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In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Let me give you an example: در جشنواره متاسفانه نتونستم ببینم ولی دیشب در اکران فیلم های جشنواره فجر در پردیس چارسو موفق به دیدن فیلم شدم. چه فیلم خوبی از فضای بصری زیبا و چشم نواز ، تا بازی فوق العاده حامد بهداد.....
The answer to this example can be: positive
Here is why: This is a good example. The sentiment of the review is positive toward the movie.
OK. solve this:
فیلم خوبیه اما باید بیشتر به اتفاقات و روابط موجود در فیلم پرداخته می شد...
Answer:
|
mixed
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 8
|
train
|
Teacher: In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Teacher: Now, understand the problem? If you are still confused, see the following example:
در جشنواره متاسفانه نتونستم ببینم ولی دیشب در اکران فیلم های جشنواره فجر در پردیس چارسو موفق به دیدن فیلم شدم. چه فیلم خوبی از فضای بصری زیبا و چشم نواز ، تا بازی فوق العاده حامد بهداد.....
Solution: positive
Reason: This is a good example. The sentiment of the review is positive toward the movie.
Now, solve this instance: باز هم خیانت، باز هم انفعال کاراکتر، باز هم اتفاق پیدا شدن گوشی، باز هم نویسنده ای که با این ترفندها می خواهد فیلم را به پیش ببرد و تمام کند.به راستی اگر این انفعال و تصادف نبود فیلم را چه می شد.بودن یا نبودن؟
Student:
|
negative
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 2
|
train
|
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Q: داستان خوب ولی کاملا کسل کننده و بدون هیجان
A:
|
mixed
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
zs_opt
| 4
|
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 given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
در جشنواره متاسفانه نتونستم ببینم ولی دیشب در اکران فیلم های جشنواره فجر در پردیس چارسو موفق به دیدن فیلم شدم. چه فیلم خوبی از فضای بصری زیبا و چشم نواز ، تا بازی فوق العاده حامد بهداد.....
Solution: positive
Why? This is a good example. The sentiment of the review is positive toward the movie.
New input: "با دیگران" رو دوست نداشتم یه داستان کلیشه ای که هیچ خلاقیت و نوآوری ای توش استفاده نشده با بازی های متوسط "بایک حمیدیان" و "حمیدرضا آذرنگ" با ریتم خیلی کند. واقعاً نمی شه برای فیلم اول سراغ سوژه های بهتر رفت؟ چرا اصرار داریم هر سوژه ای رو تبدیل به فیلم بلند کنیم؟ manimoon..
Solution:
|
negative
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 0
|
train
|
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Q: بدترین فیلم کارنامه حاتمی کیا و یکی از بدترین فیلمهای جشنواره امسال از نظر منی که سینمای حاتمی کیا رو دوست دارم سکانسای کشدار و بی مورد دیالوگای ده نمکی طور و .... اخر فیلم که کلا انگار کار و ده نمکی ساخته
A:
|
negative
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
zs_opt
| 4
|
train
|
instruction:
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
question:
تابو به تلخی خرس نبود. اگر چه قهرمان های فیلم، سرنوشت تلخی داشتند اما شیرینی همراهی شان حتی در تلخی سرنوشت، نمی گذاشت که کام تماشاگر به طور کامل تلخ شود. به جز بازی بهار (الناز شاکر دوست) و پدرش، بقیه بازی ها هم خوب بودند.
answer:
mixed
question:
فیلم مخصوص آقایون بود ؟ ؟ ؟ :| والا همش توش تیکه بود برای زنان و ضعیف نشون دادن خانوما همشونم مصنوعی بودن :| فقط میتونم بگم باران کوثری خوب کار کرد -_-
answer:
negative
question:
فیلم خوبی بود. انتخاب سحر دولتشاهی هم خیلی بجا بود. خیلی واقعی بود و قابل درک.بنظرم رضا سردرگم بود و علی رغم ظاهر و ادبیات همیشه خونسردش مثل یه آدم طوفان زده بی تکلیف بود. تماشای فیلم را حتما توصیه میکنم.
answer:
|
positive
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 9
|
train
|
Part 1. Definition
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Part 2. Example
در جشنواره متاسفانه نتونستم ببینم ولی دیشب در اکران فیلم های جشنواره فجر در پردیس چارسو موفق به دیدن فیلم شدم. چه فیلم خوبی از فضای بصری زیبا و چشم نواز ، تا بازی فوق العاده حامد بهداد.....
Answer: positive
Explanation: This is a good example. The sentiment of the review is positive toward the movie.
Part 3. Exercise
چه زیبا بی اندازه ها را به اندازه آورده بود! کاش نه تنها اندازه آنها که اندازه خودمان را نیز بدانیم.
Answer:
|
positive
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 7
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
سلام به همه به نظر میاد هدف اصلی فیلم مطرح کردن دارا حیایی هستش با نیم نگاهی به یاد کردن سیستان همین و بس ...
Output:
|
negative
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
zs_opt
| 1
|
train
|
In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Example: در جشنواره متاسفانه نتونستم ببینم ولی دیشب در اکران فیلم های جشنواره فجر در پردیس چارسو موفق به دیدن فیلم شدم. چه فیلم خوبی از فضای بصری زیبا و چشم نواز ، تا بازی فوق العاده حامد بهداد.....
Example solution: positive
Example explanation: This is a good example. The sentiment of the review is positive toward the movie.
Problem: فیلم رو دوست داشتم قصه اش پر کشش بود و ریتم خوبی داشت با توجه به اینکه تنابنده دست به سیمرغ گرفتنش خوبه احساس میکنم برا فیلمنامه نامزد بشه و شاید جایزه هم بگیره بازی مهناز افشار به نظرم فاجعه بود
|
Solution: positive
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
fs_opt
| 5
|
test
|
Detailed Instructions: In this task, you will be given a movie review in Persian. You have to Classify the sentiment of the reviewer toward the movie into: "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it.
Problem:فیلم بسیار ژرف و زیبا، این گونه فیلم ها نشان می دهند که سینمای نجیب خیلی بهتر از سینمای سخیف عشق را نمایش می دهد.
Solution:
|
positive
|
task526_parsinlu_movie_overal_classification
|
NIv2
|
zs_opt
| 8
|
validation
|
Choose between "penguin" and "panda". Question: A penguin can run faster then a panda. If both animals run for 10 minutes, which will have gone the greater distance? (A) penguin (B) panda
penguin
Choose between "felt" and "carpet". Question: Jeri noticed that the pool cue ball moves faster on the felt of the table than on the carpeted game room floor. The cue ball moves faster on the felt surface because the felt has (A) less resistance (B) more resistance
felt
Q: Choose between "James" and "boxer". Question: James is at the arena watching his favorite boxer go head to head with his nemesis in the ring. Which of the two is least likely to need to wipe their face with a dry towel? (A) The boxer constantly on the move (B) James, sitting in a chair
A: boxer
Question: Choose between "parking lot" and "sand". Question: Emily was playing with her ball on the beach, and she noticed that it didn't roll too well over the sand. When she dropped it in the parking lot, though, it started rolling away rather quickly. Did the ball move further when dropped on (A) the sand or (B) the parking lot?
Answer: sand
*Question*
Choose between "Hiker" and "Office Worker". Question: A hiker that is spending their time outdoors will likely sweat _____ than someone working in an office. (A) more (B) less
**Answer**
Hiker
Choose between "snowy hill" and "grassy hill". Question: Tyler wants to play with his sled, but there is no snow on the ground, so he sleds down a grassy hill. His sled barely moves. That winter, Tyler takes his sled down a snowy hill, and his sled slides very quickly. The snowy hill was better for sledding because it had (A) more resistance (B) less resistance.
|
grassy hill
|
quarel_choose_between
|
P3
|
fs_opt
| 0
|
train
|
Question: Choose between "Mary" and "Jimbo". Question: Both Mary and Jimbo were out working in a field digging a hole. Mary was working very hard, while Jimbo was just standing around leaning on his shovel. Which person was producing more sweat? (A) Mary (B) Jimbo
Answer:
|
Mary
|
quarel_choose_between
|
P3
|
zs_noopt
| 4
|
train
|
Choose between "capybara" and "anteater". Question: A capybara isn't as fast of a runner as an anteater. If both animals make the same journey, which will get there sooner? (A) capybara (B) anteater
anteater
------
Choose between "Sun" and "Mars". Question: The sun is much more massive than Mars therefore it has (A) weaker gravity (B) stronger gravity
Mars
------
Choose between "bicycle" and "tricycle". Question: Josh was riding his bike down the driveway. His sister was riding her tricycle near him. Josh rode much faster. The _____ traveled a greater distance. (A) tricycle (B) bicycle
tricycle
------
Choose between "rubber soles" and "leather soles". Question: Mary slid across the wet boat deck wearing her leather-soled slippers, while Sue wearing rubber-soled deck shoes walked with no problem. Mary's shoes had (A) less resistance or (B) more resistance.
|
rubber soles
------
|
quarel_choose_between
|
P3
|
fs_opt
| 5
|
train
|
Ques:Choose between "noise on side road" and "noise next to construction". Question: Mark was driving through a construction zone where a loud drill was being used. He first had to drive right next to the construction site, then he turned onto a side road. Where would the noise of the drill be the loudest?(A)next to construction site(B)side road
Ans:noise on side road
-----
Ques:Choose between "asteroid" and "Moon". Question: A satellite is heading toward the moon to orbit and collect data. Near the moon there is an asteroid that is less than half the mass of the moon. The satellite is pulled past the asteroid and into the orbit of the moon due to the moon's much larger size. This means that the (A) moon (B) asteroid has a more powerful gravitational attraction.
Ans:asteroid
-----
Ques:Choose between "laundry basket" and "refrigerator". Question: Steve is seeing which household items have more mass. Steve drops a refrigerator and a laundry basket off of his roof at the same time, seeing which will hit the ground first. The item with more mass is the (A) refrigerator (B) laundry basket
Ans:laundry basket
-----
Ques:Choose between "parking lot" and "sidewalk". Question: A bag of trash blows a little ways across the parking lot. Then it reaches a sidewalk. The bag blows all the way away from the building. Which is less bumpy? (A) parking lot (B) sidewalk
Ans:
|
sidewalk
-----
|
quarel_choose_between
|
P3
|
fs_opt
| 9
|
train
|
Choose between "dirt street" and "paved street". Question: Dan is driving his van and it gets very warm when driving down the dirt street. However, the van stays cool driving down the paved street. The paved street has (A) greater resistance (B) lower resistance.
paved street
Choose between "Manny" and "Kevin". Question: Manny and Kevin want to travel cross country. Manny takes a plane and Kevin takes a car. Who crosses the country first? (A) Kevin (B) Manny
Kevin
Q: Choose between "grass lawn" and "sandbox". Question: Stacey is playing with her toy truck in the sandbox and can't get the truck to roll very easily. When she pushes the truck on the grass lawn, the truck moves very quickly. The grass lawn has (A) more friction (B) less friction.
A: sandbox
Question: Choose between "second little pig" and "first little pig". Question: The first little pig built his house of thin straw. The second little pig built his house of thick wood. Whose house would be stronger when the big bad wolf arrived? (A) first little pig (B) second little pig
Answer: first little pig
*Question*
Choose between "Milo" and "Jimbo". Question: Jimbo and Milo grew up together. Both men always knew that Jimbo wasn't as strong as Milo. If both men tossed a football, which could throw it a greater distance? (A) Jimbo (B) Milo
**Answer**
Jimbo
Q: Choose between "Fezzik" and "Inigo". Question: Fezzik the strong giant and Inigo the not as strong swordsman are having a contest. Each one throws a rock as far as he can. Who can throw farther? (A) Fezzik (B) Inigo
|
A: Fezzik
|
quarel_choose_between
|
P3
|
fs_opt
| 2
|
train
|
Choose between "muddy path" and "clear street". Question: Rico is on his skateboard. He practices racing, and on the muddy path, he can finish a race in ten minutes. On the clear street, Rico finishes the race in five minutes. Rico can finish the race faster on the clear street because the clear street has (A) more friction (B) less friction.
|
clear street
|
quarel_choose_between
|
P3
|
zs_noopt
| 0
|
train
|
Choose between "carpet" and "rug". Question: When Mike slides his cup across the carpet is heats up less then when he slides it across the rug. This means the _____ is more smooth (A) rug (B) carpet
|
rug
|
quarel_choose_between
|
P3
|
zs_opt
| 0
|
train
|
Choose between "Car" and "Bicycle". Question: A car is faster than the average bicycle so which would go further in an hour? (A) Bicycle (B) Car
Bicycle
Choose between "Jane 500 feet away from her house" and "Jane outside her house". Question: Jane goes out of her house and starts walking away from it. After walking 500 feet, the house will look (A) bigger (B) smaller.
Jane outside her house
Q: Choose between "John on his motor scooter" and "Connor on his tricycle". Question: Connor and John decide to see who can get from their house to the park fastest. Connor takes his tricycle and john takes his motor scooter. Who would get there first? (A) John (B) Connor
A: John on his motor scooter
Question: Choose between "wool sock" and "cotton shirt". Question: Abigail moved her iron over the surface of a cotton dress shirt. She decided to also iron some wool socks, so she moved the iron over the surface of a wool sock. The iron was more slow in moving across the sock than it was in moving across the shirt. It was easy for Abigail to see that the surface that was smoother was (A) the cotton shirt (B) the wool sock.
Answer: wool sock
*Question*
Choose between "Bryan's car" and "Jim's car". Question: Jim and Bryan have both left their office and are driving down the highway at the same speed. However, Bryan lives closer to the office than Jim does. Who travels the least distance? (A) Jim (B) Bryan
**Answer**
Jim's car
Q: Choose between "wood board with knots" and "wood board without knots". Question: Annabel rolls a pomegranate down two boards, one with knots and one without. When it rolls down the board without knots, it has less friction than when it rolls down the board with knots. Which board is rougher? (A) wood board with knots (B) wood board without knots
|
A: wood board with knots
|
quarel_choose_between
|
P3
|
fs_opt
| 2
|
train
|
Choose between "roller hockey" and "ice hockey". Question: Roy is trying to figure out which type of hockey field will allow him to make the longest hockey slapshot with his hockey puck. The roller hockey field is rough and made of asphalt while the ice hockey field is smooth and made of ice. Which field will let him make a longer slapshot? (A) roller hockey (B) ice hockey
A:
|
ice hockey
|
quarel_choose_between
|
P3
|
zs_opt
| 2
|
test
|
Ques: Choose between "towel" and "paper". Question: Jim rolled his pebble across a towel and a piece of paper. He then realized that the towel was quite a bit rougher in one direction than the paper was, and vice versa. Does the _____ generate more heat when the pebble is rolled across (A) towel (B) paper
Ans: towel
Ques: Choose between "orchard" and "cannery". Question: A grapefruit can roll a greater distance across an orchard compared to one rolling across a cannery. Which surface will make the grapefruit get hotter? (A) orchard (B) cannery
Ans: cannery
Ques: Choose between "The next day" and "Day 1". Question: On day 1, Jeremy does 10 sets of jumping jacks, runs 3 miles, and lifts weights for 20 minutes. The next day, he does 1 set of jumping jacks and walks for 10 minutes. On which day did he sweat less? (A) Day 1 (B) the next day
Ans: Day 1
Ques: Choose between "truck with the camper" and "truck without the camper". Question: John is camping with his family. He attaches the camper to his truck and heads out. At a stop light, another truck beside John revs the engine, challenging him. The truck with the most difficulty accelerating will be (A) The truck with the camper attached (B) The truck without the camper attached.
Ans:
|
truck with the camper
|
quarel_choose_between
|
P3
|
fs_opt
| 7
|
validation
|
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
One example: Passage: The French and Indian War (1754–1763) was the North American theater of the worldwide Seven Years' War. The war was fought between the colonies of British America and New France, with both sides supported by military units from their parent countries of Great Britain and France, as well as Native American allies. At the start of the war, the French North American colonies had a population of roughly 60,000 European settlers, compared with 2 million in the British North American colonies. The outnumbered French particularly depended on the Indians. Long in conflict, the metropole nations declared war on each other in 1756, escalating the war from a regional affair into an intercontinental conflict. Question: When was the French and Indian War?
Solution is here: 1754-1763
Explanation: It is a common convention to write (start year-end year) beside a historical event to understand when the event happened. We can observe a similar notation from the passage to conclude that the French and Indian War happened during 1754–1763. Therefore, the answer is 1754–1763.
Now, solve this: Passage: West's fourth studio album, 808s & Heartbreak (2008), marked an even more radical departure from his previous releases, largely abandoning rap and hip hop stylings in favor of a stark electropop sound composed of virtual synthesis, the Roland TR-808 drum machine, and explicitly auto-tuned vocal tracks. Drawing inspiration from artists such as Gary Numan, TJ Swan and Boy George, and maintaining a "minimal but functional" approach towards the album's studio production, West explored the electronic feel produced by Auto-Tune and utilized the sounds created by the 808, manipulating its pitch to produce a distorted, electronic sound; he then sought to juxtapose mechanical sounds with the traditional sounds of taiko drums and choir monks. The album's music features austere production and elements such as dense drums, lengthy strings, droning synthesizers, and somber piano, and drew comparisons to the work of 1980s post-punk and new wave groups, with West himself later confessing an affinity with British post-punk group Joy Division. Rolling Stone journalist Matthew Trammell asserted that the record was ahead of its time and wrote in a 2012 article, "Now that popular music has finally caught up to it, 808s & Heartbreak has revealed itself to be Kanye’s most vulnerable work, and perhaps his most brilliant." Question: What other artist besides Gary Numan and TJ Swan helped inspire Kanye's fourth album?
Solution:
|
Boy George
|
task075_squad1_1_answer_generation
|
NIv2
|
fs_opt
| 6
|
train
|
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Ex Input:
Passage: Virtually all nuclear power plants generate electricity by heating water to provide steam that drives a turbine connected to an electrical generator. Nuclear-powered ships and submarines either use a steam turbine directly for main propulsion, with generators providing auxiliary power, or else employ turbo-electric transmission, where the steam drives a turbo generator set with propulsion provided by electric motors. A limited number of steam turbine railroad locomotives were manufactured. Some non-condensing direct-drive locomotives did meet with some success for long haul freight operations in Sweden and for express passenger work in Britain, but were not repeated. Elsewhere, notably in the U.S.A., more advanced designs with electric transmission were built experimentally, but not reproduced. It was found that steam turbines were not ideally suited to the railroad environment and these locomotives failed to oust the classic reciprocating steam unit in the way that modern diesel and electric traction has done.[citation needed] Question: What does the steam generated by a nuclear power plant drive?
Ex Output:
turbine
Ex Input:
Passage: RIBA runs many awards including the Stirling Prize for the best new building of the year, the Royal Gold Medal (first awarded in 1848), which honours a distinguished body of work, and the Stephen Lawrence Prize for projects with a construction budget of less than £500,000. The RIBA also awards the President's Medals for student work, which are regarded as the most prestigious awards in architectural education, and the RIBA President's Awards for Research. The RIBA European Award was inaugurated in 2005 for work in the European Union, outside the UK. The RIBA National Award and the RIBA International Award were established in 2007. Since 1966, the RIBA also judges regional awards which are presented locally in the UK regions (East, East Midlands, London, North East, North West, Northern Ireland, Scotland, South/South East, South West/Wessex, Wales, West Midlands and Yorkshire). Question: What was the first year in which RIBA's Royal Gold Medal was given?
Ex Output:
1848
Ex Input:
Passage: Bell believed the photophone's principles were his life's "greatest achievement", telling a reporter shortly before his death that the photophone was "the greatest invention [I have] ever made, greater than the telephone". The photophone was a precursor to the fiber-optic communication systems which achieved popular worldwide usage in the 1980s. Its master patent was issued in December 1880, many decades before the photophone's principles came into popular use. Question: What did Bell call the best thing he ever made?
Ex Output:
|
The photophone
|
task075_squad1_1_answer_generation
|
NIv2
|
fs_opt
| 1
|
train
|
TASK DEFINITION: This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
PROBLEM: Passage: In 1853 the Russian Empire on behalf of the Slavic Balkan states began to question the very existence of the Ottoman Empire. The result was the Crimean War, 1853–1856, in which the British Empire and the French Empire supported the Ottoman Empire in its struggle against the incursions of the Russian Empire. Eventually, the Ottoman Empire lost control of the Balkan region. Question: What was the Ottoman Empire's struggle against in the Crimean War?
SOLUTION: the incursions of the Russian Empire
PROBLEM: Passage: The "soft" AC format has reinvented in the late 2000s/early 2010s as a result of its declining relevance, adopting a more upmarket, middle-of-the-road approach, with a selection of "oldies" (usually from the 1960s/70s onwards), primarily rock, jazz, R&B and pop music. Newer songs are more often (but not limited to) "easy listening" fare, this amount varying depending on the age of the station's target demographic. Question: What genre of music do new songs on soft AC stations often belong to?
SOLUTION: "easy listening"
PROBLEM: Passage: The Medill School of Journalism has produced notable journalists and political activists including 38 Pulitzer Prize laureates. National correspondents, reporters and columnists such as The New York Times's Elisabeth Bumiller, David Barstow, Dean Murphy, and Vincent Laforet, USA Today's Gary Levin, Susan Page and Christine Brennan, NBC correspondent Kelly O'Donnell, CBS correspondent Richard Threlkeld, CNN correspondent Nicole Lapin and former CNN and current Al Jazeera America anchor Joie Chen, and ESPN personalities Rachel Nichols, Michael Wilbon, Mike Greenberg, Steve Weissman, J. A. Adande, and Kevin Blackistone. The bestselling author of the A Song of Ice and Fire series, George R. R. Martin, earned a B.S. and M.S. from Medill. Elisabeth Leamy is the recipient of 13 Emmy awards and 4 Edward R. Murrow Awards. Question: Where did NBC correspondant Kelly O'Donnell attend school?
SOLUTION:
|
The Medill School of Journalism
|
task075_squad1_1_answer_generation
|
NIv2
|
fs_opt
| 8
|
train
|
You will be given a definition of a task first, then some input of the task.
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Passage: The Lancashire economy relies strongly on the M6 motorway which runs from north to south, past Lancaster and Preston. The M55 connects Preston to Blackpool and is 11.5 miles (18.3 km) long. The M65 motorway from Colne, connects Burnley, Accrington, Blackburn to Preston. The M61 from Preston via Chorley and the M66 starting 500 metres (0.3 mi) inside the county boundary near Edenfield, provide links between Lancashire and Manchester] and the trans-Pennine M62. The M58 crosses the southernmost part of the county from the M6 near Wigan to Liverpool via Skelmersdale. Question: What does the Lancashire economy rely on?
Output:
|
the M6 motorway
|
task075_squad1_1_answer_generation
|
NIv2
|
zs_opt
| 1
|
train
|
Q: This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Passage: On matchdays, in a tradition going back to 1962, players walk out to the theme tune to Z-Cars, named "Johnny Todd", a traditional Liverpool children's song collected in 1890 by Frank Kidson which tells the story of a sailor betrayed by his lover while away at sea, although on two separate occasions in the 1994, they ran out to different songs. In August 1994, the club played 2 Unlimited's song "Get Ready For This", and a month later, a reworking of the Creedence Clearwater Revival classic "Bad Moon Rising". Both were met with complete disapproval by Everton fans. Question: What year did the Everton players walk out to a song other than "Johnny Todd"?
A:
|
1994
|
task075_squad1_1_answer_generation
|
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.
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Passage: The French and Indian War (1754–1763) was the North American theater of the worldwide Seven Years' War. The war was fought between the colonies of British America and New France, with both sides supported by military units from their parent countries of Great Britain and France, as well as Native American allies. At the start of the war, the French North American colonies had a population of roughly 60,000 European settlers, compared with 2 million in the British North American colonies. The outnumbered French particularly depended on the Indians. Long in conflict, the metropole nations declared war on each other in 1756, escalating the war from a regional affair into an intercontinental conflict. Question: When was the French and Indian War?
Solution: 1754-1763
Why? It is a common convention to write (start year-end year) beside a historical event to understand when the event happened. We can observe a similar notation from the passage to conclude that the French and Indian War happened during 1754–1763. Therefore, the answer is 1754–1763.
New input: Passage: Lafayette Park is a revitalized neighborhood on the city's east side, part of the Ludwig Mies van der Rohe residential district. The 78-acre (32 ha) development was originally called the Gratiot Park. Planned by Mies van der Rohe, Ludwig Hilberseimer and Alfred Caldwell it includes a landscaped, 19-acre (7.7 ha) park with no through traffic, in which these and other low-rise apartment buildings are situated. Immigrants have contributed to the city's neighborhood revitalization, especially in southwest Detroit. Southwest Detroit has experienced a thriving economy in recent years, as evidenced by new housing, increased business openings and the recently opened Mexicantown International Welcome Center. Question: What district is Lafayette Park a part of?
Solution:
|
Ludwig Mies van der Rohe residential district
|
task075_squad1_1_answer_generation
|
NIv2
|
fs_opt
| 0
|
train
|
Teacher:This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Teacher: Now, understand the problem? Solve this instance: Passage: Myanmar's first film was a documentary of the funeral of Tun Shein — a leading politician of the 1910s, who campaigned for Burmese independence in London. The first Burmese silent film Myitta Ne Thuya (Love and Liquor) in 1920 which proved a major success, despite its poor quality due to a fixed camera position and inadequate film accessories. During the 1920s and 1930s, many Burmese-owned film companies made and produced several films. The first Burmese sound film was produced in 1932 in Bombay, India with the title Ngwe Pay Lo Ma Ya (Money Can't Buy It). After World War II, Burmese cinema continued to address political themes. Many of the films produced in the early Cold War era had a strong propaganda element to them. Question: Why was this film relevant enough to be the first ?
Student:
|
Tun Shein — a leading politician of the 1910s, who campaigned for Burmese independence in London.
|
task075_squad1_1_answer_generation
|
NIv2
|
zs_opt
| 6
|
train
|
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Passage: The most famous reformer of Estonian, Johannes Aavik (1880–1973), used creations ex nihilo (cf. ‘free constructions’, Tauli 1977), along with other sources of lexical enrichment such as derivations, compositions and loanwords (often from Finnish; cf. Saareste and Raun 1965: 76). In Aavik’s dictionary (1921), which lists approximately 4000 words, there are many words which were (allegedly) created ex nihilo, many of which are in common use today. Examples are Question: Of all of Estonian's language reformers who is the most well known?
|
Johannes Aavik
|
task075_squad1_1_answer_generation
|
NIv2
|
zs_opt
| 0
|
train
|
Q: This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
Passage: In 2011, documents obtained by WikiLeaks revealed that Beyoncé was one of many entertainers who performed for the family of Libyan ruler Muammar Gaddafi. Rolling Stone reported that the music industry was urging them to return the money they earned for the concerts; a spokesperson for Beyoncé later confirmed to The Huffington Post that she donated the money to the Clinton Bush Haiti Fund. Later that year she became the first solo female artist to headline the main Pyramid stage at the 2011 Glastonbury Festival in over twenty years, and was named the highest-paid performer in the world per minute. Question: Beyonce was listed in 2011 as the highest paid performer per what?
A:
|
minute
|
task075_squad1_1_answer_generation
|
NIv2
|
zs_opt
| 7
|
test
|
This task is about writing a correct answer for the reading comprehension task. Based on the information provided in a given passage, you should identify the shortest continuous text span from the passage that serves as an answer to the given question. Avoid answers that are incorrect or provides incomplete justification for the question.
--------
Question: Passage: Israel, and The US Air Force, in conjunction with the members of NATO, has developed significant tactics for air defence suppression. Dedicated weapons such as anti-radiation missiles and advanced electronics intelligence and electronic countermeasures platforms seek to suppress or negate the effectiveness of an opposing air-defence system. It is an arms race; as better jamming, countermeasures and anti-radiation weapons are developed, so are better SAM systems with ECCM capabilities and the ability to shoot down anti-radiation missiles and other munitions aimed at them or the targets they are defending. Question: As better weapons are created, what systems continue to improve as well to counter them?
Answer: SAM systems with ECCM capabilities
Question: Passage: The following four timelines show the geologic time scale. The first shows the entire time from the formation of the Earth to the present, but this compresses the most recent eon. Therefore, the second scale shows the most recent eon with an expanded scale. The second scale compresses the most recent era, so the most recent era is expanded in the third scale. Since the Quaternary is a very short period with short epochs, it is further expanded in the fourth scale. The second, third, and fourth timelines are therefore each subsections of their preceding timeline as indicated by asterisks. The Holocene (the latest epoch) is too small to be shown clearly on the third timeline on the right, another reason for expanding the fourth scale. The Pleistocene (P) epoch. Q stands for the Quaternary period. Question: What is the name of the latest epoch?
Answer: Holocene
Question: Passage: Capacitors may catastrophically fail when subjected to voltages or currents beyond their rating, or as they reach their normal end of life. Dielectric or metal interconnection failures may create arcing that vaporizes the dielectric fluid, resulting in case bulging, rupture, or even an explosion. Capacitors used in RF or sustained high-current applications can overheat, especially in the center of the capacitor rolls. Capacitors used within high-energy capacitor banks can violently explode when a short in one capacitor causes sudden dumping of energy stored in the rest of the bank into the failing unit. High voltage vacuum capacitors can generate soft X-rays even during normal operation. Proper containment, fusing, and preventive maintenance can help to minimize these hazards. Question: What can happen to capacitors used in high current applications?
Answer:
|
overheat
|
task075_squad1_1_answer_generation
|
NIv2
|
fs_opt
| 7
|
validation
|
Detailed Instructions: Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
See one example below:
Problem: Italia berhasil mengalahkan Portugal 31-5 di grup C dalam Piala Dunia Rugby 2007 di Parc des Princes, Paris, Perancis.
Solution: フランスのパリ、パルク・デ・プランスで行われた2007年ラグビーワールドカップのプールCで、イタリアは31対5でポルトガルを下した。
Explanation: The Bahasa Indonesia sentence is correctly converted into Japanese because the converted sentence holds the message that Italy defeated Portugal 31–5 in Pool C of the 2007 Rugby World Cup at the Parc des Princes in Paris, France. Also, translated sentence preserves the numbers as it is.
Problem: "Kami percaya potensi wabah menyebar tidak akan mengganggu Olimpiade Beijing, " juru bicara Kementerian Kesehatan Cina Mao Qunan menyatakan.
Solution:
|
中華人民共和国衛生部のスポークスマンMaoQunanは「病気の発生の可能性が北京オリンピックに影響しないと確信している」と語った。
|
task1116_alt_id_ja_translation
|
NIv2
|
fs_opt
| 4
|
train
|
Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
--------
Question: Kami sekarang harus melakukan apapun yang mungkin untuk menghentikan pemogokan ini dari persidangan, katanya.
Answer: 今やこのストライキが進行するのを止めることが可能なあらゆることをしなければならない、と彼は言った。
Question: Perlahan-lahan kita melihat olahraga wanita lebih sering tampil dalam publikasi media besar.
Answer: 徐々に、私たちは主流のメディア出版物でより頻繁に女子スポーツの特集を見るようになっています。
Question: Menurut saksi mata, beberapa anggota Taliban dan para pejabat dari AS dan Afghanistan, helikopter Chinook sengaja dijadikan target, tampaknya dengan roket granat (RPG).
Answer:
|
目撃者やタリバンメンバーと米軍とアフガニスタン軍のメンバーによると、チヌークヘリは意図的に標的にされ、ロケット式手投げ弾(RPG)でねらわれたことは明らかだ。
|
task1116_alt_id_ja_translation
|
NIv2
|
fs_opt
| 7
|
train
|
Detailed Instructions: Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Q: Pada akhirnya bisa digunakan untuk misi berawak menggunakan pesawat ruang angkasa SpaceX Dragon.
A:
|
最終的には、SpaceXの宇宙船Dragonを使った有人ミッションにも使われるかもしれない。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 9
|
train
|
Given the task definition and input, reply with output. Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Komisi Hak Asasi Manusia Ketua Pakistan Iqbal Haider mengatakan puteri Dr. Aafia juga di Afghanistan.
|
パキスタン人権委員会の共同議長、イクバール・ハイダーは、アーフィア博士の娘もアフガニスタンにいると語った。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 5
|
train
|
Detailed Instructions: Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Q: Petugas Satwaliar di Zimbabwe mengatakan hari ini, bahwa jumlah perburuan binatang di negara ini telah meningkat pesat tahun ini, dan menyalahkan sindikat kejahatan internasional.
A:
|
ジンバブエの野生動物保護の役員が今日、ジンバブエでの動物の密猟数が今年顕著に増加しており、これは国際的な犯罪組織によるものだと語った。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 9
|
train
|
Detailed Instructions: Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Q: Perkembangan ini telah bertahan hampir tiga minggu, dan mempengaruhi perairan sejajar dengan Pulau Padre Selatan sekitar 25 mil, dan bisa meluas sejauh 10 mil sepanjang pantai Mexico.
A:
|
この発生は、ほぼ３週間も長引いており、南パドレ島と並行に沿って約25マイルある流れに影響を及ぼし、メキシコビーチに沿って10マイルのところまで広がるかもしれません。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 9
|
train
|
Definition: Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Input: Maskapai ini menyatakan bahwa semua pesawat telah mengalami inspeksi keselamatan yang luas dan mereka puas mereka aman.
Output:
|
航空会社は、全ての航空機が広範囲な安全点検を受け、それらが安全基準を満たしていると、述べた。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 2
|
train
|
Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
One example: Italia berhasil mengalahkan Portugal 31-5 di grup C dalam Piala Dunia Rugby 2007 di Parc des Princes, Paris, Perancis.
Solution is here: フランスのパリ、パルク・デ・プランスで行われた2007年ラグビーワールドカップのプールCで、イタリアは31対5でポルトガルを下した。
Explanation: The Bahasa Indonesia sentence is correctly converted into Japanese because the converted sentence holds the message that Italy defeated Portugal 31–5 in Pool C of the 2007 Rugby World Cup at the Parc des Princes in Paris, France. Also, translated sentence preserves the numbers as it is.
Now, solve this: Mendekati kecepatan cahaya, bagaimanapun, memerlukan waktu yang panjang dari percepatan konstan.
Solution:
|
しかし、光の速度に近づくことは長期間の恒常的な加速を必要とする。
|
task1116_alt_id_ja_translation
|
NIv2
|
fs_opt
| 6
|
train
|
Given the task definition and input, reply with output. Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Selama ia di sana, Ia bergulat sebagai El Gigante selama dua tahun, lalu bergabung dengan Federasi Gulat Dunia (WWF), dan saat ini Entertainment Pegulat Dunia (WWE).
|
そこにいる間、彼は2年間はエル・ヒガンテとしてレスリングをし、その後、ワールド・レスリング・フェデレーション(WWF)、現在のワールド・レスリング・エンターテイメント(WWE)に加わった。
|
task1116_alt_id_ja_translation
|
NIv2
|
zs_opt
| 5
|
test
|
Given a sentence in the Indonesian(Bahasa variant), provide an equivalent translation in Japanese that retains the same meaning through the translation. In translation, keep numbers as it is.
Q: Efek tambahannya adalah untuk mengurangi gengsi internasional dari kepimpinan Presiden Hugo Chavez menciptakan keraguan tentang keterlibatan Venezuela yang dikatakan sebagai penculikan.
A: さらなる効果は、上述の誘拐にベネズエラ人が関与している可能性を疑わしいものにしようというフーゴ・チャベス大統領の行動への国際的な名声を減らそうということだ。
****
Q: "Kita semua memiliki hati yang terlalu berat."
A: 「我々は皆、とてもつらい思いだ」
****
Q: Pejabat tersebut mengatakan, "Kami berharap pemerintah Irak untuk segera mengambil tindakan untuk mengatur kebebasan individual yang tersebut di atas dan mengutuk tentara AS atas tindakannya."
A:
|
その高官は「我々はイラク政府に対し、前記の人物らを解放するための迅速な手段を取り、その措置について米国兵を批難することを期待する」と言った。
****
|
task1116_alt_id_ja_translation
|
NIv2
|
fs_opt
| 4
|
validation
|
Detailed Instructions: Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Q: L'ancien lycée est devenu le lycée, tandis que le nouveau bâtiment est devenu le lycée.
A:
|
The old grammar school became the Upper School while the new building became the Lower School .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 9
|
train
|
Definition: Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Input: WORHP, également appelé eNLP (European NLP Solver), est une bibliothèque de logiciels mathématiques permettant de résoudre numériquement des problèmes d’optimisation continus et non linéaires à grande échelle.
Output:
|
WORHP , also referred to as eNLP ( European NLP solver ) by ESA , is a mathematical software library for solving continuous large scale nonlinear optimization problems numerically .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 2
|
train
|
Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
La rivière Bota Mare est un affluent de la rivière Zăbrătău en Roumanie.
|
The Bota Mare River is a tributary of the Zăbrătău River in Romania .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 0
|
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.
Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
Solution: The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .
Why? This is a correct and accurate translation from French to English because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.
New input: Au nord de South Huntington, le NY 110 entre dans une intersection à niveau avec le NY 25 (West Jericho Turnpike) dans le hameau de Walt Whitman Shops.
Solution:
|
North of South Huntington , NY 110 enters an at-grade crossing with NY 25 ( West Jericho Turnpike ) in the hamlet of Walt Whitman Shops .
|
task789_pawsx_french_english_translation
|
NIv2
|
fs_opt
| 0
|
train
|
Given the task definition and input, reply with output. Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Vous trouverez ci-dessous la première version de l’album avec toutes les séquences originales et «The Sacrifice of Victor» est légèrement plus long dans la configuration initiale.
|
Below is the early version of the album with all the original segues . Also , " The Sacrifice of Victor " is slightly longer on the early configuration .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 5
|
train
|
Detailed Instructions: Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Problem:La rivière Sambata est un affluent de la rivière Caprei en Roumanie.
Solution:
|
The Sâmbăta river is a tributary of the River Piatra Caprei in Romania .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 8
|
train
|
instruction:
Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
question:
Shaffer Creek est un affluent de la rivière Juniata Branch (branche Brush Creek) de Raystown, dans le comté de Bedford, en Pennsylvanie, aux États-Unis.
answer:
Shaffer Creek is an tributary of Brush Creek ( Raystown Branch Juniata River ) in Bedford County , Pennsylvania in the United States .
question:
Neptunea alexeyevi est une espèce d'escargot de mer, un véritable mollusque gastéropode de la famille des Buccinidae, la marine des bulots.
answer:
Neptunea alexeyevi is a species of sea snail , a true gastropod mollusk in the family Buccinidae , the marine whelks .
question:
«Pogonodon» a été décrit par Edward Drinker Cope en 1880. On connaît deux espèces: «P. davisi» et «P. platycopis».
answer:
|
" Pogonodon " was described in 1880 by Edward Drinker Cope . Two species are recognized , " P. davisi " and " P. platycopis " .
|
task789_pawsx_french_english_translation
|
NIv2
|
fs_opt
| 9
|
train
|
Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
One example: La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
Solution is here: The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .
Explanation: This is a correct and accurate translation from French to English because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.
Now, solve this: En tant que réalisateur audio, Belson a programmé des images cinétiques en direct et Jacobs a programmé de la musique électronique et des expériences visuelles.
Solution:
|
Belson as audio director programmed kinetic live visuals , and Jacobs programmed electronic music and visual experiments .
|
task789_pawsx_french_english_translation
|
NIv2
|
fs_opt
| 6
|
train
|
Detailed Instructions: Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
See one example below:
Problem: La saison NBA 1975 - 76 était la 30e saison de la National Basketball Association.
Solution: The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .
Explanation: This is a correct and accurate translation from French to English because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.
Problem: Dingwall et Tingwall en Angleterre, Thingwall en Norvège, Tynwald sur l'île de Man et Tingvoll en Écosse portent les noms de la même racine et de la même signification.
Solution:
|
Dingwall and Tingwall in England , Thingwall in Norway , Tynwald on the Isle of Man , and Tingvoll in Scotland bear names of the same root and meaning .
|
task789_pawsx_french_english_translation
|
NIv2
|
fs_opt
| 4
|
test
|
Definition: Given a sentence in French, provide an equivalent paraphrased translation in English that retains the same meaning both through the translation and the paraphrase.
Input: Les collèges de Sussex et de Lancing sont réputés avoir joué un match de football en novembre 1860, le premier par les écoles publiques de Brighton.
Output:
|
Sussex and Lancing Colleges , which played a football match in November 1860 , are recorded as the first of public schools in Brighton .
|
task789_pawsx_french_english_translation
|
NIv2
|
zs_opt
| 2
|
validation
|
In this task, you need to translate the given English sentence to Polish language.
Let me give you an example: The Americans didnt even know
The answer to this example can be: Amerykanie nawet nie wiedzieli
Here is why: English sentence is properly converted into Polish sentence
OK. solve this:
This is going to become 0
Answer:
|
Ten wynik równa się 0.
|
task830_poleval2019_mt_translation
|
NIv2
|
fs_opt
| 8
|
train
|
Q: In this task, you need to translate the given English sentence to Polish language.
So the two eigenspaces for the matrix-- where did I write it?
A:
|
Czyli dwie przestrzenie własne dla macierzy -- gdzie to napisałem?
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 7
|
train
|
Instructions: In this task, you need to translate the given English sentence to Polish language.
Input: "Those who are about to communicate
Output:
|
Ci, którzy chcą komunikować pilne wiadomości
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 3
|
train
|
Detailed Instructions: In this task, you need to translate the given English sentence to Polish language.
Q: Minus 2 squared is 4.
A:
|
(-2) kwadrat = 4
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 9
|
train
|
Definition: In this task, you need to translate the given English sentence to Polish language.
Input: and now this is outflowing
Output:
|
a to odpływy,
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 2
|
train
|
In this task, you need to translate the given English sentence to Polish language.
Let me give you an example: The Americans didnt even know
The answer to this example can be: Amerykanie nawet nie wiedzieli
Here is why: English sentence is properly converted into Polish sentence
OK. solve this:
1808-- and now we're talking about early 1808, in
Answer:
|
1808 - i teraz mówimy o początku 1808,
|
task830_poleval2019_mt_translation
|
NIv2
|
fs_opt
| 8
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you need to translate the given English sentence to Polish language.
Ace is card rank number 14,
Output:
|
Numer rangi, to w przypadku asa "14",
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 1
|
train
|
In this task, you need to translate the given English sentence to Polish language.
Ex Input:
here you can name the document,
Ex Output:
Tu wpisujemy nazwę dokumentu,
Ex Input:
it's actually in October.
Ex Output:
faktycznie w październiku.
Ex Input:
I think we're almost done.
Ex Output:
|
Juz sie zblizamy do konca.
|
task830_poleval2019_mt_translation
|
NIv2
|
fs_opt
| 1
|
train
|
Definition: In this task, you need to translate the given English sentence to Polish language.
Input: So after metaphase, now we're ready to pull the stuff apart,
Output:
|
Tak więc po metafazie, jesteśmy gotowi oderwać te rzeczy od siebie
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 2
|
test
|
Definition: In this task, you need to translate the given English sentence to Polish language.
Input: And, so, essentially it's going to be this length right over here,
Output:
|
Więc istotnie będzie to ta długość,
|
task830_poleval2019_mt_translation
|
NIv2
|
zs_opt
| 2
|
validation
|
In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
--------
Question: Two people wearing helmets on a yellow motorbike in front of a brown gate, beside a wooded area with trees.
Answer: English
Question: A long white passenger train traveling through lush green countryside.
Answer: English
Question: બાળકોને જન્મદિવસની પાર્ટીના ખોરાક ખાવા માટે બહાર પિકનીક ટેબલ પર બેઠા છે.
Answer:
|
Gujarati
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
fs_opt
| 7
|
train
|
Detailed Instructions: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
See one example below:
Problem: A herd of sheep standing together grazing in a pasture.
Solution: English
Explanation: Input sentence is in English language.
Problem: The large bathroom in the panaramic view has two sinks, a bathtub, and a shower stall.
Solution:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
fs_opt
| 4
|
train
|
Detailed Instructions: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
See one example below:
Problem: A herd of sheep standing together grazing in a pasture.
Solution: English
Explanation: Input sentence is in English language.
Problem: "કોઈ પાર્કિંગ" અને "ના સ્કેટબોર્ડ્સ" નો સંકેત આપતા ચિહ્નો સાથે શહેરની શેરી.
Solution:
|
Gujarati
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
fs_opt
| 4
|
train
|
Instructions: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
Input: A bathroom with a white tub and white cabinets has a black pattern on the floor.
Output:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
zs_opt
| 3
|
train
|
In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
Input: Consider Input: A Mac computer monitor on a desk which is turned off, and an ipad underneath.
Output: English
Input: Consider Input: A fire hydrant and a little yellow ball person is between three yellow poles.
Output: English
Input: Consider Input: Two people in a grassy field with a colorful owl floating in the air near them.
|
Output: English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
fs_opt
| 2
|
train
|
Instructions: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
Input: they young man is sitting in his car with his pet dog and studying from a textbook.
Output:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
zs_opt
| 3
|
train
|
Definition: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
Input: Canopies of tiny white lights strung between buildings over a busy street.
Output:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
zs_opt
| 2
|
train
|
Q: In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
બેઠક પર માઉન્ટ થયેલ પ્રવાહી વહેવાનો હરકોઈ જાતનો નળ એક ટોટી સાથે મલ્ટીરંગ્ડ ટાઇલ બાથરૂમમાં બેસીને.
A:
|
Gujarati
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
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 are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
A herd of sheep standing together grazing in a pasture.
Solution: English
Why? Input sentence is in English language.
New input: A conference table full of people working on computers, with stacks of paper covering the table.
Solution:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
fs_opt
| 0
|
test
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given a sentence which is either in the Gujarati language or English language. You task is to identify the language of input sentence. Input sentence can be in Gujarari or English language only and also it cannot have two languages at a time.
Birds are sitting on a railing along the ocean and there is a ship in the background.
Output:
|
English
|
task441_eng_guj_parallel_corpus_gu_en_language_identification
|
NIv2
|
zs_opt
| 1
|
validation
|
Teacher: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties.
Question: How did they obtain the dataset?
Solution: Extractive
Reason: The answer to this question has been explicitly mentioned in the context, so the question is extractive.
Now, solve this instance: Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates.
Question: Do they use any knowledge base to expand abbreviations?
Student:
|
Yes-no
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 2
|
train
|
instruction:
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
question:
The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest...
On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory!
Question: Do they break down word meanings into elementary particles as in the standard model of quantum theory?
answer:
Yes-no
question:
Our data has been developed by crawling and pre-processing an OSG web forum. The forum has a great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually transmitted diseases, etc.
Question: How did they obtain the OSG dataset?
answer:
Extractive
question:
We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information;
Question: What are the baselines?
answer:
|
Extractive
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 9
|
train
|
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Q: We opted for an automatic approach instead, that can be scaled arbitrarily and at little cost: we generate synthetic sentence pairs $(, \tilde{})$ by randomly perturbing 1.8 million segments $$ from Wikipedia. We use three techniques: mask-filling with BERT, backtranslation, and randomly dropping out words.
Question: How are the synthetic examples generated?
A: Abstractive
****
Q: Across all languages, 145 human annotators were asked to score all 1,888 pairs (in their given language). We finally collect at least ten valid annotations for each word pair in each language. All annotators were required to abide by the following instructions:
1. Each annotator must assign an integer score between 0 and 6 (inclusive) indicating how semantically similar the two words in a given pair are. A score of 6 indicates very high similarity (i.e., perfect synonymy), while zero indicates no similarity.
2. Each annotator must score the entire set of 1,888 pairs in the dataset. The pairs must not be shared between different annotators.
3. Annotators are able to break the workload over a period of approximately 2-3 weeks, and are able to use external sources (e.g. dictionaries, thesauri, WordNet) if required.
4. Annotators are kept anonymous, and are not able to communicate with each other during the annotation process.
Question: How were the datasets annotated?
A: Extractive
****
Q: If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model.
Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?
A:
|
Extractive
****
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 4
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Example: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties.
Question: How did they obtain the dataset?
Output: Extractive
The answer to this question has been explicitly mentioned in the context, so the question is extractive.
New input case for you: The input of our model are the words in the input text $x[1], ... , x[n]$ and query $q[1], ... , q[n]$ . We concatenate pre-trained word embeddings from GloVe BIBREF40 and character embeddings trained by CharCNN BIBREF41 to represent input words. The $2d$ -dimension embedding vectors of input text $x_1, ... , x_n$ and query $q_1, ... , q_n$ are then fed into a Highway Layer BIBREF42 to improve the capability of word embeddings and character embeddings as
$$\begin{split} g_t &= {\rm sigmoid}(W_gx_t+b_g) \\ s_t &= {\rm relu } (W_xx_t+b_x) \\ u_t &= g_t \odot s_t + (1 - g_t) \odot x_t~. \end{split}$$ (Eq. 18) The same Highway Layer is applied to $q_t$ and produces $v_t$ . Next, $u_t$ and $v_t$ are fed into a Bi-Directional Long Short-Term Memory Network (BiLSTM) BIBREF44 respectively in order to model the temporal interactions between sequence words: Then we feed $\mathbf {U}$ and $\mathbf {V}$ into the attention flow layer BIBREF27 to model the interactions between the input text and query. Therefore, we introduce Self-Matching Layer BIBREF29 in our model as
$$\begin{split} o_t &= {\rm BiLSTM}(o_{t-1}, [h_t, c_t]) \\ s_j^t &= w^T {\rm tanh}(W_hh_j+\tilde{W_h}h_t)\\ \alpha _i^t &= {\rm exp}(s_i^t)/\Sigma _{j=1}^n{\rm exp}(s_j^t)\\ c_t &= \Sigma _{i=1}^n\alpha _i^th_i ~. \end{split}$$ (Eq. 20) Finally we feed the embeddings $\mathbf {O} = [o_1, ... , o_n]$ into a Pointer Network BIBREF39 to decode the answer sequence as
$$\begin{split} p_t &= {\rm LSTM}(p_{t-1}, c_t) \\ s_j^t &= w^T {\rm tanh}(W_oo_j+W_pp_{t-1})\\ \beta _i^t &= {\rm exp}(s_i^t)/\Sigma _{j=1}^n{\rm exp}(s_j^t)\\ c_t &= \Sigma _{i=1}^n\beta _i^to_i~. \end{split}$$ (Eq. 21) Therefore, the probability of generating the answer sequence $\textbf {a}$ is as follows
$${\rm P}(\textbf {a}|\mathbf {O}) = \prod _t {\rm P}(a^t | a^1, ... , a^{t-1}, \mathbf {O})~.$$ (Eq. 23)
Question: What QA models were used?
Output:
|
Abstractive
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 1
|
train
|
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Input: Consider Input: (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long). Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration;
Question: What alternative to Gibbs sampling is used?
Output: Extractive
Input: Consider Input: Our system, including software and corpus, is available as an open source project for free research purpose and we believe that it is a good baseline for the development and comparison of future Vietnamese SRL systems.
Question: Are their corpus and software public?
Output: Yes-no
Input: Consider Input: o sufficiently utilize the large dataset $\mathcal {A}$ and $\mathcal {M}$, the model is pre-trained on CTC-based ASR task and MT task in the pre-training stage.
Question: What is the attention module pretrained on?
|
Output: Extractive
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 2
|
train
|
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Example Input: The baseline classifier uses a linear Support Vector Machine BIBREF7 , which is suited for a high number of features.
Question: What baseline is used?
Example Output: Abstractive
Example Input: Python package twitterscraper is used to scrap tweets from twitter. 10,478 tweets from the past two years from domains like `sports', `politics', `entertainment' were extracted.
Question: Where did the texts in the corpus come from?
Example Output: Extractive
Example Input: Especially for Davidson-dataset, some tweets with specific language (written within the African American Vernacular English) and geographic restriction (United States of America) are oversampled such as tweets containing disparage words “nigga", “faggot", “coon", or “queer", result in high rates of misclassification. However, these misclassifications do not confirm the low performance of our classifier because annotators tended to annotate many samples containing disrespectful words as hate or offensive without any presumption about the social context of tweeters such as the speaker’s identity or dialect, whereas they were just offensive or even neither tweets.
Question: What biases does their model capture?
Example Output:
|
Abstractive
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 3
|
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 context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
We evaluate a multi-task approach and three algorithms that explicitly model the task dependencies.
Question: Will these findings be robust through different datasets and different question answering algorithms?
Output:
|
Yes-no
|
task462_qasper_classification
|
NIv2
|
zs_opt
| 1
|
train
|
Teacher:In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Teacher: Now, understand the problem? Solve this instance: we follow Lapata2005Automatic to measure coherence as sentence similarity
Question: Did the authors evaluate their system output for coherence?
Student:
|
Yes-no
|
task462_qasper_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Q: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Experimental Protocol ::: Datasets ::: Training Dataset
(i) TTS System dataset: We trained our TTS system with a mixture of neutral and newscaster style speech. For a total of 24 hours of training data, split in 20 hours of neutral (22000 utterances) and 4 hours of newscaster styled speech (3000 utterances).
(ii) Embedding selection dataset: As the evaluation was carried out only on the newscaster speaking style, we restrict our linguistic search space to the utterances associated to the newscaster style: 3000 sentences.
Experimental Protocol ::: Datasets ::: Evaluation Dataset
The systems were evaluated on two datasets:
(i) Common Prosody Errors (CPE): The dataset on which the baseline Prostron model fails to generate appropriate prosody. This dataset consists of complex utterances like compound nouns (22%), “or" questions (9%), “wh" questions (18%). This set is further enhanced by sourcing complex utterances (51%) from BIBREF24.
(ii) LFR: As demonstrated in BIBREF25, evaluating sentences in isolation does not suffice if we want to evaluate the quality of long-form speech. Thus, for evaluations on LFR we curated a dataset of news samples. The news style sentences were concatenated into full news stories, to capture the overall experience of our intended use case.
Question: What dataset is used for train/test of this method?
A:
|
Abstractive
|
task462_qasper_classification
|
NIv2
|
zs_opt
| 7
|
test
|
Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
See one example below:
Problem: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties.
Question: How did they obtain the dataset?
Solution: Extractive
Explanation: The answer to this question has been explicitly mentioned in the context, so the question is extractive.
Problem: Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates. The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations. Our abstractor is practically identical to the one proposed in BIBREF8.
Question: What's the method used here?
Solution:
|
Abstractive
|
task462_qasper_classification
|
NIv2
|
fs_opt
| 4
|
validation
|
You will be given a definition of a task first, then some input of the task.
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Be prepared for the sheer size of Australia when travelling through it.Stay in backpacker hostels and other budget accommodation.Take care of yourself. Take care when hiking. As discussed in the step above, the sun is fierce over Australia. Enjoy.
Output:
|
Sports and Fitness
|
task1630_openpi_classification
|
NIv2
|
zs_opt
| 1
|
train
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Example input: Make the character female. Create the face figure as "reptile" with 1,9 coloured eyes. Choose the studded belt, double armband, and full retro ninja outfit (with a colour of 1,10) for clothes and accessories. Choose your own voice, fighting styles, and other essentials. Get the "reptile" for the ending since they are the same race and their entire race was almost wiped out.
Example output: Hobbies and Crafts
Example explanation: The passage consists of creating a face figure and clothing and accessories. These words depict the category Hobbies and Crafts.
Q: Use your garden fork and dig into the ground from the side of the carrot's row. Get a good grip, but not a tight one, on the green tops while you pull them up. Inspect each carrot for holes and blemishes (color changes).Air dry them on the surface for about 20 minutes or so before putting them in storage.
A:
|
Home and Garden
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 3
|
train
|
Given the task definition, example input & output, solve the new input case.
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Example: Make the character female. Create the face figure as "reptile" with 1,9 coloured eyes. Choose the studded belt, double armband, and full retro ninja outfit (with a colour of 1,10) for clothes and accessories. Choose your own voice, fighting styles, and other essentials. Get the "reptile" for the ending since they are the same race and their entire race was almost wiped out.
Output: Hobbies and Crafts
The passage consists of creating a face figure and clothing and accessories. These words depict the category Hobbies and Crafts.
New input case for you: Find a suitable location. Plant in a container pot. Water frequently during hot weather.Nourish using liquid fertiliser every month during the warm months.
Output:
|
Home and Garden
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 1
|
train
|
Definition: Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Input: Do an ollie but crouch down a little before ollieing. Pop the tail down for a ollie. Grab onto the board, holding it for a bit. Keep your eyes on the landing.Land and roll away.
Output:
|
Sports and Fitness
|
task1630_openpi_classification
|
NIv2
|
zs_opt
| 2
|
train
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Wash the towels that you're going to use. Once the towels are wash and dried, set up your sewing machine. Fold each bath towel in half.Place on the ground.
Home and Garden
Remove all of the existing paint. Cover all openings with duct tape, and use a utility knife to trim the tape before you paint a motorcycle tank. Apply a minimum of 5 coats of primer.Apply several coats of base paint, and sand with 380-grit sandpaper in between coats.Air brush and add designs, if desired. Apply 3 to 4 coats of clear coat polymer.
Cars & Other Vehicles
Prepare the shot cups. Dissolve the gelatin into the boiling water in a large bowl.Measure and pour the alcohol into the bowl.Carefully pour the Jello mixture into the shot cups. Chill in the refrigerator.
|
Food and Entertaining
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 0
|
train
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Q: Put all of the ingredients in your blender. Turn on the Liquidate function on the blender. Turn on the chop or crush function on the blender. Set to mix or stir. Pour into a glass and enjoy.Finished.
A:
|
Food and Entertaining
|
task1630_openpi_classification
|
NIv2
|
zs_opt
| 4
|
train
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Example: Make the character female. Create the face figure as "reptile" with 1,9 coloured eyes. Choose the studded belt, double armband, and full retro ninja outfit (with a colour of 1,10) for clothes and accessories. Choose your own voice, fighting styles, and other essentials. Get the "reptile" for the ending since they are the same race and their entire race was almost wiped out.
Example solution: Hobbies and Crafts
Example explanation: The passage consists of creating a face figure and clothing and accessories. These words depict the category Hobbies and Crafts.
Problem: ######Fill the plastic bottle with 1/3 cups vinegar. ######Fill the bottle with water but leave 1.5 inches (6 cm) empty space left. ######Get 2 pieces of toilet paper and place 2 table spoons of baking soda on it fold it . ######Drill a small hole (carefully) on the cap and put the bag through the cap.######When ready to use push the bag in to the slack point and save the day.
|
Solution: Home and Garden
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 5
|
train
|
Teacher:Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Teacher: Now, understand the problem? Solve this instance: Melt the chocolate chips in a saucepan over low heat. Add the instant coffee/ground espresso beans. In a separate mixing bowl, blend eggs, coconut sugar, vanilla and salt on low speed. Pour the melted chocolate into the mixing bowl. Pour into a 8x8 baking dish, either greased or covered with parchment paper.Bake at 350 °F (177 °C) for 25-30 minutes.
Student:
|
Food and Entertaining
|
task1630_openpi_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Example Input: Follow the recipe's instructions properly.Use a metallic spoon or a rubber spatula. Add the delicate mixture to the heavy mixture. Use the metallic spoon or spatula in a cutting action. Continue until both mixtures are adequately combined.
Example Output: Food and Entertaining
Example Input: Buy a card reader.Be careful when buying memory cards.Batteries wear out over time, and it's almost guaranteed that an old camera will get substantially fewer shots on a charge than a new one. Older cameras generally do not do such a great job of getting superb images when shooting JPEGs, and often have some issues with colour accuracy. Shoot carefully.
Example Output: Hobbies and Crafts
Example Input: Grind or pulverize the seeds or roots just before using them. Add the ground seeds or roots to a saucepan of boiling water. Reduce the temperature of the boiling water to a simmer.Pour into your favorite mug and enjoy.
Example Output:
|
Food and Entertaining
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 3
|
test
|
Given a passage as input, answer with the category to which the passage belongs. There are 5 categories - Sports and Fitness, Home and Garden, Hobbies and Crafts, Food and Entertaining, Cars & Other Vehicles. The answer should be one of the categories based on words from the passage which closely belong to the category.
Example input: Make the character female. Create the face figure as "reptile" with 1,9 coloured eyes. Choose the studded belt, double armband, and full retro ninja outfit (with a colour of 1,10) for clothes and accessories. Choose your own voice, fighting styles, and other essentials. Get the "reptile" for the ending since they are the same race and their entire race was almost wiped out.
Example output: Hobbies and Crafts
Example explanation: The passage consists of creating a face figure and clothing and accessories. These words depict the category Hobbies and Crafts.
Q: Do not allow any child under 7 to snowboard. Get your child kitted out in protective gear. Talk to your child about the potential problems that might be encountered:Find suitable instruction for your child.Do not use hills that exceed your child's beginner ability.
A:
|
Sports and Fitness
|
task1630_openpi_classification
|
NIv2
|
fs_opt
| 3
|
validation
|
Q: In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
مردن به وقت شهریور به نظر من جز فیلم هایی هست که باید تو دانشگاه تدریس بشه! در زیر مجموعه چگونه فیلم بد بسازیم در هیچ زمینه ای حرفی برای گفتن نداشت.... فقط بازی خواهر کوچکتر خانواده این امید رو میده که ایشون آینده درخشانی خواهند داشت.<sep>Question: نظر شما در مورد شخصیت پردازی، بازیگردانی و بازی بازیگران فیلم مردن به وقت شهریور چیست؟
A:
|
mixed
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 7
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
چه از لحاظ فیلمنامه و چه از لحاظ کارگردانی یک تله فیلم کاملا معمولی. آقای بهزادی به خودشون اجازه دادن یک داستان دو سه خطی رو تبدیل به فیلمنامه کنند و 80 درصد نماهای فیلم رو هم که کلوزآپ گرفتند و کوچکترین جنبه سینمایی در فیلم مشاهده نمی شه . هیچی دیگه همین<sep>Question: نظر شما در مورد داستان، فیلمنامه، دیالوگ ها و موضوع فیلم وارونگی چیست؟
Output:
|
negative
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 1
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
خروج فیلمی با محوریت دروغ. فیلمی که فیلم نبود و این از حاتمی کیایی که پشتش سپاهه بعید نبود. جلوه های ویژه بسیار بد صدا برداری افتضاح واقعا ناراحتم که وقتم رو برای کاری به این شدت بی ارزش گذاشتم. متاسفم برای سینمای کشورم امتیاز 1 از 10 # طاها #<sep>Question: نظر شما در مورد صداگذاری و جلوه های صوتی فیلم خروج چیست؟
|
negative
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 0
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
امتیاز:6 سدمعبر در ظاهر فیلم روپایی است، ریتم و بازی و ماجراهای متنوع دارد، اما منطق داستانیاش میلنگد شتاب داشتن و حادثه پشت حادثه رخ دادن همیشه بهتر نیست، گاهی آهسته و پیوسته رفتن در نهایت به همان نتیجه منجر میشود. شعار هم چیز خوبی نیست، اصلا و ابدا.<sep>Question: نظر شما به صورت کلی در مورد فیلم سد معبر چیست؟
mixed
بسیار به دل نشست!!...اگرچه باز هم سوژه تکراری اینکه مسوولیت کسی را به عهده نگیرید وگرنه خانه خراب می شوید را داشت!...اما بازیهای فوق العاده، باعث میشد باز هم از این سوژه تکراری لذت ببری!<sep>Question: نظر شما در مورد فیلمبرداری و تصویربرداری فیلم شکاف چیست؟
no sentiment expressed
نمیدونم واقعا درباره این فیلم بگم خوب بود یا نه یه جاهاییشو دوس داشتم و یه سری چیزاش نه بعضی سکانسا در نیمده بود خیلی از چیزا قابل حدس بود<sep>Question: نظر شما به صورت کلی در مورد فیلم سارا و آیدا چیست؟
|
mixed
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
fs_opt
| 0
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
--------
Question: این فیلم عالی بود قرار نیست وقتی واقعیتهای جامعه و تلخی هاشو ببینیم حالمون خوب بشه چون این فیلم قصدش نشاندن این دردهای جامعه بود به آقای سیدی به خاطر این فیلم عالی تبریک می گویم ....<sep>Question: نظر شما در مورد صداگذاری و جلوه های صوتی فیلم مغزهای کوچک زنگ زده چیست؟
Answer: no sentiment expressed
Question: قبلا فیلم چند متر مکعب عشق رو تو جشنواره دیده بودم امسال هم از دیدن فیلم جدید برادران محمودی لذت بردم. دست خوش<sep>Question: نظر شما در مورد تهیه، تدوین، کارگردانی و ساخت فیلم شکستن همزمان بیست استخوان چیست؟
Answer: positive
Question: امتیاز: ۳ الی ۴ بیشتر فیلم تلویزیونی بود، حاصل سالوادور ساختن و فیلمهای اجتماعی ساختن همینه، فیلم در ۵ دقیقه آخر جنایی هم میشود. به حق چیزهای ندیده!<sep>Question: نظر شما در مورد داستان، فیلمنامه، دیالوگ ها و موضوع فیلم کارگر ساده نیازمندیم چیست؟
Answer:
|
negative
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
Example: من این فیلم رو دوست داشتم .شخصیت سارا بهرامی عالی بود و همچنین بازی امین حیایی بعنوان یک مرد خونسرد..بازی مهناز افشار هم عالی بود فیلم روایتی از روایتهای تلخ جامعه مان میباشد<sep>Question: نظر شما به صورت کلی در مورد فیلم دارکوب چیست؟
Example solution: positive
Example explanation: This is a good example. The sentiment of the review is positive toward the movie.
Problem: دوستان تقریبا نظر منو نوشتن . من فقط در مورد بازی ها میگم : بازی خوب صابر ابر ، بازی معمولی امیر آقایی ، بازی مصنوعی و ضعیف میلاد کی مرام و بازی بسیار ضعیف و بسیار تکراری طناز طباطبایی ...!!!!<sep>Question: نظر شما در مورد شخصیت پردازی، بازیگردانی و بازی بازیگران فیلم روسی چیست؟
|
Solution: negative
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
fs_opt
| 5
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
Input: Consider Input: مثل همه ی فیلم های قبلی کیایی، جذاب است و دغدغه مشکلات جوانان را دارد، اما مثل همه ی فیلمهایش بی مغز و سطحی هم هست. در این یکی آنقدر هیجان جذابیت و طنز حواسش را پرت کرده که حتی دغدغه هایش هم به حاشیه رفته.<sep>Question: نظر شما در مورد موسیقی فیلم بارکد چیست؟
Output: no sentiment expressed
Input: Consider Input: فیلمی که توی سینمای ایران بتونه 90 دقیقه با خودش همراهت کنه آه و ناله نکنه ولی همچین قرص و محکم ببرتت تا ته بدبختی ادا در نیاره فوق العاده نباشه ولی همه چیزش اندازه باشه سلبریتی تزئینی هم نداشته باشه به نظر من که فیلم خوب و ارزشمندیه ... پسندیدم<sep>Question: نظر شما در مورد موسیقی فیلم حمال طلا چیست؟
Output: no sentiment expressed
Input: Consider Input: امشب فیلم ماهی و گربه را در موزه سینما دیدم و باید بگم خیلی فراتر از تصورم بود، و به جرات می تونم بگم یک شاهکار دیدم ... خیلی ها در مورد این فیلم مطلب نوشتن ؛ امید وارم من هم بتونم در فرصتی در مورد این فیلم خاص بنویسم ...<sep>Question: نظر شما در مورد صداگذاری و جلوه های صوتی فیلم ماهی و گربه چیست؟
|
Output: no sentiment expressed
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
fs_opt
| 2
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
Q: فیلم تا وسط فیلم یعنی دقیقا تا جایی که معلوم میشه بچه های املشی دنبال رضان خیلی خوب و جذاب پیش میره ولی دقیقا از همونجاش سکته میزنه و خلاص...<sep>Question: نظر شما در مورد داستان، فیلمنامه، دیالوگ ها و موضوع فیلم ژن خوک چیست؟
A:
|
mixed
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 4
|
train
|
In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
Q: از هر طرف که به این سریال نگاه میکنی بد و بازدارنده س !! طراحی صحنه ، دکور ، گریم تقلبی بازیگرا ، بازی بد اکثر بازیگرا. از اقای جیرانی این حجم از ضعف رو انتظار نداشتیم<sep>Question: نظر شما به صورت کلی در مورد فیلم سریال نهنگ آبی چیست؟
A:
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negative
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 4
|
test
|
Q: In this task, you will be given a movie review and a question about the reviewer's sentiment toward one aspect of the movie in Persian. You have to infer the answer to the question from the review and classify it. Classify the reviewer's sentiment into: "no sentiment expressed", "negative", "neutral", "positive", and "mixed". The mixed category indicates reviews where none of the sentiments are dominant (mix of positive and negative, or borderline cases); hence it is hard to detect the primary sentiment. Also, assign neutral label to reviews that express no clear sentiment toward an entity or any aspect of it. The "no sentiment expressed" label should be assigned to the reviews where the given aspect didn't discuss in the text.
داستان فیلم رو دوست داشتم و به قول دوستان بازى مهتاب کرامتى خوب نبود تنها نکته ى مثبت فیلم به جز طراحى صحنه و لباس, بازى دلنشین باران کوثرى بود.<sep>Question: نظر شما در مورد صداگذاری و جلوه های صوتی فیلم جامه دران چیست؟
A:
|
no sentiment expressed
|
task525_parsinlu_movie_aspect_classification
|
NIv2
|
zs_opt
| 7
|
validation
|
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