones” and “/film/film/prequel”, that do not lead to hierarchy. The characteristic of this dataset accounts for why our proposed models doesn't outperform the previous state-of-the-art as much as that of WN18RR and YAGO3-10 datasets.
AGO3-10 datasets contains entities with high relation-specific indegree BIBREF18. For example, the link prediction task $(?, hasGender, male)$ has over 1000 true answers, which makes the task challenging. Fortunately, we can regard “male” as an entity at higher level of the hierarchy and the predicted head entities as entities at lower level. In this way, YAGO3-10 is a dataset that clearly has semantic hierarchy property, and we can expect that our proposed models is capable of working well on this dataset. Table TABREF19 validates our expectation. Both ModE and HAKE significantly outperform the previous state-of-the-art. Notably, HAKE gains a 0.050 higher MRR, 6.0% higher H@1 and 4.6% higher H@3 than RotatE, respectively.
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How better does HAKE model peform than state-of-the-art methods?
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The answers are shown as follows:
* 0.021 higher MRR, a 2.4% higher H@1, and a 2.4% higher H@3 against RotatE, respectively
* doesn't outperform the previous state-of-the-art as much as that of WN18RR and YAGO3-10
* HAKE gains a 0.050 higher MRR, 6.0% higher H@1 and 4.6% higher H@3 than RotatE, respectively
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The Adventures of Tintin (French: Les Aventures de Tintin [lez‿avɑ̃tyʁ də tɛ̃tɛ̃]) is a series of 24 bande dessinée albums created by Belgian cartoonist Georges Remi, who wrote under the pen name Hergé. The series was one of the most popular European comics of the 20th century. By 2007, a century after Hergé's birth in 1907, Tintin had been published in more than 70 languages with sales of more than 200 million copies, and had been adapted for radio, television, theatre, and film.
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What is Adventures of Tintin?
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Adventures of Tinting is a very popular comic book series created by Belgian cartoonist Georges Remi who wrote under the pen name Herge'. It is a series of 24 comic books that have been published in 70 languages with sales of 200 million copies.
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What is the best Engineering college in Bangalore?
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1. RV college of Engineering
2. PEST University
3. Dayanand Sagar
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2002.02758
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As seen in figures, in most of the cases our model produces comparable result with human translator. Result for BLEU score for our model and Google's Neural Machine Translation is compared in table TABREF19:
Result for BLEU score for our model and Google's Neural Machine Translation is compared in table TABREF19
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What is their baseline?
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The answers are shown as follows:
* Google's Neural Machine Translation
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Despite rapid progress in general question-answering (QA), e.g., BIBREF1, and formal models for qualitative reasoning (QR), e.g., BIBREF2, BIBREF3, there has been little work on reasoning with textual qualitative knowledge, and no dataset available in this area. Although many datasets include a few qualitative questions, e.g., BIBREF4, BIBREF5, the only one directly probing QR is QuaRel BIBREF0. However, although QuaRel contains 2700 qualitative questions, its underlying qualitative knowledge was specified formally, using a small, fixed ontology of 19 properties. As a result, systems trained on QuaRel are limited to only questions about those properties. Likewise, although the QR community has performed some work on extracting qualitative models from text, e.g., BIBREF6, BIBREF7, and interpreting questions about identifying qualitative processes, e.g., BIBREF8, there is no dataset available for the NLP community to study textual qualitative reasoning. QuaRTz addresses this need.
However, although QuaRel contains 2700 qualitative questions, its underlying qualitative knowledge was specified formally, using a small, fixed ontology of 19 properties. As a result, systems trained on QuaRel are limited to only questions about those properties.
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Are there any limitations when trained systems on QuaRel?
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The underlying qualitative knowledge of qualitative questions in QuaRel was specified formally, thus systems trained on QuaRel are limited to only questions about those properties.
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Identify which animal species is alive or extinct: Stupendemys, Gray Whale
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Gray Whale is alive, Stupendemys is extinct.
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Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
INLINEFORM0
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
INLINEFORM0
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs (Naseem et al., 2009).
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How about the performance of the cross lingual latent variables approach?
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This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs (Naseem et al., 2009).
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What is self advocacy?
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Self advocacy is a process in which a person themselves or asks another individual, group, or community to discuss or publish the benefits of what that person has done with others that have external influence to benefit the original person seeking self-advocacy. Self advocacy works well in corporate environments and is a behavior in start-up companies that does not make an impact on performance, ranking or promotion.
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Please provide 5 different variants of served coffee
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1. Cold Brew
2. Americano
3. Espresso
4. Latte
5. Cappuccino
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What are the words of House Waxley?
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"Light in the Darkness"
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1909.11297
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Previous attention-based methods can be categorized as soft-selection approaches since the attention weights scatter across the whole sentence and every word is taken into consideration with different weights. This usually results in attention distraction BIBREF7, i.e., attending on noisy or misleading words, or opinion words from other aspects. Take Figure FIGREF1 as an example, for the aspect place in the sentence “the food is usually good but it certainly is not a relaxing place to go”, we visualize the attention weights from the model ATAE-LSTM BIBREF2. As we can see, the words “good” and “but” are dominant in attention weights. However, “good” is used to describe the aspect food rather than place, “but” is not so related to place either. The true opinion snippet “certainly is not a relaxing place” receives low attention weights, leading to the wrong prediction towards the aspect place.
FLOAT SELECTED: Table 2: Experimental results (accuracy %) on all the datasets. Models in the first part are baseline methods. The results in the first part (except BERT-Original) are obtained from the prior work (Tay et al., 2018). Avg column presents macro-averaged results across all the datasets.
Previous attention-based methods can be categorized as soft-selection approaches since the attention weights scatter across the whole sentence and every word is taken into consideration with different weights.
FLOAT SELECTED: Table 2: Experimental results (accuracy %) on all the datasets. Models in the first part are baseline methods. The results in the first part (except BERT-Original) are obtained from the prior work (Tay et al., 2018). Avg column presents macro-averaged results across all the datasets.
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Which soft-selection approaches are evaluated?
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LSTM and BERT
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At what age can you get a driver's permit in the state of Washington?
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15
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Classify each of the follow weapons used by the US Military as a Fighter Jet, Rifle, Attack Helicopter, or Tank: M4 Carbine, F16 Falcon, M1 Abrams, F15 Eagle, AH64 Apache, M1903 Springfield, AH1 Cobra, M4 Sherman, F14 Tomcat, F86 Sabre
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M4 Carbine - Rifle
F16 Falcon - Fighter Jet
M1 Abrams - Tank
F15 Eagle - Fighter Jet
AH64 Apache - Attack Helicopter
M1903 Springfield - Rifle
AH1 Cobra - Attack Helicopter
M4 Sherman - Tank
F14 Tomcat - Fighter Jet
F86 Sabre - Fighter Jet
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A client with whom you have been training for 10 weeks retakes a submaximal exercise test and finds that his heart rate is lower at each stage (workload) of the test. He is bothered by this because he thought his heart would be stronger and beat more times per minute. Brainstorm an easy-to-understand explanation to help him understand what is going on. How would you respond?
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I would explain that, counterintuitively, the stronger your heart is, the less you can expect it to beat (lower heart rate). To explain it simply, his heart is more efficient; being able to pump the necessary amount of oxygen into his body without contracting as many times as before, i.e. the muscles of his heart are stronger. He can relax because his lower heart rate means he has better cardiovascular health now!
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Where is Florida?
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Florida is a state in the southeast corner of the continental United States of America. It is a peninsula surrounded by the Atlantic Ocean to the east and the Gulf of Mexico to the west. To the north, it is surrounded by the states of Georgia and Alabama. Key West, Florida is the southern most point of the continental United States. Ka Lae, on the Big Island of Hawaii, is the southern most point out of all 50 United States.
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Give me some ideas for my son's birthday party.
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There are several ways to plan a fun and engaging birthday party for your son. To get started, it would be helpful to consider your son's interests. From there, you can set that thing of interest as a theme and plan your party around it. For example, let's say your son loves the car. In that scenario, you could decorate the event space with car-related stuff and perhaps even order a cake shaped like a car!
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| 377
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We find that entailment problems which depend on any non-trivial amount of world knowledge are problematic from the point of view of training and testing systems for entailment. Indeed, in the presence of a large number of arbitrary facts, the conclusion can come solely from such knowledge, completely ignoring the premise. At best, the premise is serving as priming the memory of the reader. We find this issue to happen in RTE in a significant number of cases, including some of those listed above. For example, in problem 454, it is common knowledge that a bomb was indeed dropped on Hiroshima — yet we may consider that the entailment does not hold as such.
We find that entailment problems which depend on any non-trivial amount of world knowledge are problematic from the point of view of training and testing systems for entailment.
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Are the entailment problems which depend on any non-trivial amount of world knowledge problematic from the point of view of training and testing systems for entailment?
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Yes, they are.
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The French Bulldog, French: Bouledogue Français, is a French breed of companion dog or toy dog. It appeared in Paris in the mid-nineteenth century, apparently the result of cross-breeding of Toy Bulldogs imported from England and local Parisian ratters.
It is commonly kept as a pet, and is among the most frequently registered dogs in a number of countries including Australia, the United Kingdom, and the United States.
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Based on this paragraph about dogs, what is a French Bulldog?
|
The French Bulldog is a French breed of companion dog or toy dog. It is is among the most frequently registered dogs in a number of countries including Australia, the United Kingdom, and the United States.
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Identify which animal species is alive or extinct: Sarcosuchus, Plains Bison
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Sarcosuchus is extinct, Plains Bison is alive.
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Why do we love movies so much?
|
Because movies allow us to experience all those things/situations which are hard to take place in real life, movies can satisfy our desires and imaginations. There are many stories in the film to which we can relate ourselves. That's why we are so attracted to and entertained by movies.
There are several genres of movies
Action, Comedy, Drama, Fantasy, Horror, Mystery, Romance, Thriller
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Give me three reasons why basketball is so popular.
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Basketball is popular around the world because one, it is easy to learn the rules and everyone of all ages can learn the rules and play. Two, it's a team sport so it is fun to play with friends and is a way to make new friends. Three, it's exciting and has a fast pace of play which makes it appeal to people around the world.
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Which of the following are bait for fishing and which are candy: live worms, gummy worms, lollipops, Hershey's kisses, crickets, and leeches.
|
The candies are gummy worms, lollipops, and Hershey’s kisses, and the fish bait is live worms, crickets, and leeches. It’s possible that some of the candies can be used as fish bait but non of items categorized as fish bait would be acceptable as candy.
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| 512
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We selected three benchmark datasets from the TU Dortmund data collection: MUTAG dataset, IMDB-BINARY, and IMDB-MULTI. These datasets are provided for classification, thus we treated the experiments as invariant tasks. Since the size of these datasets are small, we run the 10-folds cross-validation score for each dataset.
We selected three benchmark datasets from the TU Dortmund data collection(Kersting et al., 2016): MUTAG dataset, IMDB-BINARY, and IMDB-MULTI. These datasets are provided for classification, thus we treated the experiments as invariant tasks. Since the size of these datasets are small, we run the 10-folds cross-validation score for each dataset.
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The experimental section is weak and contains comparison only to several simple tasks (introduced in Maron et al.). While these experimental results provide some knowledge about the model, and evaluation on real datasets (e.g., graph classification benchmarks ) would significantly strengthen the paper. If such an experiment is not possible for some reason can the authors discuss this?
|
We added experiments on graph benchmark data. Only three graph benchmarks experiments were completed during the rebuttal period, but we are currently running classification experiments using point cloud dataset; ModelNet40. It was not in time for the rebuttal revision manuscript, but if our paper is accepted, we will include in the camera-ready manuscript the results of the ModelNet 40 and other baseline methods results of the graph benchmark dataset.
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Q: What is the risk of using a hand sanitizer that contains methanol (wood alcohol) or 1-propanol?
|
A: Methanol exposure can result in nausea, vomiting, headache, blurred vision, permanent blindness, seizures, coma, permanent damage to the nervous system or death. Although people using these products on their hands are at risk for methanol poisoning, young children who accidentally swallow these products and adolescents and adults who drink these products as an alcohol (ethanol) substitute are most at risk.
Swallowing or drinking a hand sanitizer with 1-propanol can result in decreased breathing and heart rate, among other serious symptoms, and can lead to death. Hand sanitizer with 1-propanol contamination can irritate your skin (or eyes, if exposed). Although it is rare, some people have reported allergic skin reactions. Learn more about methanol and 1-propanol toxicities.
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| 12
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Because our work builds heavily upon BERT, we provide a brief summary of its features. BERT is built upon the Transformer architecture BIBREF0, which uses self-attention, feed-forward layers, residual connections and layer normalization as the main building blocks. It has two pre-training objectives:
Masked language modelling - some of the words in a sentence are being masked and the model has to predict them based on the context (note the difference from the typical autoregressive language model training objective);
Next sentence prediction - given two input sequences, decide whether the second one is the next sentence or not.
BERT has been shown to beat the state-of-the-art performance on 11 tasks with no modifications to the model architecture, besides adding a task-specific output layer BIBREF1. We follow same procedure suggested in BIBREF1 for our tasks. Fig. FIGREF8 shows the BERT model for classification. We obtain two kinds of representation from BERT: pooled output from last transformer block, denoted by H, and posterior probabilities, denoted by P. There are two variants of BERT - BERT-Base and BERT-Large. In this work we are using BERT-Base for faster training and experimentation, however, our methods are applicable to BERT-Large as well. BERT-Base and BERT-Large are different in model parameters such as number of transformer blocks, number of self-attention heads. Total number of parameters in BERT-Base are 110M and 340M in BERT-Large.
BERT suffers from major limitations in terms of handling long sequences. Firstly, the self-attention layer has a quadratic complexity $O(n^2)$ in terms of the sequence length $n$ BIBREF0. Secondly, BERT uses a learned positional embeddings scheme BIBREF1, which means that it won't likely be able to generalize to positions beyond those seen in the training data.
To investigate the effect of fine-tuning BERT on task performance, we use either the pre-trained BERT weights, or the weights from a BERT fine-tuned on the task-specific dataset on a segment-level (i.e. we preserve the original label but fine-tune on each segment separately instead of on the whole text sequence). We compare these results to using the fine-tuned segment-level BERT predictions directly as inputs to the next layer.
Given that BERT is limited to a particular input length, we split the input sequence into segments of a fixed size with overlap. For each of these segments, we obtain H or P from BERT model. We then stack these segment-level representations into a sequence, which serves as input to a small (100-dimensional) LSTM layer. Its output serves as a document embedding. Finally, we use two fully connected layers with ReLU (30-dimensional) and softmax (the same dimensionality as the number of classes) activations to obtain the final predictions.
With this approach, we overcome BERT's computational complexity, reducing it to $O(n/k * k^2) = O(nk)$ for RoBERT, with $k$ denoting the segment size (the LSTM component has negligible linear complexity $O(k)$). The positional embeddings are also no longer an issue.
Given that Transformers' edge over recurrent networks is their ability to effectively capture long distance relationships between words in a sequence BIBREF0, we experiment with replacing the LSTM recurrent layer in favor of a small Transformer model (2 layers of transformer building block containing self-attention, fully connected, etc.). To investigate if preserving the information about the input sequence order is important, we also build a variant of ToBERT which learns positional embeddings at the segment-level representations (but is limited to sequences of length seen during the training).
ToBERT's computational complexity $O(\frac{n^2}{k^2})$ is asymptotically inferior to RoBERT, as the top-level Transformer model again suffers from quadratic complexity in the number of segments. However, in practice this number is much smaller than the input sequence length (${\frac{n}{k}} << n$), so we haven't observed performance or memory issues with our datasets.
To investigate the effect of fine-tuning BERT on task performance, we use either the pre-trained BERT weights1 , or the weights from a BERT fine-tuned on the task-specific dataset on a segment-level (i.e. we preserve the original label but fine-tune on each segment separately instead of on the whole text sequence).
|
In what way does the team investigate the effect of fine-tuning BERT on task performance?
|
They use either the pre-trained BERT weights1 , or the weights from a BERT fine-tuned on the task-specific dataset on a segment-level.
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The pension reforms have been long-considered by Macron and his government. Reforming the pension system was a significant part of his platform for election in 2017, with initial protests and transport strikes in late 2019, prior to the COVID-19 pandemic which saw Macron delay the reforms further. Raising the retirement age was not part of these initial reforms, but another "plan to unify the complex French pension system" by "getting rid of the 42 special regimes for sectors ranging from rail and energy workers to lawyers was crucial to keep the system financially viable". On 26 October 2022, Macron announced in a televised interview that pension reform scheduled for 2023 intended to raise the retirement age to 65, specifically that the minimum retirement age to be able to receive a full pension would be "gradually increased" from 62 to 65 by 2031, by three months per year from September 2023 to September 2030. Furthermore, the number of years that contributions would need to be made to qualify for the full state pension would increase from 42 to 43 in 2027, meaning that some may have to work to 67 – the year at which a person is automatically able to receive a state pension from. In addition to this, France's 42 separate pension schemes would be "streamlined". Macron clarified he would be willing to "discuss the retirement age with unions and make potential amendments", and that not implementing the reforms would lead to a reduction in the size of pensions.
Details of the reforms were scheduled to be revealed on 15 December 2022, but were delayed further to 10 January 2023, as a courtesy to the Greens and Republicans who were in the midst of electing new leaders; postponing the reforms to allow Macron to consult with said new leaders before revealing said details.
|
Which changes to the law are planned for the french pension reform?
|
President Macron plans to raise the retirement age from 62 to 65 by 2031. The plan is to gradually increase the age by 3 month per year from September 2023 to September 2030.
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| 19
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This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs.
Morphological (re)inflection from context is of particular relevance to the field of computational linguistics: it is compelling to estimate how well a machine-learned system can capture the morphosyntactic properties of a word given its context, and map those properties to the correct surface form for a given lemma.
There are two tracks of Task 2 of CoNLL–SIGMORPHON 2018: in Track 1 the context is given in terms of word forms, lemmas and morphosyntactic descriptions (MSD); in Track 2 only word forms are available. See Table TABREF1 for an example. Task 2 is additionally split in three settings based on data size: high, medium and low, with high-resource datasets consisting of up to 70K instances per language, and low-resource datasets consisting of only about 1K instances.
The baseline provided by the shared task organisers is a seq2seq model with attention (similar to the winning system for reinflection in CoNLL–SIGMORPHON 2016, BIBREF1 ), which receives information about context through an embedding of the two words immediately adjacent to the target form. We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system: (1) We use an LSTM to encode the entire available context; (2) We employ a multi-task learning approach with the auxiliary objective of MSD prediction; and (3) We train the auxiliary component in a multilingual fashion, over sets of two to three languages.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results. Multi-task learning, paired with multilingual training and subsequent monolingual finetuning, scored highest for five out of seven languages, improving accuracy by another 9.86% on average.
The task is to generate an inflected word form given its lemma and the context in which it occurs.
|
What is the aim of Task 2 of the CoNLL–SIGMORPHON 2018?
|
The task is to generate an inflected word form given its lemma and the context in which it occurs.
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Which of these is edible or not edible:
1. Banana
2. Laptop
3. Marmite
4. Tape
5. Mushroom
6. Pizza
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The following are edible:
1. Banana
2. Marmite
3. Pizza
The following are not edible:
1. Laptop
2. Tape
The following may or may not be edible:
1. Mushroom
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A las vegas seven days trip itinerary including budget and activities for a group of 5 friends.
|
A las vegas 7-day trip from Bangalore to Las Vegas for a group of 5 people.
A trip begins with packing. Check the weather forecast for all your travel destinations and plan your clothes accordingly.
Your bag itinerary must have common medicines, and also, if you are allergic, be specific about packing what you need.
Always keep a pair of cloth in cabin baggage if check-in baggage has a delay - you can stay a day or two with the clothes packed in cabin baggage.
Make sure to have some snacks in the cabin baggage
Make sure to keep travel documents copy set in all luggage you carry. Make sure to have some local currency as well ready before you travel.
Make sure all hotels, Airport pickup and drop are booked, and you keep a copy of those documents.
Flight charges from Bangalore to LasVegas cost between 130k to 180k per person depending on the carrier and seat preference( Economy or first class)
Now that you have done pre checklist, now below are the things to do in Las Vegas
In Las Vegas - Visit the Bellagio fountain at night.
Check out High Roller Observation Wheel
Visit the Neon Museum
Walk at night in the las vegas strip
If you are interested in casinos, tons of casinos start from the Airport. In the las vegas strip, you can visit MGM Grand, The Venetian, etc., for Casinos.
Visit the Shark Reef Aquarium at Mandalay Bay
Visit the Red Rock Canyon National Conservation Area
You can hire a rental car - The cost per day is approx. 70 to 100 USD per person if divided by all five people
Visit the Hoover Dam in a car
Next, you can plan for Zion National Park and prepare for hiking trails.
Next move to Grand Canyon
Stay in Page, Arizona
From there, you can visit GC National Park's South rim and north rim
Don't miss Horseshoe Bend and Grand Canyon Lower and Upper Antelope Canyon tours
So these places can be covered in LasVegas for three days and Page, Arizona for three days
You can head back to LasVegas and Shopping for one day and catch the next flight back to your hometown
Expenses per person per day, 1000 USD to 1500 USD, depending on whether you are in a budget hotel. Except for Food. Food costs up to 50 to 100 USD per day.
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What is the analysis of mutations in cancer, in genomics?
|
In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. New physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single-nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.
Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second cancer contains driver mutations which need to be distinguished from passengers.
With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably. This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples.
Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.
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Think up a short list of the best US Presidents
|
Abraham Lincoln, George Washington, FDR, Ronald Reagan, Dwight Eisenhower, Harry Truman, Thomas Jefferson
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1804.08094
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To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs. For pre-processing, in this case we do not preserve casing and delete English stopwords.
To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.
|
What was the baseline model?
|
The answers are shown as follows:
* a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs
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Woods was born on December 30, 1975, in Cypress, California, to Earl and Kultida "Tida" Woods. He is their only child, though he has two half-brothers and a half-sister from his father's first marriage. Earl was a retired U.S. Army officer and Vietnam War veteran; he was born to African-American parents and was also said to have had European, Native American, and possibly Chinese ancestry. Kultida (née Punsawad) is originally from Thailand, where Earl met her when he was on a tour of duty there in 1968. She is of mixed Thai, Chinese, and Dutch ancestry. In 2002, ESPN claimed: "For the record, he is one-quarter Thai, one-quarter Chinese, one-quarter Caucasian, one-eighth African American and one-eighth Native American." Tiger has described his ethnic make-up as "Cablinasian" (a syllabic abbreviation he coined from Caucasian, Black, American Indian, and Asian).
Woods's first name, Eldrick, was chosen by his mother because it began with "E" (for Earl) and ended with "K" (for Kultida). His middle name Tont is a traditional Thai name. He was nicknamed Tiger in honor of his father's friend, South Vietnamese Colonel Vuong Dang Phong, who had also been known as Tiger.
Woods has a niece, Cheyenne Woods, who played for the Wake Forest University golf team and turned professional in 2012 when she made her pro debut in the LPGA Championship.[
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Given a reference text about Tiger Woods, tell me when they were born, what their real first name is, and what profession his niece has.
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Tiger Woods was borh on December 30, 1975. His real first name is "Eldrick". His niece, Cheyenne Woods, is a professional golfer.
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| 82
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We train an auxiliary system using NMT model from the simplified sentence to the ordinary sentence, which is first trained on the available parallel data. For leveraging simplified sentences to improve the quality of NMT model for text simplification, we propose to adapt the back-translation approach proposed by Sennrich et al. BIBREF11 to our scenario. More concretely, Given one sentence in simplified sentences, we use the simplified-ordinary system in translate mode with greedy decoding to translate it to the ordinary sentences, which is denoted as back-translation. This way, we obtain a synthetic parallel simplified-ordinary sentences. Both the synthetic sentences and the available parallel data are used as training data for the original NMT system.
For leveraging simplified sentences to improve the quality of NMT model for text simplification, we propose to adapt the back-translation approach proposed by Sennrich et al.(Sennrich et al., 2015) to our scenario.
|
Whose back-translation approach did the team propose to use?
|
Sennrich et al.
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Twitch is an American video live streaming service that focuses on video game live streaming, including broadcasts of esports competitions, in addition to offering music broadcasts, creative content, and "in real life" streams. Twitch is operated by Twitch Interactive, a subsidiary of Amazon.com, Inc. It was introduced in June 2011 as a spin-off of the general-interest streaming platform Justin.tv. Content on the site can be viewed either live or via video on demand. The games shown on Twitch's current homepage are listed according to audience preference and include genres such as real-time strategy games (RTS), fighting games, racing games, and first-person shooters.
The popularity of Twitch eclipsed that of its general-interest counterpart. In October 2013, the website had 45 million unique viewers, and by February 2014, it was considered the fourth-largest source of peak Internet traffic in the United States. At the same time, Justin.tv's parent company was re-branded as Twitch Interactive to represent the shift in focus – Justin.tv was shut down in August 2014. That month, the service was acquired by Amazon for US$970 million, which later led to the introduction of synergies with the company's subscription service Amazon Prime. Twitch acquired Curse LLC in 2016, an operator of online video gaming communities and introduced means to purchase games through links on streams along with a program allowing streamers to receive commissions on the sales of games that they play.
By 2015, Twitch had more than 100 million viewers per month. In 2017, Twitch remained the leading live streaming video service for video games in the US, and had an advantage over YouTube Gaming, which shut down its standalone app in May 2019. As of February 2020, it had 3 million broadcasters monthly and 15 million daily active users, with 1.4 million average concurrent users. As of May 2018, Twitch had over 27,000 partner channels.[1
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Given this paragraph about Twitch Interactive, in what year did Amazon acquire them?
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Amazon acquired Twitch in the year 2014.
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How do banks make money?
|
Traditionally, banks accept deposits and loan that money to borrowers providing a time arbitrage on the velocity of money which regulates in profit. In more recent years, banks have diversified their profit centers by investing those deposits in fixed income securities. There has even been a proliferation of into multiple types of banks. Some of these banks include investment banks which transact in the capital markets pairing investors with capital seeking counterparties. The most innovative banks are starting to see how data can become a profit center as well as allow them to provide a better customer experience.
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What is the difference between Montessori and Regular schooling?
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A Montessori format of schooling is based on an individual's learning pace. In a Montessori classroom, there are a plethora of activities laid out at the eye level of the child. Each child can pick and choose an activity that interests him or her. The learning progresses based on the child's interests. Children often observe other children practicing other activities, which fosters curiosity and motivates them to try different activities in the Montessori setting.
In a regular schooling environment, the curriculum for the year is pre-decided and the children are supposed to follow the same, hence everyone is expected to learn at the same pace. It is structured around student-teacher interaction and adheres to a time-table.
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Oskar Dillon (born 10 February 1999), is an Australian professional footballer who plays as a centre back for Oakleigh Cannons in NPL Victoria.
Club career
Gold Coast Knights
Dillon was part of the 2019 NPL Queensland championship winning Gold Coast Knights team. He played the full game and scored the winning goal, a free-kick from 25 yards out, as the Knights' defeated Olympic FC 2-1 in the 2019 NPL Queensland Grand Final on 14 September 2019, claiming their first piece of top-flight silverware. After an impressive campaign, Dillon was awarded the NPL Queensland Young Player of the Year at the inaugural Football in Queensland Awards Night.
Western United
On 2 January 2020, Dillon signed his first professional contract with Western United, penning a one-year deal for the 2019-20 season. He made his debut in a Round 15 clash against Central Coast Mariners, playing the full game as United ran out 3-0 winners at GMHBA Stadium. Dillon was released by the club at the end of the 2019–20 A-League.
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How far did Oskar Dillon kick in the winning goal at the NPL Queensland championship?
|
Oskar Dillon, the Australian professional footballer did a free kick from 25 yards out defeating the Knights in the 2019 NPL Queensland Grand final.
|
1604.05781
| false
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Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Twitter activity consists of short posts called tweets which are limited to 140 characters. Retweets, where users repost a tweet to spread its content, were not considered. (The spread of causal statements will be considered in future work.) We considered only English-language tweets for this study. To avoid cross-language effects, we kept only tweets with a user-reported language of `English' and, as a second constraint, individual tweets needed to match more English stopwords than any other language's set of stopwords. Stopwords considered for each language were determined using NLTK's database BIBREF29 . A tweet will be referred to as a `document' for the rest of this work.
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three “cause-words”, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively.
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API.
Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present.
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What is the source of the "control" corpus?
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Randomly selected from a Twitter dump, temporally matched to causal documents
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