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In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
[Q]: " No apologies are adequate for my leaving you in danger... nor can I ever thank you enough for saving my life. "
[A]: Plural
[Q]: " The sheriff should be real comfortable.
[A]: Singular
[Q]: The trickle of blood was now running down one of the wrinkles from her mouth to her chin like a droplet of rain down a gutter.
[A]:
|
Singular
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 5
|
train
|
Teacher: In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Coming from a xenophobic race that possesses the unique ability to pass among other species and chooses not to, the bounty hunter is a walking contradiction.
Solution: Singular
Reason: "the bounty hunter" is the subject of the sentence which is singular.
Now, solve this instance: The stairwell is dark and I see no switches.
Student:
|
Singular
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 2
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
National Geographic shots of feeding piranhas flood my mind, and I add yet another note to my schedule : find out where the library is.
Output:
|
Plural
|
task430_senteval_subject_count
|
NIv2
|
zs_opt
| 1
|
train
|
Detailed Instructions: In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Q: After all, that was why she was in the Watch, wasn " t it ?
A:
|
Singular
|
task430_senteval_subject_count
|
NIv2
|
zs_opt
| 9
|
train
|
TASK DEFINITION: In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
PROBLEM: My eyelids finally abandoned their fight, and I woke up.
SOLUTION: Plural
PROBLEM: The paint on this one was a myriad of golds with a few highlights of crimson.
SOLUTION: Singular
PROBLEM: Witches and vampires are different.
SOLUTION:
|
Plural
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 8
|
train
|
In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Q: Resistance then generally collapsed and the criminal gangs nearly all turned themselves in.
A: Singular
****
Q: The bullet had passed right through, not cutting any major arteries.
A: Singular
****
Q: The bullet had passed right through, not cutting any major arteries.
A:
|
Singular
****
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 4
|
train
|
Definition: In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Input: " Some visitors at last.
Output:
|
Plural
|
task430_senteval_subject_count
|
NIv2
|
zs_opt
| 2
|
train
|
In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Q: His footsteps echoed like a death knell when he walked away.
A: Plural
****
Q: The red firelight glinted in his auburn hair, and I could see he might have been attractive, even downright handsome, once.
A: Singular
****
Q: " How are your butterflies ? "
A:
|
Plural
****
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 4
|
train
|
Definition: In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Input: Dangerous Beans was still staring at the unseen world.
Output:
|
Plural
|
task430_senteval_subject_count
|
NIv2
|
zs_opt
| 2
|
test
|
Part 1. Definition
In this task you are given a sentence. You must judge whether subject of the main clause is singular or plural. Label the instances as "Singular" or "Plural" based on your judgment.
Part 2. Example
Coming from a xenophobic race that possesses the unique ability to pass among other species and chooses not to, the bounty hunter is a walking contradiction.
Answer: Singular
Explanation: "the bounty hunter" is the subject of the sentence which is singular.
Part 3. Exercise
His first stroke was swift, filling me up.
Answer:
|
Singular
|
task430_senteval_subject_count
|
NIv2
|
fs_opt
| 7
|
validation
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Q: Sentence 1: Several children lined up at and looking through a fence. Sentence 2: People looking at a ghost
A:
|
The kids are outside.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
zs_opt
| 4
|
train
|
Given the task definition and input, reply with output. In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Sentence 1: A crowd is staring at a screen that is showing a colorful image. Sentence 2: The crowd is near a screen watching a show.
|
The group is looking at the colors on a screen.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
zs_opt
| 5
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
[Q]: Sentence 1: These people are doing dangerous things. Sentence 2: These people are doing illegal things.
[A]: People doing dangerous things
[Q]: Sentence 1: A boy is leaning on a car with flowers on the hood. Sentence 2: A boy is leaning on his father brand new car.
[A]: Someone is leaning on the hood of a car.
[Q]: Sentence 1: Young happy toddler smiling and riding her horsey. Sentence 2: a young child is having fun with her pet horse
[A]:
|
a young child enjoys herself
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
fs_opt
| 5
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
--------
Question: Sentence 1: A young girl dressed in gray kneels to look at a dog. Sentence 2: She is teaching the dog a trick.
Answer: The girl is looking at the dog while wearing gray.
Question: Sentence 1: Four people wait in a stone tiled courtyard. Sentence 2: Four people are waiting for a tour guide.
Answer: Four people are outside.
Question: Sentence 1: A group of people sitting at a table together. Sentence 2: They are workign together
Answer:
|
the peopel are sitting
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Input: Consider Input: Sentence 1: Group of asian boy on the right of the long table of vegetables and food and asian adults on the left. Sentence 2: The group Asians are eating dinner at the table.
Output: There are many Asians at the table.
Input: Consider Input: Sentence 1: People in bright green shirts, construction hats and blue jeans are standing near a tree that is laying on the ground. Sentence 2: Construction workers pose next to a tree that has fallen
Output: A tree is on the ground next to people in construction hats
Input: Consider Input: Sentence 1: Women walking through deep snow and down a steep hill. Sentence 2: A woman hiking down a hillside after a climb.
|
Output: A woman walking down a hill covered in snow.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
fs_opt
| 2
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Sentence 1: A group of young Asian women smiling for the camera. Sentence 2: asian women smile for a camera while on vacation
Output:
|
asian women smile for a camera
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
zs_opt
| 1
|
train
|
Detailed Instructions: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Q: Sentence 1: Villager pouring water into cooking pot. Sentence 2: The village chef prepares a meal.
A:
|
A person puts water into a pot for cooking.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
zs_opt
| 9
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Example input: Sentence 1: Jon saw his friend Tom coming out of the grocery store with a bag of fruit. Sentence 2: Tom had been shopping for fruit to give Jon.
Example output: Tom had never been in the store.
Example explanation: Tom must have gone into the store to come out of it.
Q: Sentence 1: White, long-necked bird with yellow legs flies over water. Sentence 2: The bird is flying over a lake.
A:
|
The bird has wings.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
fs_opt
| 3
|
train
|
Teacher: In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Sentence 1: Jon saw his friend Tom coming out of the grocery store with a bag of fruit. Sentence 2: Tom had been shopping for fruit to give Jon.
Solution: Tom had never been in the store.
Reason: Tom must have gone into the store to come out of it.
Now, solve this instance: Sentence 1: A wet dog carrying a stick in its mouth. Sentence 2: The wet dog wanted to play fetch.
Student:
|
The wet dog is dripping water
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
fs_opt
| 2
|
test
|
Teacher:In this task, you're given a pair of sentences, sentence 1 and sentence 2, that neither agree with nor contradict each other. Your job is to alter sentence 2 so that the pair clearly contradict each other. Generated sentences must be short, with less than 15 words. New information can be introduced. Avoid using pronouns to confuse the subject of the sentence.
Teacher: Now, understand the problem? Solve this instance: Sentence 1: A group of kids in red and white playing soccer. Sentence 2: There are kids playing ball in a soccer tournament.
Student:
|
Some kids are playing ball.
|
task189_snli_neutral_to_contradiction_text_modification
|
NIv2
|
zs_opt
| 6
|
validation
|
Q: You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Sentence: Stretching her back, the woman smiled at the girl.
A:
|
Whose back was the woman stretching?
|
task489_mwsc_question_generation
|
NIv2
|
zs_opt
| 7
|
train
|
instruction:
You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
question:
Sentence: James asked Robert for a favor but he refused.
answer:
Who refused?
question:
Sentence: Esther figures that she will save shipping costs if she builds her factory in Springfield instead of Franklin, because most of her customers live there.
answer:
In which town do most of Esther's customers live?
question:
Sentence: As Ollie carried Tommy up the long winding steps, his legs ached.
answer:
|
Whose legs ached?
|
task489_mwsc_question_generation
|
NIv2
|
fs_opt
| 9
|
train
|
Detailed Instructions: You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Q: Sentence: Joe paid the detective after he delivered the final report on the case.
A:
|
Who delivered the final report?
|
task489_mwsc_question_generation
|
NIv2
|
zs_opt
| 9
|
train
|
Teacher: You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Sentence: I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out.
Solution: What couldn't I get out?
Reason: This question is based on the following sentence given- I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out. From here it's clear that I couldn't get it out chewing gum and this is a good question since answer for this question is there in the sentence given.
Now, solve this instance: Sentence: As Ollie carried Tommy up the long winding steps, his legs dangled.
Student:
|
Whose legs dangled?
|
task489_mwsc_question_generation
|
NIv2
|
fs_opt
| 2
|
train
|
Part 1. Definition
You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Part 2. Example
Sentence: I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out.
Answer: What couldn't I get out?
Explanation: This question is based on the following sentence given- I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out. From here it's clear that I couldn't get it out chewing gum and this is a good question since answer for this question is there in the sentence given.
Part 3. Exercise
Sentence: As Andrea in the crop duster passed over Susan, she could see the landing gear.
Answer:
|
Who could see the landing gear?
|
task489_mwsc_question_generation
|
NIv2
|
fs_opt
| 7
|
train
|
Teacher:You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Teacher: Now, understand the problem? Solve this instance: Sentence: Dan had to stop Bill from toying with the injured bird. He is very cruel.
Student:
|
Who is cruel?
|
task489_mwsc_question_generation
|
NIv2
|
zs_opt
| 6
|
train
|
Definition: You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Input: Sentence: The doctor informed Kate that she had cancer and presented several options for future treatment.
Output:
|
Who had cancer?
|
task489_mwsc_question_generation
|
NIv2
|
zs_opt
| 2
|
train
|
Instructions: You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Input: Sentence: Everyone really loved the oatmeal cookies; only a few people liked the snickerdoodle cookies. Next time, we should make more of them.
Output:
|
Which cookie should we make more of, next time?
|
task489_mwsc_question_generation
|
NIv2
|
zs_opt
| 3
|
train
|
You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Ex Input:
Sentence: Bill passed the gameboy to John because his turn was next.
Ex Output:
Whose turn was next?
Ex Input:
Sentence: As Andrea in the crop duster passed over Susan, she could see the landing gear.
Ex Output:
Who could see the landing gear?
Ex Input:
Sentence: James asked Robert for a favor but he was refused.
Ex Output:
|
Who was refused?
|
task489_mwsc_question_generation
|
NIv2
|
fs_opt
| 1
|
test
|
Given the task definition, example input & output, solve the new input case.
You need to read the given sentence and construct a question about the information present in the sentence. Construct a question in such a way that (i) it is unambiguous, (ii) its answer is unique. The question must involve coreference resolution which is a task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Example: Sentence: I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out.
Output: What couldn't I get out?
This question is based on the following sentence given- I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get it out. From here it's clear that I couldn't get it out chewing gum and this is a good question since answer for this question is there in the sentence given.
New input case for you: Sentence: Bill passed the half-empty plate to John because he was full.
Output:
|
Who was full?
|
task489_mwsc_question_generation
|
NIv2
|
fs_opt
| 1
|
validation
|
Instructions: Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Input: This study describes a case of eye burn induced by sodium hypochlorite used as an irrigant during root canal preparation.', 'A 24-year-old female endodontist was using an operating microscope during root canal treatment, and as the root canal was irrigated, the pressure cannula burst and the irrigant (3.5% sodium hypochlorite) came into direct contact with her left eye. She immediately sought ophthalmologic emergency care for pain, redness of the cornea, burning sensation, photophobia, intraocular pressure, and blurred vision. The initial treatment consisted of washing the eye with saline solution and administering analgesic and anti-inflammatory (steroid) medications. One day after the accident, a topical demulcent and hydroxypropyl medication were applied to the eyeball (conjunctiva), the eye was bandaged for 24 hours, and rest was prescribed for 7 days. Eight days later, a corneal ulcer was diagnosed, and antibiotic and anti-inflammatory (steroid) medications were used.', 'Vision was restored without any sequelae 4 weeks after the accident. The endodontist was instructed to apply control medication (Lagricel; Sophia SA, Caracas, Venezuela) for 3 months and to return for ophthalmologic follow-up every 6 months.
Output:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 3
|
train
|
Teacher:Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Teacher: Now, understand the problem? Solve this instance: The cyclooxygenase-2 (cox-2) pathway is now recognized to be important in human cancer development and progression. The gene for cox-2 carries a common single nucleotide polymorphism, T8473C, located within a potential functional region in the 3'-UTR of cox-2 gene was identified. We have investigated the frequencies of cox-2 genotypes in Tunisian population to determine whether that polymorphism was associated with the risk of nasopharyngeal carcinoma (NPC) in Tunisian population.", 'One hundred and eighty-nine NPC patients were compared to 237 healthy controls.', 'The cox-2 T8473C polymorphism was significantly associated with NPC (P=0.031). The CC-genotype and C allele were more frequent in control compared to patients group [CC: OR=0.37; P=0.013; 95% CI: 0.17-0.81; C: OR=0.72; P=0.032; 95% CI: 0.53-0.97]. Multivariate logistic regression analyses revealed that the CC-genotype was associated with a significantly decreased risk of NPC (P=0.013). Tumor sizes, histologic grade, presence of primary lymph node metastases, age or sex were not associated with cox-2 genotypes.
Student:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Example Input: Oxidants are believed to play a major role in the development of emphysema.', 'This study aimed to determine if the expression of human copper-zinc superoxide dismutase (CuZnSOD) within the lungs of mice protects against the development of emphysema.', 'Transgenic CuZnSOD and littermate mice were exposed to cigarette smoke (6 h/d, 5 d/wk, for 1 yr) and compared with nonexposed mice. A second group was treated with intratracheal elastase to induce emphysema.', 'Lung inflammation was measured by cell counts and myeloperoxidase levels. Oxidative damage was assessed by immunofluorescence for 3-nitrotyrosine and 8-hydroxydeoxyguanosine and lipid peroxidation levels. The development of emphysema was determined by measuring the mean linear intercept (Lm).', 'Smoke exposure caused a fourfold increase in neutrophilic inflammation and doubled lung myeloperoxidase activity. This inflammatory response did not occur in the smoke-exposed CuZnSOD mice. Similarly, CuZnSOD expression prevented the 58% increase in lung lipid peroxidation products that occurred after smoke exposure. Most important, CuZnSOD prevented the onset of emphysema in both the smoke-induced model (Lm, 68 exposed control vs. 58 exposed transgenic; p < 0.04) and elastase-generated model (Lm, 80 exposed control vs. 63 exposed transgenic; p < 0.03). These results demonstrate for the first time that antioxidants can prevent smoke-induced inflammation and can counteract the proteolytic cascade that leads to emphysema formation in two separate animal models of the disease.
Example Output: 1
Example Input: Patients with acute myocardial infarction (AMI) whose culprit lesion lies in a branch of the 3 major coronary arteries have well-preserved cardiac function. A first MI with preserved cardiac function is a risk factor for left ventricular free wall rupture (LVFWR), so the aim of this study was to investigate the possible relationship between AMI with branch segment occlusion and LVFWR.', 'The 439 patients with AMI were retrospectively studied. They were divided into 2 groups: group B (n=70; segments 4 atrioventricular node artery, 4 posterior descending coronary artery, 8, 9, 10, 12, 14, or 15 according to the AHA classification), and group P (n=369; segments 1, 2, 3, 5, 6, 7, 11, or 13). Primary percutaneous coronary intervention (PCI) was more often performed in group P (75% vs 57%; P=0.0018). In-hospital mortality tended to be lower in group B (1.4% vs 6.2%; P=0.105). The incidence of LVFWR was significantly higher in group B (10.0% vs 1.6%; P=0.0002).By multivariate logistic regression analysis, 1-vessel disease, absence of primary PCI, branch segment occlusion, and age were identified as independent predictors of LVFWR.
Example Output: 0
Example Input: Clinical data have shown that an increased level of serum soluble CD40 ligand (sCD40L) is associated with atherosclerogenesis. We hypothesize that sCD40L induces proliferation and migration of vascular smooth muscle cells (VSMCs) through activation of matrix metalloproteinases (MMPs).', 'Human VSMCs were treated with sCD40L (1 or 5 microg/mL). Cell proliferation and migration were studied using a nonradioactive cell proliferation assay (MTT) and a modified Boyden chamber combined with a scrape-wound assay, respectively. Messenger RNA (mRNA) and protein levels of MMP-2 and MMP-9 were measured with real-time polymerase chain reaction and enzyme-linked immunosorbent assays. Neutralizing antibodies against MMP-2 or MMP-9 were used to evaluate their effects on sCD40L-induced cell proliferation and migration.', 'MTT assay showed a 35% increase in cell proliferation in the high-dose (5 microg/mL) sCD40L-treated group. Cell migration was also increased by 33% (Transwell assay) to 3-fold (scrape-wound assay) after high-dose sCD40L treatment. When cells were treated with 5 microg/mL of sCD40L for 24 hours, significant decreases in MMP-2 and increases in MMP-9 mRNA and protein levels were observed. Neutralizing antibodies against MMP-9 effectively blocked sCD40L-induced cell proliferation and migration.
Example Output:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
fs_opt
| 3
|
train
|
Q: Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Activation of the endothelin (ET) system promotes inflammation and fibrosis in various tissues including the kidney. Male ET-1 transgenic mice are characterized by chronic kidney inflammation and renal scarring. We hypothesized that this renal phenotype might be modulated by androgens. Thus the aim of our study was to elucidate the impact of gonadectomy in ET-1 transgenic mice on kidney function and morphology. -', 'Male ET-1 transgenic mice at the age of 10 weeks were randomly allocated to the following groups: normal ET transgenic mice (ET; n = 17) and ET transgenic mice that underwent castration (ET + cas; n = 12). Study duration was 9 months. Creatinine clearance and protein excretion was monitored. At study end animals were sacrificed and kidneys were harvested for histology/immunhistochemistry.', 'Castration significantly ameliorated glomerulosclerosis in ET-1 transgenic mice (ET glomerulosclerosis-score: 3.0 +/- 0.17 vs ET+cas: 2.4 +/- 0.17; p < 0.05) as well as renal perivascular fibrosis (ET fibrosis-score: 3.0 +/- 0.14 vs ET + cas: 2.2 +/- 0.14; p < 0.05). However, interstitial fibrosis and media/lumen-ratio of renal arteries remained unaffected by castration. Regarding inflammation, castration significantly reduced the number of CD4-positive cells in renal tissue of ET-1 transgenic mice (ET CD4-positive cells/10000 cells: 355 +/- 72 vs ET + cas: 147 +/- 28; p < 0.05). Renal tissue contents of CD8 positive cells as well as of macrophages were not affected by castration. Regarding kidney function castration significantly reduced proteinuria in ET-1 transgenic mice whereas creatinine clearance did not differ between study groups.
A:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 7
|
train
|
Teacher:Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Teacher: Now, understand the problem? Solve this instance: Previous results have shown that mice lacking in the group 1B phospholipase A(2) (Pla2g1b) are resistant to obesity and diabetes induced by feeding a diabetogenic high-fat/high-carbohydrate diet. This study examined the potential of using the Pla2g1b inhibitor methyl indoxam as therapy to suppress diet-induced obesity and diabetes.', 'Male C57BL/6 mice were fed the diabetogenic diet with or without methyl indoxam supplementation. Body weight gain, fasting plasma glucose levels, glucose tolerance and postprandial lysophospholipid absorption were compared.', 'Wild-type C57BL/6 mice fed the diabetogenic diet without Pla2g1b inhibitor showed 31 and 69% body weight gain after 4 and 10 weeks respectively. These animals also showed elevated plasma glucose levels and were glucose intolerant. In contrast, C57BL/6 mice fed the diabetogenic diet with 90 mg.kg(-1) of methyl indoxam gained only 5% body weight after 10 weeks. These animals were also euglycaemic and displayed normal glucose excursion rates in glucose tolerance test. Methyl indoxam suppression of diet-induced body weight gain and glucose intolerance was correlated with the inhibition of Pla2g1b-mediated postprandial lysophospholipid absorption.
Student:
|
1
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Q: Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Segmental handling of sodium along the proximal and distal nephron might be heritable and different between black and white participants.', 'We randomly recruited 95 nuclear families of black South African ancestry and 103 nuclear families of white Belgian ancestry. We measured the (FENa) and estimated the fractional renal sodium reabsorption in the proximal (RNaprox) and distal (RNadist) tubules from the clearances of endogenous lithium and creatinine. In multivariable analyses, we studied the relation of RNaprox and RNadist with FENa and estimated the heritability (h) of RNaprox and RNadist.', 'Independent of urinary sodium excretion, South Africans (n = 240) had higher RNaprox (unadjusted median, 93.9% vs. 81.0%; P < 0.001) than Belgians (n = 737), but lower RNadist (91.2% vs. 95.1%; P < 0.001). The slope of RNaprox on FENa was steeper in Belgians than in South Africans (-5.40 +/- 0.58 vs. -0.78 +/- 0.58 units; P < 0.001), whereas the opposite was true for the slope of RNadist on FENa (-3.84 +/- 0.19 vs. -13.71 +/- 1.30 units; P < 0.001). h of RNaprox and RNadist was high and significant (P < 0.001) in both countries. h was higher in South Africans than in Belgians for RNaprox (0.82 vs. 0.56; P < 0.001), but was similar for RNadist (0.68 vs. 0.50; P = 0.17). Of the filtered sodium load, black participants reabsorb more than white participants in the proximal nephron and less postproximally.
A:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 7
|
train
|
Definition: Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Input: To describe the epidemiology of acute hand injuries and hand infections and to describe the factors associated with the transfer of these patients to a level 1 trauma center. In addition, we sought to understand management before transfer.', 'Retrospective review of patients with hand trauma or hand infection transferred to our level 1 trauma center from May 2009 to August 2011. We also identified hospitals with emergency departments (EDs) in our region and surveyed ED providers in these hospitals with regard to acute hand care.', 'A level 1 trauma center in the United States.', 'Four hundred sixty consecutive transfers for acute hand care.', 'The average patient age was 38. Most were male (84%), uninsured (51%), and from another county (59%). The average distance of transfer was 51 miles, and 80% were transferred by ground ambulance. The most common reasons for transfer were amputations (24%), infections (21%), lacerations (17%), and fractures/dislocations (16%). Of the 345 hospitals with an ED surveyed, 71% never had hand surgery coverage.
Output:
|
1
|
task848_pubmedqa_classification
|
NIv2
|
zs_opt
| 2
|
train
|
instruction:
Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
question:
Chronic pancreatitis is characterized by inflammation, atrophy, fibrosis with progressive ductal changes, and functional changes that include variable exocrine and endocrine insufficiency and multiple patterns of pain. We investigated whether abdominal imaging features accurately predict patterns of pain.', 'We collected data from participants in the North American Pancreatitis Study 2 Continuation and Validation, a prospective multicenter study of patients with chronic pancreatitis performed at 13 expert centers in the United States from July 2008 through March 2012. Chronic pancreatitis was defined based on the detection of characteristic changes by cross-sectional abdominal imaging, endoscopic retrograde cholangiopancreatography, endoscopic ultrasonography, or histology analyses. Patients were asked by a physician or trained clinical research coordinator if they had any abdominal pain during the year before enrollment, those who responded "yes" were asked to select from a list of 5 pain patterns. By using these patterns, we\xa0classified patients\' pain based on timing and severity. Abnormal pancreatitis-associated features on abdominal imaging were recorded using standardized case report forms.', 'Data were collected from 518 patients (mean age, 52 ± 14.6 y; 55% male; and 87.6% white). The most common physician-identified etiologies were alcohol (45.8%) and idiopathic (24.3%); 15.6% of patients reported no abdominal pain in the year before enrollment. The most common individual pain pattern was described as constant mild pain with episodes of severe pain and was reported in 45% of patients. The most common imaging findings included pancreatic ductal dilatation (68%), atrophy (57%), and calcifications (55%). Imaging findings were categorized as obstructive for 20% and as inflammatory for 25% of cases. The distribution of individual imaging findings was similar among patients with different patterns of pain. The distribution of pain patterns did not differ among clinically relevant groups of imaging findings.
answer:
1
question:
To assess the influence of vascular clamping and ischemia time on long-term post-operative renal function following partial nephrectomy (PN) for cancer in a solitary kidney.', 'This is a retrospective study including 259\xa0patients managed by PN between 1979 and 2010 in 13\xa0centers. Clamping use, technique choice (pedicular or parenchymal clamping), ischemia time, and peri-operative data were collected. Pre-operative and last follow-up glomerular filtration rates were compared. A multivariate analysis using a Cox model was performed to assess the impact of ischemia on post-operative chronic renal failure risk.', 'Mean tumor size was 4.0±2.3cm and mean pre-operative glomerular filtration rate was 60.8±18.9mL/min. One hundred and six patients were managed with warm ischemia (40.9%) and 53\xa0patients with cold ischemia (20.5%). Thirty patients (11.6%) have had a chronic kidney disease. In multivariate analysis, neither vascular clamping (P=0.44) nor warm ischemia time (P=0.1) were associated with a pejorative evolution of renal function. Pre-operative glomerular filtration rate (P<0.0001) and blood loss volume (P=0.02) were significant independent predictive factors of long-term renal failure.
answer:
1
question:
Endoscopic volume reduction of the stomach may provide a minimally invasive alternative for surgical procedures in the treatment of obesity.', 'To assess safety and preliminary effectiveness in the first human application of a novel endoscopic stapling technique.', 'Prospective, observational, phase 1 study.', 'Two university hospitals in The Netherlands.', 'Patients with a body mass index (BMI) of 40 to 45 kg/m(2) or 30 to 39.9 kg/m(2) with obesity-related comorbidity.', 'Gastric volume reduction with an endoscopic stapler.', 'Primary outcome measure was the prevalence of serious or mild adverse events. Reduction of excess body weight after 12 months was assessed as a secondary outcome measure for effectiveness of the procedure.', 'Seventeen patients with a median BMI of 40.2 kg/m(2) (interquartile range [IQR] 37.6-42.8) underwent an endoscopic stapling procedure. Median procedure time was 123 minutes (IQR 95-129). No serious adverse events occurred. Adverse events were gastric pain (n\xa0= 7, range 1-3 days), sore throat (n\xa0= 4, 2-3 days), diarrhea (n\xa0= 4, 2-15 days), nausea (n\xa0= 3, 2-4 days), constipation (n\xa0= 4, 3-14 days), and vomiting (n\xa0= 3, 1-4 days). All adverse events were mild and resolved with conservative treatment within 15 days after surgery. The median percentage excess weight loss in the first year was 34.9% (IQR 17.8-46.6).
answer:
|
1
|
task848_pubmedqa_classification
|
NIv2
|
fs_opt
| 9
|
train
|
Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Example Input: Several viruses have been recently isolated from Mediterranean phlebotomine sand flies; some are known to cause human disease while some are new to science. To monitor the Phlebotomus-borne viruses spreading, field studies are in progress using different sand fly collection and storage methods. Two main sampling techniques consist of CDC light traps, an attraction method allowing collection of live insects in which the virus is presumed to be fairly preserved, and sticky traps, an interception method suitable to collect dead specimens in high numbers, with a risk for virus viability or integrity. Sand flies storage requires a "deep cold chain" or specimen preservation in ethanol. In the present study the influence of sand fly collection and storage methods on viral isolation and RNA detection performances was evaluated experimentally.', 'Specimens of laboratory-reared Phlebotomus perniciosus were artificially fed with blood containing Toscana virus (family Bunyaviridae, genus Phlebovirus). Various collection and storage conditions of blood-fed females were evaluated to mimic field procedures using single and pool samples. Isolation on VERO cell cultures, quantitative Real time-Retro-transcriptase (RT)-PCR and Nested-RT-PCR were performed according to techniques commonly used in surveillance studies.', 'Live engorged sand flies stored immediately at -80 °C were the most suitable sample for phlebovirus identification by both virus isolation and RNA detection. The viral isolation rate remained very high (26/28) for single dead engorged females frozen after 1 day, while it was moderate (10/30) for specimens collected by sticky traps maintained up to 3 days at room temperature and then stored frozen without ethanol. Opposed to viral isolation, molecular RNA detection kept very high on dead sand flies collected by sticky traps when left at room temperature up to 6 days post blood meal and then stored frozen in presence (88/95) or absence (87/88) of ethanol. Data were confirmed using sand fly pools.
Example Output: 0
Example Input: Dehydroepiandrosterone (DHEA) was shown to improve the immune function and survival in experimental sepsis. This study examined the effect of DHEA on intestinal leukocyte recruitment during experimental sepsis, considering factors of gender (male, female and ovariectomized female animals) and combined treatment using orthovanadate (OV) in two models of sepsis.', 'Male rats underwent colon ascendens stent peritonitis (CASP) or endotoxemia. DHEA was administered after induction of experimental sepsis. Changes in leukocyte adherence and capillary perfusion (measured as intestinal functional capillary density - FCD) were assessed using intravital microscopy. While DHEA increased baseline leukocyte adherence in control animals, DHEA reduced leukocyte adherence and increased FCD in male animals with CASP. These effects were also observed in DHEA-treated ovariectomized female rats with CASP. Similarly, the administration of DHEA reduced the number of adherent leukocytes to intestinal venules by 30% in the endotoxemia model. The combined treatment of DHEA and OV significantly reduced adherence of leukocytes to intestinal venules and improved FCD.
Example Output: 0
Example Input: The objective of this study was to experimentally induce inflammatory cysts in an animal model so as to test the hypothesis that radicular cysts develop via the "abscess pathway."', 'Twenty-eight perforated custom-made Teflon cages were surgically implanted into defined locations in the back of 7 Sprague Dawley rats. A week after the implantation of the cages, a known quantity of freshly grown, close allogeneic oral keratinocytes in phosphate buffer solution (PBS) was injected into each cage. One cage per animal was treated as the control that received only epithelial cells. The remaining 3 cages of each animal were trials. Seven days post epithelial cell inoculation; a suspension of 0.2 mL of Fusobacterium nucleatum (10(8) bacteria per mL) was injected into each of the 3 trial cages. Two, 12, and 24 weeks after the inoculation of the bacteria, the cages were taken out, and the tissue contents were fixed and processed by correlative light and transmission electron microscopy. Sixteen of the 21 trial cages could be processed and yielded results.', 'Inoculations of epithelial cells followed 1 week later by F. nucleatum into tissue cages resulted in the development inflammatory cysts in 2 of the 16 cages. The 2 cages contained a total of 4 cystic sites. None of the control cages showed the presence of any cyst-like pathology.
Example Output:
|
1
|
task848_pubmedqa_classification
|
NIv2
|
fs_opt
| 3
|
test
|
Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.
Example input: Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 innate lymphoid cells (ILC2s) represent a recently discovered cell population which has been implicated in driving Th2 inflammation in CRS; however, their relationship with clinical disease characteristics has yet to be investigated. The aim of this study was to identify ILC2s in sinus mucosa in patients with CRS and controls and compare ILC2s across characteristics of disease. A cross-sectional study of patients with CRS undergoing endoscopic sinus surgery was conducted. Sinus mucosal biopsies were obtained during surgery and control tissue from patients undergoing pituitary tumour resection through transphenoidal approach. ILC2s were identified as CD45(+) Lin(-) CD127(+) CD4(-) CD8(-) CRTH2(CD294)(+) CD161(+) cells in single cell suspensions through flow cytometry. ILC2 frequencies, measured as a percentage of CD45(+) cells, were compared across CRS phenotype, endotype, inflammatory CRS subtype and other disease characteristics including blood eosinophils, serum IgE, asthma status and nasal symptom score. 35 patients (40% female, age 48 ± 17 years) including 13 with eosinophilic CRS (eCRS), 13 with non-eCRS and 9 controls were recruited. ILC2 frequencies were associated with the presence of nasal polyps (P = 0.002) as well as high tissue eosinophilia (P = 0.004) and eosinophil-dominant CRS (P = 0.001) (Mann-Whitney U). They were also associated with increased blood eosinophilia (P = 0.005). There were no significant associations found between ILC2s and serum total IgE and allergic disease. In the CRS with nasal polyps (CRSwNP) population, ILC2s were increased in patients with co-existing asthma (P = 0.03). ILC2s were also correlated with worsening nasal symptom score in CRS (P = 0.04).
Example output: 1
Example explanation: There is a line in the passage which say 'The aim of this study was...' which suggests that it is talking about a specific aim. Hence, the output should be 1 as this passage has a definite aim.
Q: Despite advances in the understanding of diabetic retinopathy, the nature and time course of molecular changes in the retina with diabetes are incompletely described. This study characterized the functional and molecular phenotype of the retina with increasing durations of diabetes.', 'Using the streptozotocin-induced rat model of diabetes, levels of retinal permeability, caspase activity, and gene expression were examined after 1 and 3 months of diabetes. Gene expression changes were identified by whole genome microarray and confirmed by qPCR in the same set of animals as used in the microarray analyses and subsequently validated in independent sets of animals. Increased levels of vascular permeability and caspase-3 activity were observed at 3 months of diabetes, but not 1 month. Significantly more and larger magnitude gene expression changes were observed after 3 months than after 1 month of diabetes. Quantitative PCR validation of selected genes related to inflammation, microvasculature and neuronal function confirmed gene expression changes in multiple independent sets of animals.
A:
|
0
|
task848_pubmedqa_classification
|
NIv2
|
fs_opt
| 3
|
validation
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Q: But it's not that simple.
A:
|
Mas não é assim tão simples.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 9
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Example input: Astronomers now believe that every star in the galaxy has a planet, and they speculate that up to one fifth of them have an Earth-like planet that might be able to harbor life, but we haven't seen any of them.
Example output: Os astrónomos acreditam que cada estrela da galáxia tem um planeta, e especulam que até um quinto deles tem um planeta do tipo da Terra que poderá ter vida, mas ainda não vimos nenhum deles.
Example explanation: The English sentence is correctly translated into Portugese, because the meaning is preserved.
Q: It was printed in 1983 by Chuck Hull, who invented 3D printing.
A:
|
Foi impresso em 1983 pelo Senhor Chuck Hull que inventou a impressão 3D.
|
task1094_ted_translation_en_pt
|
NIv2
|
fs_opt
| 3
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Ex Input:
But they didn't spend a lot of time reliving the glory days.
Ex Output:
discussões de grupo sobre o lançamento de novos produtos,
Ex Input:
And one of the first things that surprised me when I got on board Kendal was, where are all the people?
Ex Output:
E uma das primeiras coisas que me surpreendeu quando embarquei no Kendal foi: onde estavam todas as pessoas?
Ex Input:
He stuck handlebars into a bike seat and called it "" Bull's Head. "" Sounds convincing, right?
Ex Output:
|
Ele juntou um guiador e um assento duma bicicleta
|
task1094_ted_translation_en_pt
|
NIv2
|
fs_opt
| 1
|
train
|
Given the task definition and input, reply with output. You are given a sentence in English. Your job is to translate the English sentence into Portugese.
But you have to have a device to go up in the trees.
|
Mas era necessário pôr um aparelho no cimo das árvores.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 5
|
train
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Q: And that's what we've been able to do now, but in three dimensions.
A:
|
E isso é o que fizemos agora, mas em três dimensões.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 9
|
train
|
Instructions: You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Input: The bridge looks like it's crossing the river east to west.
Output:
|
A ponte parece atravessar o rio de este para oeste.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 3
|
train
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Problem:They were just influenced by the adults around them.
Solution:
|
Eles apenas foram influenciados pelos adultos à sua volta.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 8
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Example: Astronomers now believe that every star in the galaxy has a planet, and they speculate that up to one fifth of them have an Earth-like planet that might be able to harbor life, but we haven't seen any of them.
Example solution: Os astrónomos acreditam que cada estrela da galáxia tem um planeta, e especulam que até um quinto deles tem um planeta do tipo da Terra que poderá ter vida, mas ainda não vimos nenhum deles.
Example explanation: The English sentence is correctly translated into Portugese, because the meaning is preserved.
Problem: When I was a kid, no one wore a seat belt.
|
Solution: Quando eu era criança, ninguém usava cinto de segurança.
|
task1094_ted_translation_en_pt
|
NIv2
|
fs_opt
| 5
|
train
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Problem:And I'll just play you part of a mockup of it, but you get a sense of what it's like to actually enter into that moment in time and be transported back in history.
Solution:
|
E vou só passar uma parte do modelo disso mas conseguem ter uma ideia do que é entrar nesse momento no tempo e ser-se transportado para trás na História.
|
task1094_ted_translation_en_pt
|
NIv2
|
zs_opt
| 8
|
test
|
You are given a sentence in English. Your job is to translate the English sentence into Portugese.
Let me give you an example: Astronomers now believe that every star in the galaxy has a planet, and they speculate that up to one fifth of them have an Earth-like planet that might be able to harbor life, but we haven't seen any of them.
The answer to this example can be: Os astrónomos acreditam que cada estrela da galáxia tem um planeta, e especulam que até um quinto deles tem um planeta do tipo da Terra que poderá ter vida, mas ainda não vimos nenhum deles.
Here is why: The English sentence is correctly translated into Portugese, because the meaning is preserved.
OK. solve this:
From Wall Street to Mohammed Mahmoud Street, it has never been more important to understand the aspirations of ordinary people.
Answer:
|
De Wall Street à Rua Mohammed Mahmoud, nunca foi tão importante compreender as aspirações do cidadão comum.
|
task1094_ted_translation_en_pt
|
NIv2
|
fs_opt
| 8
|
validation
|
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
મારા વ્હાલા ભાઈઓ બહેનો, અમે સખત નિર્ણયો લેવાનું સામર્થ્ય ધરાવીએ છે કારણ કે દેશ હિત અમારા માટે સર્વોપરી છે.
|
വളരെ കഠിനമായ തീരുമാനങ്ങള് എടുക്കാനും കഴിവുള്ളവരാണ് ഞങ്ങള്, എന്തെന്നാല് ദേശീയതാല്പര്യമാണ് നമ്മുടെ മുന്ഗണന.
|
task1041_pib_translation_gujarati_malayalam
|
NIv2
|
zs_opt
| 0
|
train
|
Detailed Instructions: A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
Q: એપ્રિલના છેલ્લા સપ્તાહમાં કનેકટિવિટી સાથે સંકળાયેલા માળખાગત સુવિધાઓના ક્ષેત્રો અંગે કરાયેલી સમીક્ષા પછી હાથ ધરાયેલી આ સમીક્ષા અંદાજે ત્રણ કલાક સુધી ચાલી હતી અને તેમાં પ્રધાનમંત્રી શ્રીની કચેરી, નીતિ આયોગ, અને માળખાગત સુવિધાઓ સાથે સંબંધ ધરાવતા તમામ મંત્રાલયોના ટોચના અધિકારીઓ હાજર રહ્યા હતા.
A:
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കണക്ടിവിറ്റിയുമായി ബന്ധപ്പെട്ട അടിസ്ഥാന സൗകര്യ മേഖലയിലെ പുരോഗതി വിലയിരുത്താന് കഴിഞ്ഞ മാസം അവസാന വാരം ചേര്ന്ന യോഗത്തിന്റെ തുടര്ച്ചയായി വിളിച്ച് ചേര്ത്ത മൂന്ന് മണിക്കൂര് നീണ്ട യോഗത്തില് പ്രധാനമന്ത്രിയുടെ ഓഫീസ്, നിതി ആയോഗ്, കേന്ദ്ര ഗവണ്മെന്റിന്റെ എല്ലാ അടിസ്ഥാന സൗകര്യ മന്ത്രാലയങ്ങള്
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task1041_pib_translation_gujarati_malayalam
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NIv2
|
zs_opt
| 9
|
train
|
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
આ સંખ્યા આપણને બહુ જ સામાન્ય લાગી શકે છે પરંતુ જો દુનિયાના હિસાબથી હું કહું, જો એક ચીનને આપણે છોડી દઈએ તો ભારતમાં દુનિયાના કોઈપણ દેશની વસ્તી થી વધારે લોકોએ વોટિંગ કર્યું હતું.
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ഈ സംഖ്യ സാധാരണമെന്നു നമുക്കു തോന്നാം. എന്നാല് ഇത് ലോകത്താകെയുള്ള ജനസംഖ്യ കണക്കിലെടുത്താല് ചൈന ഒഴിച്ച് മറ്റേതൊരു രാജ്യത്തെക്കാളുമധികം ആളുകള് ഭാരതത്തില് വോട്ടു ചെയ്തു എന്നു കാണാം.
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
zs_opt
| 0
|
train
|
Detailed Instructions: A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
See one example below:
Problem: ઊર્જાનો પુરવઠો, ઊર્જાનાં સ્રોતો અને ઊર્જાનાં વપરાશની પદ્ધતિઓ બદલાઈ રહી છે
Solution: Energy ർജ്ജ വിതരണ, energy ർജ്ജ സ്രോതസ്സുകൾ, energy ർജ്ജ ഉപഭോഗ രീതികൾ മാറുകയാണ്
Explanation: Correct translation for given sentence. Input sentence means 'Energy supply, energy sources and energy consumption methods are changing' which is the same as the output sentence.
Problem: ઓબીસી આયોગને વર્ષોથી બંધારણીય સ્થાન માટેની માંગ ચાલી રહી હતી.
Solution:
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സി കമ്മിഷന് ഭരണഘടനാപദവി നല്കണമെന്ന ആവശ്യം വര്ഷങ്ങളായി നിലനില്ക്കുന്നതാണ്.
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
fs_opt
| 4
|
train
|
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
Ex Input:
સરકારના સ્વચ્છ, તંદુરસ્ત અને ઈલેક્ટ્રીફાઈડ ભારતની પ્રતિબદ્ધતાને ધ્યાનમાં રાખીને પ્રધાનમંત્રી ગામડાઓના એવા સરપંચોને પણ અભિનંદન પાઠવશે કે જેમણે 100 ટકા ધુમાડા રહિત રસોડા, મિશન ઇન્દ્રધનુષ અંતર્ગત 100 ટકા રસીકરણ અને સૌભાગ્ય યોજના હેઠળ 100 ટકા વિદ્યુતીકરણના લક્ષ્યાંકને પ્રાપ્ત કર્યો છે.
Ex Output:
സംശുദ്ധവും, ആരോഗ്യപൂര്ണവും, വൈദ്യുതീകരിച്ചതുമായ ഒരു ഇന്ത്യയെന്ന ഗവണ്മെന്റിന്റെ പ്രതിബദ്ധത പാലിച്ച 100 ശതമാനം പുകയില്ലാത്ത അടുക്കളകള്, ഇന്ദ്രധനുഷ് ദൗത്യത്തിന് കീഴില് 100 ശതമാനം പ്രതിരോധ കുത്തിവയ്പ്പ് , സൗഭാഗ്യ പദ്ധതിക്ക് കീഴില് 100 ശതമാനം വൈദ്യുതീകരണം എന്നിവ കൈവരിച്ച ഗ്രമങ്ങളിലെ ഗ്രാമ മുഖ്യന്മാരെ പ്രധാനമന്ത്രി ആദരിക്കും.
Ex Input:
મંત્રીમંડળે યુરોપિયન બેંક ફોર રિકન્સ્ટ્રક્શન એન્ડ ડેવલપમેન્ટ માટે ભારતનાં સભ્યપદની મંજૂરી આપી
Ex Output:
പുനര് നിര്മ്മാണത്തിനും വികസനത്തിനുമായുള്ള യൂറോപ്യന് ബാങ്കില് ഇന്ത്യ അംഗത്വമെടുക്കും
Ex Input:
મારા પ્રિય દેશવાસીઓ, આજે આપણે વિકાસની સાથે, શાંતિ અને સુરક્ષા જે તેની અનિવાર્ય બાબત છે.
Ex Output:
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ലോകത്തെ എല്ലാ രാജ്യങ്ങളും ഇന്ത്യയുടെ ഏതെങ്കിലും ഒരു ഉല്പ്പന്നം ഇറക്കുമതിചെയ്യുന്നതായി എന്തുകൊണ്ട് നമുക്ക് സ്വപ്നം കണ്ടുകൂടാ, എന്തുകൊണ്ട് ഇന്ത്യയിലെ ഓരോ ജില്ലയ്ക്കും എന്തെങ്കിലും ഉല്പ്പന്നങ്ങള് കയറ്റി അയച്ചുകൂടാ?
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
fs_opt
| 1
|
train
|
You will be given a definition of a task first, then some input of the task.
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
વર્ષ 11માં આર્થિક સુધારા શરૂ થયા પછી અત્યાર સુધીની કોઈ પણ સરકાર કરતાં હાલની સરકારે છેલ્લાં પાંચ વર્ષમાં વધારે વૃદ્ધિ કરી છે.
Output:
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ലോകത്തിലെ ഏറ്റവും വേഗം വളരുന്ന സമ്പദ്വ്യവസ്ഥയാണ് ഇന്ത്യയുടേത്. 1991ലെ സാമ്പത്തിക പരിഷ്കരണം മുതല് ഇങ്ങോട്ടുള്ള മറ്റേതൊരു ഗവണ്മെന്റിനും നേടിയെടുക്കാന് സാധിക്കാത്തവിധമുള്ള മൊത്തം ആഭ്യന്തര ഉല്പാദനം സാധ്യമാക്കാന് ഈ ഗവണ്മെന്റിന്റെ കാലത്തു സാധിച്ചു.
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
zs_opt
| 1
|
train
|
TASK DEFINITION: A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
PROBLEM: વધુમાં, પત્રાતું સુપર થર્મલ પાવર સ્ટેશનનો પ્રથમ તબક્કો 2022-23ના ચોથા ત્રિમાસ દરમિયાન શરુ થઇ જવાની શક્યતા છે.
SOLUTION: പട്രാടു താപവൈദ്യുത നിലയത്തിന്റെ ആദ്യഘട്ടം 2022-23 ലെ നാലാംപാദത്തില് കമ്മിഷന് ചെയ്യാനാകും.
PROBLEM: છેલ્લાં પાંચ વર્ષ દરમિયાન દેશમાં વિસ્તૃત આર્થિક સ્થિરતાનો શ્રેષ્ઠ તબક્કો જોવા મળ્યો હતો.
SOLUTION: കഴിഞ്ഞ അഞ്ചു വര്ഷത്തിനിടെ രാജ്യം ഏറ്റവും മികച്ച മാക്രോ-ഇക്കണോമിക് സുസ്ഥിരത നേടി.
PROBLEM: તેમણે કહ્યું હતુ કે, પીએમ-કિસાન – કિસાન સમ્માન નિધિ અને ખેડૂત કેન્દ્રિત અન્ય યોજનાઓનાં લાભો નિયત સમયમર્યાદામાં ઇચ્છિત લાભાર્થીઓ સુધી પહોંચાડવા પડશે.
SOLUTION:
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-കിസാന്-കിസാന് സമ്മാന് നിധി തുടങ്ങിയ പദ്ധതികളുടെ ഗുണം നിശ്ചിത സമയത്തിനകം ഗുണഭോക്താക്കള്ക്കു ലഭിക്കണമെന്ന് അദ്ദേഹം വ്യക്തമാക്കി.
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
fs_opt
| 8
|
train
|
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
બીજા દેશો સાથે આપણાં સંબંધોમાં પણ આપણે સહયોગની આજ ભાવનાનો પરિચય આપીએ છીએ.
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ഈ കാലഘട്ടം ഇങ്ങനെയാണ് എന്നതു വളരെ വലിയ നേട്ടമാണ്. ഈ വര്ഷം നമുക്കു വളരെ പ്രധാനപ്പെട്ടതാണ്.
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
zs_opt
| 0
|
train
|
A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
બંને પક્ષોની સંમતિ હોય એવા કાર્યક્રમો પર ચર્ચાવિચારણા, સંમેલન અને અન્ય બેઠકોનું આયોજન કરવું.
സെമിനാറുകള്, കോണ്ഫറന്സുകള്, അംഗീകരിച്ച കാര്യപരിപാടികളിലെ മറ്റു യോഗങ്ങള് എന്നിവ സംഘടിപ്പിക്കല്
આ સમજૂતીનો ઉદ્દેશ ભારત અને યુકે વચ્ચે ટકાઉ શહેરી વિકાસ અંગે સંસ્થાકિય સહયોગ માટે સુગમતા કરી આપવાનો તથા તેને મજબૂત બનાવવાનો છે.
ഇന്ത്യയും യു. കെയും തമ്മില് സുസ്ഥിര നഗരവികസനത്തിനുള്ള സ്ഥാപനസഹകരണത്തിന് സൗകര്യമൊരുക്കുകയും അത് ശക്തമാക്കുകയുമാണ് കരാറിന്റെ ഉദ്ദേശ്യം.
રાષ્ટ્રીય અને આંતરરાષ્ટ્રીય સ્તરે માન્યતાપ્રાપ્ત લવાદો, સમાધાનકર્તાઓ અને મધ્યસ્થો અથવા નિષ્ણાતો જેવા સર્વેક્ષણકર્તાઓ અને તપાસકર્તાઓની પેનલ બનાવીને રાખવી;
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സി) രാജ്യാന്തര തലത്തിലെ അക്രഡിറ്റഡ് ആര്ബിട്രര്മാര്, അനുരജ്നക്കാര്, മദ്ധ്യസ്ഥന് എന്നിവരുടെ, അല്ലെങ്കില് സര്വേയര്മാരെപ്പോലെയും അന്വേഷകരെപ്പോലെയും ഉള്ള വിദഗ്ധരുടെ പാനല് സൂക്ഷിക്കുക
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task1041_pib_translation_gujarati_malayalam
|
NIv2
|
fs_opt
| 0
|
test
|
Detailed Instructions: A text is given in Gujarati. Translate it from the Gujarati language to the Malayalam language. The translation must not omit or add information to the original sentence.
Q: પ્રધાનમંત્રી શ્રી નરેન્દ્ર મોદીની અધ્યક્ષતામાં કેન્દ્રીય મંત્રીમંડળે આજે ભારત અને નેપાળ વચ્ચે ભારત-નેપાળની સરહદ પર મેચી નદી પર નવા પુલનું નિર્માણ શરૂ કરવા ખર્ચની વહેંચણી કરવા, શીડ્યુલ્સ અને સલામતીના મુદ્દાઓ પર અમલીકરણ સમજૂતી કરવા થયેલા સમજૂતીકરાર (એમઓયુ)ને મંજૂરી આપી હતી.
A:
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ഇന്ഡോ-നേപ്പാള് അതിര്ത്തിയില് മേച്ചി നദിക്ക് കുറുകെ പുതിയൊരു പാലം നിര്മ്മിക്കുന്നതിനുള്ള ചെലവ് പങ്കിടല്, സമയക്രമം നിശ്ചയിക്കല്, സുരക്ഷാ വിഷയങ്ങള് തുടങ്ങിയവയുമായി ബന്ധപ്പെട്ട നടപ്പാക്കല് സംവിധാനത്തിന് രൂപം നല്കാന് ഇന്ത്യയും നേപ്പാളും തമ്മില് ഒപ്പിടേണ്ട ധാരണാപത്രത്തിന് പ്രധാനമന്ത്രി ശ്രീ. നരേന്ദ്ര മോദിയുടെ അദ്ധ്യക്ഷതയില് ചേര്ന്ന കേന്ദ്ര മന്ത്രിസഭാ യോഗം അംഗീകാരം നല്കി.
|
task1041_pib_translation_gujarati_malayalam
|
NIv2
|
zs_opt
| 9
|
validation
|
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Context: A corporate chain restaurant bought out a local restaurant. The townspeople were upset.
Sentence: Most said they'd never eat there.
Character: Townspeople
sad
Context: None
Sentence: Anna started college rooming with two guys and two girls.
Character: Male roommate
None
Context: Thomas was a steamboat captain.
Sentence: He'd find fish anywhere, just to prove his metal.
Character: Fish
|
None
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 0
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Context: None
Sentence: Kim had passion for fashion.
Character: Kim
Output:
|
intrigued
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
zs_opt
| 1
|
train
|
Teacher:In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Teacher: Now, understand the problem? Solve this instance: Context: I was playing baseball in the park with friends. Too many people kept cutting through the field. We were so annoyed that we stopped playing. I randomly started walking through the trees at the edge of the park.
Sentence: I stumbled upon a huge secret field where we could play in peace.
Character: Many people
Student:
|
satisfied
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
zs_opt
| 6
|
train
|
Part 1. Definition
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Part 2. Example
Context: A cook was carrying an armful of oranged in the kitchen.
Sentence: He dropped one on the floor by accident.
Character: Cook
Answer: annoyed
Explanation: The cook dropped one orange on the floor, so, he must feel annoyed at that time.
Part 3. Exercise
Context: None
Sentence: As kids, Charles and Steve fantasized about riding motorcycles.
Character: Charles
Answer:
|
eager
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Q: Context: Tom was skinny dipping with friends.
Sentence: They all got out before him.
Character: Tom's friends
A: happy
****
Q: Context: My daughter was going to a wedding last Monday.
Sentence: Her friend was pregnant.
Character: Friend
A: proud
****
Q: Context: Lillie was always nervous leaving work.
Sentence: Her car was parked in a parking garage.
Character: Lillie
A:
|
threatened
****
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 4
|
train
|
Given the task definition and input, reply with output. In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Context: The Smith family went to the same beach every summer.
Sentence: This summer there were 3 sea turtle nests in front of their cottage.
Character: Smith family
|
excited
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
zs_opt
| 5
|
train
|
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
--------
Question: Context: None
Sentence: Ann went to the beach with her family yesterday.
Character: Ann
Answer: friendly
Question: Context: Jose wanted to try fishing for the first time. He asked his dad to show him how because his dad went every week.
Sentence: Together they went to the store and got all the supplies they needed.
Character: Dad
Answer: excited
Question: Context: Laura's cat had run away from home.
Sentence: Laura chased her cat to a tree.
Character: Firefighter
Answer:
|
None
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
One example is below.
Q: Context: A cook was carrying an armful of oranged in the kitchen.
Sentence: He dropped one on the floor by accident.
Character: Cook
A: annoyed
Rationale: The cook dropped one orange on the floor, so, he must feel annoyed at that time.
Q: Context: There once was a man named Larry Butterfrog. He went down to buy World of Warcraft. However, he lacked the money.
Sentence: So, he had to go to his mom, who was miffed.
Character: Larry butterfrog
A:
|
needy
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 9
|
train
|
In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Input: Consider Input: Context: None
Sentence: Bob saw the finish line coming up ahead.
Character: Bob
Output: excited
Input: Consider Input: Context: None
Sentence: Monty the cat had a large growth removed from his face.
Character: Monty's owners
Output: None
Input: Consider Input: Context: Bruce started dating this girl Sara.
Sentence: Sara was really into fitness and Bruce was not.
Character: Bruce
|
Output: bored
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
fs_opt
| 2
|
test
|
Instructions: In this task, you're given a context, a sentence, and a character. The sentence describes an action or job of the given character. Also, the context provides more information about the sentence or the character. Your task is to return one of the emotions which are expressed by the Character in the given sentence. For that you can use the Context; however, Context is not provided in all the inputs. Also, in some inputs, there can be zero emotion; for that return 'None'.
Input: Context: I decided to go on a trip to Key West last winter.
Sentence: I made reservations for the trip and a week later flew to Miami.
Character: I (myself)
Output:
|
excited
|
task293_storycommonsense_emotion_text_generation
|
NIv2
|
zs_opt
| 3
|
validation
|
Given a passage, write a short incorrect summary based on the passage.
[Q]: foreign ministers of the group of eight countries began talks here thursday expected to raise pressure on iran over its nuclear program , as moscow sought to prevent next month 's g# summit becoming a magnet for criticism of russia 's democratic credentials .
[A]: rand slightly weaker against dollar in early trade
[Q]: china has executed five people who had been sentenced to death for drugs trafficking , state media reported on monday .
[A]: iran tops agenda at g# ministers meeting by nick coleman UNK UNK french minister on hamas arrests
[Q]: the us space shuttle atlantis separated from the orbiting russian mir space station early saturday , after three days of test runs for life in a future space facility , nasa announced .
[A]:
|
president mandela pilloried in nigeria
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 5
|
train
|
instruction:
Given a passage, write a short incorrect summary based on the passage.
question:
japanese prime minister junichiro koizumi wednesday portrayed the japan-us alliance as the foundation for better relations with china and south korea .
answer:
foreign exchange rates in indonesia
question:
the canadian economy will bounce back in the #th quarter with an aggressive monetary policy , economists with canada 's td bank said in their latest quarterly forecast released monday .
answer:
northeast china province fights drought
question:
get ready to think about office politics and family affairs in UNK terms again .
answer:
|
on the road to trenton # strive to excite
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 9
|
train
|
Detailed Instructions: Given a passage, write a short incorrect summary based on the passage.
Problem:the opposition labor party 's lead has dropped to its lowest level for four years , according to the latest UNK poll for the sunday times .
Solution:
|
eu ambassadors to return to iran soon
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
zs_opt
| 8
|
train
|
Given the task definition and input, reply with output. Given a passage, write a short incorrect summary based on the passage.
ding UNK believes in miracles .
|
former governor : britain ignored hong kong democracy desires by ted anthony
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
zs_opt
| 5
|
train
|
Given a passage, write a short incorrect summary based on the passage.
it was a day for fifth-graders to learn how not to be fourth-graders , for a beginning elementary teacher to chase away butterflies in the stomach and face ## #-year-olds , for the mayor and schools chancellor to go back to school and read to second-graders , and for a group of high school students to marvel at their brand-new building .
|
a preview of what could be the best fall in years
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
zs_opt
| 0
|
train
|
Given the task definition and input, reply with output. Given a passage, write a short incorrect summary based on the passage.
hong kong 's key stock index plunged tuesday to its lowest level in more than five years because investors haunted by fears of a weakening yen continued to dump shares , traders said .
|
san marino UNK UNK results from saturday 's semifinal play in the
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
zs_opt
| 5
|
train
|
instruction:
Given a passage, write a short incorrect summary based on the passage.
question:
denmark 's poul-erik hoyer completed his hat-trick of men 's singles badminton titles at the european championships , winning the final here on saturday .
answer:
india 's ruling party headed for election disaster : poll
question:
boston celtics guard ray allen has been named the alternative to file in the vacancy left by injured orlando magic guard jameer nelson for the all-star game , said the nba on thursday .
answer:
#nd ld : final results confirm kadima 's lead in israeli election
question:
france 's sebastian bourdais claimed his ##th career champ-car pole position here saturday , leading qualifiers for sunday 's long beach champ-car grand prix .
answer:
|
russian mine toll rises to ###
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 9
|
train
|
Detailed Instructions: Given a passage, write a short incorrect summary based on the passage.
See one example below:
Problem: four east timorese youths who scaled the french embassy 's fence here thursday , left the embassy on their way to portugal friday .
Solution: UNK latest east javanese asylum seekers leave for peru
Explanation: The example is correct, as the location names are different from the passage
Problem: southern california motorists again wasted more time in stalled traffic than drivers did anywhere else in the nation , and the region is expected to continue its dismal ranking for years , experts said monday .
Solution:
|
is le UNK the next tuscany maybe
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 4
|
train
|
Detailed Instructions: Given a passage, write a short incorrect summary based on the passage.
See one example below:
Problem: four east timorese youths who scaled the french embassy 's fence here thursday , left the embassy on their way to portugal friday .
Solution: UNK latest east javanese asylum seekers leave for peru
Explanation: The example is correct, as the location names are different from the passage
Problem: australia 's agl energy said thursday it had made a preliminary merger approach to origin energy , paving the way for a ## billion dollar -lrb- ##.# billion us -rrb- deal that would create the country 's largest gas and electricity supplier .
Solution:
|
gazprom chevron set up joint venture
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 4
|
test
|
Given a passage, write a short incorrect summary based on the passage.
[EX Q]: italian police have uncovered a scheme by which the families of some ### illegal immigrant minors were duped into paying a gang for their freedom , police said monday .
[EX A]: iraqi president syrian baath party strive for better ties
[EX Q]: taiwan 's deputy representative to the united states , tsai UNK , stressed on tuesday that holding referendums is one of the basic human rights of the people of taiwan and it has nothing to do with taiwan 's independence or possible unification with mainland china .
[EX A]: umc november sales down on stronger new taiwan dollar
[EX Q]: hungry tigers and lions have been attacking each other at a chinese zoo that says it ca n't afford to feed its animals because of a slump in visitors amid sars fears .
[EX A]:
|
russian minister offers united states to join moscow 's nuclear deal with iran
|
task1579_gigaword_incorrect_summarization
|
NIv2
|
fs_opt
| 6
|
validation
|
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
Let me give you an example: عدد 30 را بر نیم تقسیم می کنیم و سپس عدد 10 را به آن اضافه می کنیم. نتیجه کدام گزینه است؟
The answer to this example can be: math_and_logic
Here is why: This is a good example. You need mathematical knowledge to answer this question.
OK. solve this:
اگر تعداد روزهایی که یک کارمند از تعطیلات رسمی و مرخصی در یک سال استفاده کرده است با هفته به حساب آید، ۷۸ واحد از تعداد روزهای ذکر شده کمتر است. تعداد روزها کدام است؟
Answer:
|
math_and_logic
|
task474_parsinlu_mc_classification
|
NIv2
|
fs_opt
| 8
|
train
|
Q: In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
همه گزینهها جز گزینه .............. از جهتی مشابهت دارند.
A:
|
literature
|
task474_parsinlu_mc_classification
|
NIv2
|
zs_opt
| 7
|
train
|
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
عدد 30 را بر نیم تقسیم می کنیم و سپس عدد 10 را به آن اضافه می کنیم. نتیجه کدام گزینه است؟
Solution: math_and_logic
Why? This is a good example. You need mathematical knowledge to answer this question.
New input: نزدیکترین و کوچک ترین سیاره به خورشید کدام است؟
Solution:
|
common_knowledge
|
task474_parsinlu_mc_classification
|
NIv2
|
fs_opt
| 0
|
train
|
Given the task definition and input, reply with output. In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
در کدام بیت، حذف فعل به قرینه لفظی صورت گرفته است؟
|
literature
|
task474_parsinlu_mc_classification
|
NIv2
|
zs_opt
| 5
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
یک یخچال به ۲۷۳۰ تومان با %۳۰ سود فروخته شده است اگر این یخچال با ۱۰ درصد سود فروخته شود،
قیمت آن چقدر است؟
Output:
|
math_and_logic
|
task474_parsinlu_mc_classification
|
NIv2
|
zs_opt
| 1
|
train
|
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
بهانه خسروپرويز براي حمله به روم چه بود؟
|
common_knowledge
|
task474_parsinlu_mc_classification
|
NIv2
|
zs_opt
| 0
|
train
|
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
Q: جزایر لانگرهانس در کجا قرار دارد؟
A: common_knowledge
****
Q: همه گزینه ها جزگزینه ... به زبان فارسی است:
A: literature
****
Q: كدام واژه ها باهم مترادف نيستند
A:
|
literature
****
|
task474_parsinlu_mc_classification
|
NIv2
|
fs_opt
| 4
|
train
|
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
One example is below.
Q: عدد 30 را بر نیم تقسیم می کنیم و سپس عدد 10 را به آن اضافه می کنیم. نتیجه کدام گزینه است؟
A: math_and_logic
Rationale: This is a good example. You need mathematical knowledge to answer this question.
Q: روز دانش آموز مقارن با چه تاریخی میباشد؟
A:
|
common_knowledge
|
task474_parsinlu_mc_classification
|
NIv2
|
fs_opt
| 9
|
train
|
Detailed Instructions: In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
Q: حاصل عبارت ۴ - ۰ برابر است با ؟
A:
|
math_and_logic
|
task474_parsinlu_mc_classification
|
NIv2
|
zs_opt
| 9
|
test
|
In this task, you will be presented with a question in Persian. Based on the knowledge you need to answer the question, classify the question into "math_and_logic", "literature", or "common_knowledge".
One example: عدد 30 را بر نیم تقسیم می کنیم و سپس عدد 10 را به آن اضافه می کنیم. نتیجه کدام گزینه است؟
Solution is here: math_and_logic
Explanation: This is a good example. You need mathematical knowledge to answer this question.
Now, solve this: سود پرداختی مربوط به سال سوم مربوط به ارزش نهائی اقساط ۲۰۰۰۰ ریالی با نرخ تسعیر سالانه %۱۰ چند ریال است؟
Solution:
|
math_and_logic
|
task474_parsinlu_mc_classification
|
NIv2
|
fs_opt
| 6
|
validation
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
One example: कथन: भगोड़े पुलिस से छिप गए।
विकल्प A: पुलिस ने मामला छोड़ दिया।
विकल्प B: भगोड़े बड़े पैमाने पर बने रहे।
Solution is here: विकल्प B
Explanation: Option B makes more sense as if a fugitive hides from the police, it implies that the fugitive is at large and not that the police will drop the case.
Now, solve this: कथन: महिला ने एक डिप्लोमा प्राप्त किया।
विकल्प A: उसने कॉलेज में दाखिला लिया।
विकल्प B: उसने कॉलेज से स्नातक किया।
Solution:
|
विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
fs_opt
| 6
|
train
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Input: Consider Input: कथन: मैंने गर्म पानी के नीचे आइस क्यूब चलाया।
विकल्प A: आइस क्यूब मेरी उंगलियों से चिपक गया।
विकल्प B: आइस क्यूब गायब हो गया।
Output: विकल्प B
Input: Consider Input: कथन: महिला ने अपने कान ढक लिए।
विकल्प A: फायर अलार्म बजा।
विकल्प B: महिला को धुएं की गंध आई।
Output: विकल्प A
Input: Consider Input: कथन: दोस्तों की बहस अंतर्मन पर खिंच गई।
विकल्प A: दोस्तों ने नजरें मिला कर देखा।
विकल्प B: दोस्त बाल काट रहे थे।
|
Output: विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
fs_opt
| 2
|
train
|
Definition: Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Input: कथन: आदमी ने भारी बक्सा उठा लिया।
विकल्प A: उसने अपनी पीठ बाहर कर ली।
विकल्प B: उसने अपनी पीठ खुजला ली।
Output:
|
विकल्प A
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
zs_opt
| 2
|
train
|
Given the task definition and input, reply with output. Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
कथन: मेरी साँसों से लहसुन की बदबू आ रही थी।
विकल्प A: मैंने अपना मुँह पोंछ लिया।
विकल्प B: मैंने अपने दांत साफ़ किए।
|
विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
zs_opt
| 5
|
train
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Q: कथन: मेरा पेट फूल गया।
विकल्प A: मैं नाश्ता करना भूल गया।
विकल्प B: मैं नाश्ते से भर गया था।
A:
|
विकल्प A
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
zs_opt
| 4
|
train
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Q: कथन: सुरक्षा गार्ड ने निगरानी फुटेज को फिर से देखा।
विकल्प A: निगरानी कैमरा ध्यान से बाहर था।
विकल्प B: उसने कुछ संदिग्ध गतिविधि देखी।
A: विकल्प B
****
Q: कथन: महिला ने पुरुष की तारीफ की।
विकल्प A: उसने छींक दी।
विकल्प B: वह शरमा गया।
A: विकल्प B
****
Q: कथन: चालक ने स्टीयरिंग व्हील घुमाया।
विकल्प A: कार रुकी।
विकल्प B: कार पलट गई।
A:
|
विकल्प B
****
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
fs_opt
| 4
|
train
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
कथन: घर में पानी का पाइप फट गया।
विकल्प A: पानी अस्वाभाविक था।
विकल्प B: पाइप जम गया।
|
विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
zs_opt
| 0
|
train
|
Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Let me give you an example: कथन: भगोड़े पुलिस से छिप गए।
विकल्प A: पुलिस ने मामला छोड़ दिया।
विकल्प B: भगोड़े बड़े पैमाने पर बने रहे।
The answer to this example can be: विकल्प B
Here is why: Option B makes more sense as if a fugitive hides from the police, it implies that the fugitive is at large and not that the police will drop the case.
OK. solve this:
कथन: आदमी ने पेंटिंग पर विचार किया।
विकल्प A: उसने खौफ में महसूस किया।
विकल्प B: वह ढह गया।
Answer:
|
विकल्प A
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
fs_opt
| 8
|
train
|
Detailed Instructions: Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
See one example below:
Problem: कथन: भगोड़े पुलिस से छिप गए।
विकल्प A: पुलिस ने मामला छोड़ दिया।
विकल्प B: भगोड़े बड़े पैमाने पर बने रहे।
Solution: विकल्प B
Explanation: Option B makes more sense as if a fugitive hides from the police, it implies that the fugitive is at large and not that the police will drop the case.
Problem: कथन: टॉर्च मर चुका था।
विकल्प A: मैं इसे अलग ले गया।
विकल्प B: मैंने बैटरी बदल दी।
Solution:
|
विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
fs_opt
| 4
|
test
|
Teacher:Given a premise and two alternatives in Hindi, choose the alternative that is either a plausible cause or effect of the situation described by the premise. The premise is the 'कथन' field and the alternatives are the 'विकल्प A' and 'विकल्प B' fields. The output should either be "विकल्प A" or "विकल्प B" based on your judgment.
Teacher: Now, understand the problem? Solve this instance: कथन: कोच ने अपने खिलाड़ी को एक उच्च पांच दिया।
विकल्प A: खिलाड़ी को एक दंड मिला।
विकल्प B: खिलाड़ी ने एक अंक हासिल किया।
Student:
|
विकल्प B
|
task938_copa_hi_commonsense_reasoning
|
NIv2
|
zs_opt
| 6
|
validation
|
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