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In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Ex Input:
sentence_A: A man is squatting in brush and taking a photograph. sentence_B: A person is crouching and holding a camera
Ex Output:
0
Ex Input:
sentence_A: Eggs are being strongly whisked in a bowl by a person. sentence_B: A person is strongly whisking eggs in a bowl
Ex Output:
0
Ex Input:
sentence_A: The snowboarder is jumping off a snow covered hill. sentence_B: The snowboarder is jumping off a snowy hill
Ex Output:
|
0
|
task1612_sick_label_classification
|
NIv2
|
fs_opt
| 1
|
train
|
Definition: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Input: sentence_A: There is no young child running outside over the fallen leaves. sentence_B: A young child is running outside over the fallen leaves
Output:
|
2
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 2
|
train
|
Part 1. Definition
In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Part 2. Example
sentence_A: A dancer is dancing on the stage. sentence_B: A girl is giving dance performance on the dais.
Answer: 0
Explanation: One sentence says, "Dancing on the stage" while the other sentence says, "Dance performance on the dais", which is clearly giving the same meaning and are related to each other. So the classification is entailment.
Part 3. Exercise
sentence_A: The woman is holding a whole tomato. sentence_B: The lady is slicing a tomato
Answer:
|
1
|
task1612_sick_label_classification
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
sentence_A: Two dogs are in a kennel on their hind legs and are facing one another. sentence_B: Two dogs are playing with each other
|
1
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 0
|
train
|
Definition: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Input: sentence_A: A young man on a bmx bicycle is jumping off a masonry pyramid. sentence_B: A man on a bike is jumping on a pyramid-shaped ramp
Output:
|
1
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 2
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
One example: sentence_A: A dancer is dancing on the stage. sentence_B: A girl is giving dance performance on the dais.
Solution is here: 0
Explanation: One sentence says, "Dancing on the stage" while the other sentence says, "Dance performance on the dais", which is clearly giving the same meaning and are related to each other. So the classification is entailment.
Now, solve this: sentence_A: A child is missing a baseball. sentence_B: A family is watching a little boy who is hitting a baseball
Solution:
|
1
|
task1612_sick_label_classification
|
NIv2
|
fs_opt
| 6
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
sentence_A: A dog is looking around. sentence_B: There is no dog looking around
Output:
|
2
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 1
|
train
|
In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
sentence_A: A policeman is sitting on a motorcycle. sentence_B: The cop is sitting on a police bike
|
0
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 0
|
train
|
Definition: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Input: sentence_A: Two crocodiles are floating in a green colored swimming pool near some playing kids. sentence_B: Two kids are pushing an inflatable crocodile in a pool
Output:
|
1
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 2
|
test
|
Q: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
sentence_A: The man is hammering a nail with a camera. sentence_B: The camera man is nailing a hammer to the wall
A:
|
1
|
task1612_sick_label_classification
|
NIv2
|
zs_opt
| 7
|
validation
|
Definition: In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Input: "process paragraph : : Prophase, the chromosomes become visible. Centrioles separate to move to opposite poles. Metaphase, the chromosomes line up in the center. Anaphase, the chromatids separate. Are pulled apart. Telophase, the chromosomes gather at opposite poles. Two new nuclear membranes form. The cell membranes pinch. Divide into two individual cells. ", "event : Centrioles separate to move to opposite poles.", "entity : chromosome"
Output:
|
unknown location
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 2
|
train
|
Teacher: In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Teacher: Now, understand the problem? If you are still confused, see the following example:
"process paragraph : Magma rises from deep in the earth. The magma goes into volcanos. The volcanos pressure the magma upwards. The pressure causes the magma to push through the surface of the volcano. The lava cools. The lava forms new rock. New magma is pressured to the surface of the volcano. The volcano bursts through the rock the formed after the last eruption" , "event : Magma rises from deep in the earth" , "entity : magma"
Solution: known location
Reason: The process paragraph explains in steps about - the cause for volcano eruption. The event is a part of the process paragraph given. The entity lies both in process paragraph and the event. So, this is a good example.
Now, solve this instance: "process paragraph : : A bird lays an egg. The egg hatches into a baby bird. Baby bird eats. Grows into an adult bird. The bird finds a mate. The pair build a nest. The birds lay eggs. ", "event : The egg hatches into a baby bird.", "entity : egg"
Student:
|
not exist
|
task1568_propara_classification
|
NIv2
|
fs_opt
| 2
|
train
|
Detailed Instructions: In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Q: "process paragraph : : Food or milk that could spoil easily is brought in. Through methods of heating the food is treated. The microbes that may proliferate in the food are destroyed. Depending on the treatment this happens a few more times. The food is considered pasteurized. ", "event : Through methods of heating the food is treated.", "entity : food"
A:
|
unknown location
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 9
|
train
|
In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Example input: "process paragraph : Magma rises from deep in the earth. The magma goes into volcanos. The volcanos pressure the magma upwards. The pressure causes the magma to push through the surface of the volcano. The lava cools. The lava forms new rock. New magma is pressured to the surface of the volcano. The volcano bursts through the rock the formed after the last eruption" , "event : Magma rises from deep in the earth" , "entity : magma"
Example output: known location
Example explanation: The process paragraph explains in steps about - the cause for volcano eruption. The event is a part of the process paragraph given. The entity lies both in process paragraph and the event. So, this is a good example.
Q: "process paragraph : : The primary root breaks from the seed. A shoot develops with a leaf. The shoot breaks through the soil as a seedling. The tree becomes a sapling when it gets taller than 3 ft. The mature tree produces flowers or fruit. Seeds are produced from the flowers or fruit. A dead or dying tree is known as a snag. ", "event : A shoot develops with a leaf.", "entity : primary root"
A:
|
unknown location
|
task1568_propara_classification
|
NIv2
|
fs_opt
| 3
|
train
|
Definition: In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Input: "process paragraph : : Coal is burned at a furnace. The resulting heat energy is used to heat water. The heated water is turned into steam. The steam drives a generator. Electricity is produced. ", "event : The resulting heat energy is used to heat water.", "entity : coal"
Output:
|
not exist
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 2
|
train
|
Given the task definition and input, reply with output. In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
"process paragraph : : The organism must die to begin the process. The soft tissue decomposes. The bones are left behind. Scavengers tear the body apart and move the bones elsewhere. Wind and rain also scatter the bones further. The bones left behind will weather and become buried by sand and soil. The proteins in the bone are replaced with minerals that are dissolved in the soil. This creates a rock-like substance called a fossil. Water and wind erosion wear away the layers of soil on top of the fossil. This makes discovery of the fossil possible. ", "event : The soft tissue decomposes.", "entity : organism"
|
not exist
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 5
|
train
|
In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
"process paragraph : : Water vapor gets into the atmosphere through a process called evaporation. This then turns the water that is at the top of oceans, rivers and lakes into water vapor in the atmosphere using energy from the sun. The water vapor rises in the atmosphere and there it cools down. Water vapor rises in the atmosphere and there it cools down and forms tiny water droplets through something called condensation. These then turn into clouds. When they all combine together, they grow bigger and are too heavy to stay up there in the air. This is when they will fall to the ground as rain, or maybe snow or hail by gravity. ", "event : This then turns the water that is at the top of oceans, rivers and lakes into water vapor in the atmosphere using energy from the sun.", "entity : water"
|
not exist
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 0
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Example: "process paragraph : Magma rises from deep in the earth. The magma goes into volcanos. The volcanos pressure the magma upwards. The pressure causes the magma to push through the surface of the volcano. The lava cools. The lava forms new rock. New magma is pressured to the surface of the volcano. The volcano bursts through the rock the formed after the last eruption" , "event : Magma rises from deep in the earth" , "entity : magma"
Output: known location
The process paragraph explains in steps about - the cause for volcano eruption. The event is a part of the process paragraph given. The entity lies both in process paragraph and the event. So, this is a good example.
New input case for you: "process paragraph : : Food. Water enter the body. Bloodstream. Food and water enter the pancreas. The pancreas breaks down carbs. Helps digest other foods and liquids. ", "event : Water enter the body.", "entity : carbs"
Output:
|
known location
|
task1568_propara_classification
|
NIv2
|
fs_opt
| 1
|
train
|
In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
Q: "process paragraph : : Coal is mined out of the ground. Coal is pulverized into fine powder. The coal is mixed with hot air. The coal and hot air are blown into a boiler. The coal and hot air burn over a fire. Highly purified water is pumped through pipes in the boiler. The water turns into steam. The steam presses against turbines. The turbines spin. Electricity is produced. ", "event : Coal is pulverized into fine powder.", "entity : coal"
A:
|
unknown location
|
task1568_propara_classification
|
NIv2
|
zs_opt
| 4
|
test
|
In this task, you are given a paragraph, event and an entity. The event is part of the given paragraph and it changes the state of the entity. Your task is to classify the state of the entity into three classes: 1) not exist, 2) unknown location and 3) known location. "not exist" means the entity doesn't exist in that state anymore. "unknown location" means the entity exists in that state but location is unknown. "known location" means the entity exists and location is known.
--------
Question: "process paragraph : : Tectonic plates smash together. The edges of the plates crumple up. The ridges are mountain ranges. Magma is forced to the surface. Magma forms a volcano. ", "event : The edges of the plates crumple up.", "entity : magma"
Answer: unknown location
Question: "process paragraph : : A mushroom gives off gills lined with basidia. The basidia gives off spores. The spores germinate. The spores create Hyphae. Multiple Hyphae meet and converge. They join with Mycelium. A hyphal knot forms in the ground. A primordia shoots out of the ground. A mushroom cap is formed. ", "event : The basidia gives off spores.", "entity : mushroom"
Answer: unknown location
Question: "process paragraph : : Nitrogen-fixing bacteria convert atmospheric nitrogen to organic nitrogen. Plants absorb organic nitrogen from the soil through their roots. Animals eat the plants. Plants and animals die. Animals expel wastes. Fungi convert organic nitrogen to ammonium. Nitrifying bacteria convert ammonium to nitrate. Bacteria convert nitrate to nitrogen gas. Nitrogen gas escapes from soil into the atmosphere. ", "event : Plants absorb organic nitrogen from the soil through their roots.", "entity : atmospheric nitrogen"
Answer:
|
not exist
|
task1568_propara_classification
|
NIv2
|
fs_opt
| 7
|
validation
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Spanish.
See one example below:
Problem: With economic growth, countries and societies enter into a virtuous cycle of upward mobility, opportunity and improved living standards.
Solution: Con el crecimiento, los países y las sociedades ingresan en un ciclo virtuoso de movilidad ascendente, de oportunidad y mejores niveles de vida.
Explanation: The English sentence is correctly translated into Spanish, because the meaning is preserved.
Problem: (Applause) Now, a little more difficult thing.
Solution:
|
(Aplausos) Ahora, un poco más difícil.
|
task1219_ted_translation_en_es
|
NIv2
|
fs_opt
| 4
|
train
|
Definition: You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Input: Americans have so often tried to disseminate their ideas of choice, believing that they will be, or ought to be, welcomed with open hearts and minds.
Output:
|
Los estadounidenses han intentado muy frecuentemente de diseminar sus ideas sobre elegir, creyendo que éstas serán, o deben ser recibidas con corazones y mentes abiertos.
|
task1219_ted_translation_en_es
|
NIv2
|
zs_opt
| 2
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Spanish.
I wanted to make it bigger.
|
Quería ir más lejos.
|
task1219_ted_translation_en_es
|
NIv2
|
zs_opt
| 0
|
train
|
Detailed Instructions: You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Q: We will get where we're going faster and society will recapture vast amounts of lost productivity now spent sitting in traffic basically polluting.
A:
|
Llegaremos adonde vamos más rápido y la sociedad recuperará vastas cantidades de productividad perdida hoy sentados en el tráfico básicamente contaminando.
|
task1219_ted_translation_en_es
|
NIv2
|
zs_opt
| 9
|
train
|
Definition: You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Input: It's not like print. It's not like video.
Output:
|
No es impresión. No es video.
|
task1219_ted_translation_en_es
|
NIv2
|
zs_opt
| 2
|
train
|
instruction:
You are given a sentence in English. Your job is to translate the English sentence into Spanish.
question:
One, on average, comes from Europe.
answer:
Uno, en promedio, viene de Europa.
question:
We can share ideas with other people, and when they discover them, they share with us.
answer:
Podemos compartir las ideas con otros y cuando ellos las descubran, harán lo mismo.
question:
(Laughter) Ladies and gentlemen, meet your cousins.
answer:
|
(Risas) Damas y caballeros, conozcan a sus primos.
|
task1219_ted_translation_en_es
|
NIv2
|
fs_opt
| 9
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Spanish.
--------
Question: And so we started looking, and we said, we have to do things in a different way.
Answer: Empezamos a analizar, y dijimos, tenemos que hacer las cosas de forma diferente.
Question: The Harvard men never ask that question.
Answer: Los hombres de Harvard nunca hacen esa pregunta.
Question: So Andrew said to me, he said, "" What if we stop thinking about these as drugs?
Answer:
|
Andrew me dijo: "" ¿Y si dejamos de pensar en esto como fármacos?
|
task1219_ted_translation_en_es
|
NIv2
|
fs_opt
| 7
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Q: She's got a lot of insight.
A: Ella tiene muchas ideas.
****
Q: And I heard adults of that day and time say, "That's reckless, expensive, may well fail."
A: Y oí adultos de ese día y e incluso hoy dicen: "" Eso es imprudente, caro, puede fallar "".
****
Q: That means about 190 miles per hour.
A:
|
Es decir, como 190 millas por hora.
****
|
task1219_ted_translation_en_es
|
NIv2
|
fs_opt
| 4
|
train
|
You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Example Input: Despite the enormous influence of religion on the world today, we hold them to a different standard of scrutiny and accountability than any other sector of our society.
Example Output: A pesar de la enorme influencia de la religión en el mundo de hoy, la mantenemos en un nivel diferente de escrutinio y rendición de cuentas que cualquier otro sector de nuestra sociedad.
Example Input: He said, "" Miraculous. "" And his wife said, "" This is the happiest moment of my life. "" And you wonder why.
Example Output: Dijo: "" Milagroso "". Y su esposa dijo: "" Este es el momento más feliz de mi vida "". Y se preguntan por qué.
Example Input: This prevented the system from dying in the middle of the night.
Example Output:
|
Esto impidió que el sistema quede sin señal en plena noche.
|
task1219_ted_translation_en_es
|
NIv2
|
fs_opt
| 3
|
test
|
Instructions: You are given a sentence in English. Your job is to translate the English sentence into Spanish.
Input: Now, those yellow balls have funny sticks on the end, so babies could do other things with them if they wanted to.
Output:
|
Las bolas amarillas tienen palos divertidos al final, así los bebés podrían hacer otras cosas con ellas si quisieran.
|
task1219_ted_translation_en_es
|
NIv2
|
zs_opt
| 3
|
validation
|
Please answer the following question: Question: What to you cause by hurting someone else? Options: - punishment - happiness - legal problems - physical pain - public outcry Explain why a human would choose "physical pain" to answer the question above:
A:
|
physical pain is one result of hurting someone.
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_opt
| 8
|
train
|
Question: He enjoyed attending lecture, it was his favorite way of what? Options: - applaud - take notes - learning - going online - taking notes Explain why a human would choose "learning" to answer the question above:
Ans: a lecture is a method of learning
Question: On what can you relax in your bedroom other than your bed? Options: - footbed - chair - sofa - furniture - couch Explain why a human would choose "chair" to answer the question above:
Ans: relax on a chair
Question: Some people have many pairs of jeans, they store them in their what? Options: - gap - mailbox - shopping mall - closet - thrift store Explain why a human would choose "closet" to answer the question above:
Ans: clothes are generally stored in the closet
Question: Sam thought that James was a native, because of how Lane did what to James? Options: - guide foreigners - introduced - foreigner - established - immigrant Explain why a human would choose "introduced" to answer the question above:
|
Ans: introduce | definition of introduce in english by oxford dictionaries
|
cos_e_v1_11_explain_why_human
|
P3
|
fs_opt
| 0
|
train
|
Please answer the following question: Question: If playing the saxophone wasn't enough, she also had to learn the choreographed moves on the field to perform with the what? Options: - music store - actors - store sells instruments - marching band - orchestra Explain why a human would choose "marching band" to answer the question above:
A:
|
a marching band is a group
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_noopt
| 8
|
train
|
Answer the following question: Question: If people are competing for resources what emotional state are they likely to experience? Options: - defeat - winning or losing - aggression - confusion - stress Explain why a human would choose "stress" to answer the question above:
Answer:
|
rivers flow trough valleys.
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_noopt
| 5
|
train
|
Question: Where is a fox likely to be caught? Options: - tennessee - inside joke - grassy field - the forrest - england Explain why a human would choose "grassy field" to answer the question above:
----
Answer: in full grassy field
Q: Question: Dan thought that he was bad. But hid Queen disagreed. She knighted him. She thought he was what? Options: - exceptional - upright - choice - sufficient - worthy Explain why a human would choose "worthy" to answer the question above:
A: rivers flow trough valleys.
Question: Question: Where is one likely to keep a stylus? Options: - school - garage - hand - record player - palm pilot Explain why a human would choose "palm pilot" to answer the question above:
Answer: palm pilot likely to keep a stylus
[Q]: Question: Where are all employees likely to carry a weapon? Options: - war - police station - army bunker - security personnel - holster Explain why a human would choose "police station" to answer the question above:
****
[A]: police station - wikipedia
input: Please answer the following: Question: what is a characteristic of one who want to give assistance? Options: - help one - helpful - listen - prepared - humanity Explain why a human would choose "helpful" to answer the question above:
++++++++++
output: helpful people usually are more than wiling to provide assistance when needed to
Question: The cabin wasn't as small as it seemed. It was, in fact, multiple bunkrooms connected by a what? Options: - room - hall - palace - spacecraft - villa Explain why a human would choose "hall" to answer the question above:
----
Answer:
|
a hall is most likely to connect different rooms.
|
cos_e_v1_11_explain_why_human
|
P3
|
fs_opt
| 0
|
train
|
Question: The weasel would go round and round the what while waiting to attack? Options: - apple tree - rabbit warren - chicken coop - mulberry bush - viking ship Explain why a human would choose "mulberry bush" to answer the question above:
The answer to this question is:
|
mulberry bush weasel would go round and round the what while waiting to attack
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_noopt
| 7
|
train
|
input: Please answer the following: Question: He was stabbing to death his wife when he was caught, at trial he still denied the what? Options: - bleeding - imprisonment - mess - killing - give up Explain why a human would choose "killing" to answer the question above:
++++++++++
output: a trial is about a crimianl charge.
Please answer this: Question: Why would someone not want to be cogitating? Options: - dance - decision - headaches - reaching conclusion - enlightenment Explain why a human would choose "headaches" to answer the question above:
++++++++
Answer: cogitating causes headaches
Problem: Question: Where do you find jellyfish? Options: - store - office - cuba - photographs - pond Explain why a human would choose "pond" to answer the question above:
A: smaller a lake
Problem: Given the question: Question: If a crowd of people are all in a relationship except for one, what can they be considered? Options: - single person - few people - individual - small group - fun Explain why a human would choose "single person" to answer the question above:
++++++++++++++++++++++++++++++++
The answer is:
a single is not in a relationship
input question: Question: They began to copulate, someone used spit to what? Options: - lubricate - mark territory - kiss - procreation - release tension Explain why a human would choose "lubricate" to answer the question above:???
output answer: spit is slippery.
input question: Question: What should the bean bag chair sit on? Options: - house - den - family room - wood - floor Explain why a human would choose "floor" to answer the question above:???
output answer:
|
rivers flow trough valleys.
|
cos_e_v1_11_explain_why_human
|
P3
|
fs_opt
| 8
|
train
|
Question: Who do you give money to at a ticket booth? Options: - venue - movie theater - museum - train station - clerk Explain why a human would choose "clerk" to answer the question above:
|
clerk | definition of clerk at dictionary.com
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_noopt
| 0
|
train
|
Question: If you tend to fiddle with your shoes they are likely what? Options: - you're bored - stolen from the store - like music - uncomfortable - were bored Explain why a human would choose "uncomfortable" to answer the question above:
A:
|
fiddle is not to be comfortable
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_noopt
| 2
|
test
|
Question: A person who is successful at bringing people to the table probably makes what? Options: - delicious food - compliments - acquire wealth - bread - self esteem Explain why a human would choose "delicious food" to answer the question above:
A:
|
delicious food is always served at the table
|
cos_e_v1_11_explain_why_human
|
P3
|
zs_opt
| 2
|
validation
|
Q: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Lefty, Loosey, Righty, Tighty
A:
|
lefty loosey righty tighty that be the status of my horn if i do n't work hard to tighten and loosen them then my horn will be goney
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 7
|
train
|
Definition: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Input: Chewing Gum
Output:
|
you hear chew pop and lot of other sound it taste so minty and sweet it's blue and white it smell like mint it taste so spicy it wrapping be blue and white it's messy and sticky and really hot and cool
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 2
|
train
|
You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Q: I See
A:
|
outside i see beautiful thing a bird in a tree some buzzing bee as far a the eye can see wildflower grow everywere so beautiful indeed i've never see anything as beautiful a this everytime i close my eye that be all i see
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 4
|
train
|
You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
ThunderStorms
|
grey dark cloud light rain here it come it's a pain flash light rule the sky burn lightning flashing by light go out power go refrigerator off power go no computer power go no t. v. power go no music power go this the power of a thunderstorm i will be glad when it be go
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 0
|
train
|
Instructions: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Input: Keep It Up
Output:
|
if you keep it up you'll be so sorry although i do not like to smile if im not near you
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 3
|
train
|
You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Q: There is a.....
A:
|
there be once wood and in those wood there be a path and on that path there be a house and in that house there be you
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 4
|
train
|
Definition: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Input: The Lost Cat
Output:
|
once there be a little lost cat that get hit with a ball and a baseball bat the little cat be so lose a you can see but then he come and look and he look and he find me
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 2
|
train
|
Instructions: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Input: Call of duty
Output:
|
call of duty be a lot of fun especially when i level up my gun run around a big huge map take out every one in sight it real easy dont you fright i take them all out at night call of duty be fun but not real great i would rather spend my time catch a snake
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 3
|
train
|
You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
[EX Q]: The Big Test
[EX A]: sweat down your forehead hoping you get it all right yeah i get an a
[EX Q]: honey bees
[EX A]: honey bee suckle be loyal to thier queen watch out they can sting
[EX Q]: Dragonfly
[EX A]:
|
dragonfly be insect they have six leg and long thin body dragonfly have two big eye and two small antenna dragonfly have a mouth with sharp jaw for grab and eat other bug most dragonfly be as long a your finger their wing can be winder than your hand
|
task1711_poki_text_generation
|
NIv2
|
fs_opt
| 6
|
test
|
Detailed Instructions: You are given a short text as a title. Your task is to generate a poem as output that is related to the given title and should feel like written by kids. The output should be a run-on sentence (two or more complete sentences connected without any punctuation). The poem should not be too long or too complex, because it should feel like it is written by younger person without high level of literature education.
Q: Good things
A:
|
all good thing must come to an end
|
task1711_poki_text_generation
|
NIv2
|
zs_opt
| 9
|
validation
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
One example: story: Rita loved riding her bike down the trails of the state park. While she was riding, she came across a bear. She immediately hit a side path. She pedaled as fast as she could out of the state park. Rita will now only go on the trails when she is with friends.
selected sentence: Rita will now only go on the trails when she is with friends.
Solution is here: Rita feel(s) scared >Motivates> Rita will only go on the trails with her friends
Explanation: The story is about Rita being scared in her experience, so the answer is correct.
Now, solve this: story: Julie was on the couch when she saw her mom. She jokingly asked her mom to make her a sandwich. For some reason, her mom actually said yes. She went and made Julie a really nice sandwich. Then, she let Julie eat it on the couch.
selected sentence: She jokingly asked her mom to make her a sandwich.
Solution:
|
jULIE feel(s) hungry >Motivates> Julie asks her mom to make her a sandwich
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 6
|
train
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Q: story: Lia went to Las vegas with her friends. She played at slots for a while. She did not win so she decided to leave. On her way out she played one last slot. And she won the grand jackpot!
selected sentence: Lia went to Las vegas with her friends.
A: Lia want(s) to play the slots >Motivates> Lia goes to las vegas
****
Q: story: Dan went on a fishing trip to Lake Erie. He went out on the lake in his boat. He casted out his fishing pole and waited. A fish grabbed his bait he lost his fishing pole. He gave up on fishing and went home.
selected sentence: He gave up on fishing and went home.
A: Dan feel(s) hopeless >Motivates> He gives up on fishing
****
Q: story: Jan got a new puppy. She named it Bailey. Bailey cried at all hours. Jan had to let Bailey sleep with her. Now Bailey is a spoiled pup.
selected sentence: She named it Bailey.
A:
|
Jan like(s) the name Bailey >Motivates> Jan names the puppy Bailey
****
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 4
|
train
|
Detailed Instructions: In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Problem:story: Nancy had an uncle who was very sick. She went to visit him to say her final goodbye. With a tear in her eye she told him how much she cared. Later on he passed away peacefully. Nancy was sad but knew he was no longer in pain.
selected sentence: Nancy was sad but knew he was no longer in pain.
Solution:
|
Nancy like(s) her uncle >Motivates> Nancy is sad
|
task747_glucose_cause_emotion_detection
|
NIv2
|
zs_opt
| 8
|
train
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Ex Input:
story: Emma was working on her math homework. She asked her dad to help her. Her dad didn't understand the problem. Emma called her classmate. Emma's classmate explained the problem.
selected sentence: Her dad didn't understand the problem.
Ex Output:
Her dad want(s) to help her with the problem >Motivates> He doesn't understand the problem
Ex Input:
story: We were standing outside our building on a summer night. We had young children. They looked up and pointed to the sky. We saw two bats flying around. We went inside.
selected sentence: We were standing outside our building on a summer night.
Ex Output:
We want(s) relaxation >Motivates> We stand outside of our building on a summer night
Ex Input:
story: Joseph likes pets. He has two dogs and three cats. One day one of the dogs got lost. Joseph tried to find him very hard. He finally find him.
selected sentence: Joseph tried to find him very hard.
Ex Output:
|
Joseph feel(s) concern >Motivates> Joseph tries to find his dog
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 1
|
train
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
story: Chris loved the NFL. He always wanted to go to a game. So one evening his dad tells him he's gonna be taking him to a game. Chris gets so excited and jumps up and down. Chris and his dad go to the game and have a great time.
selected sentence: Chris gets so excited and jumps up and down.
|
Chris feel(s) excitement >Motivates> Chris jumps up and down
|
task747_glucose_cause_emotion_detection
|
NIv2
|
zs_opt
| 0
|
train
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Ex Input:
story: I wanted to learn how to fly. I prayed I would wake up with a pair of wings. I wanted to fly away from that place and never be found. I knew it wasn't possible. Still, I dreamed of the day I could escape.
selected sentence: Still, I dreamed of the day I could escape.
Ex Output:
I feel(s) depressed >Motivates> I want to escape
Ex Input:
story: We're trying to potty train our new kitty. We bought her a pretty pink litter box with a lid and sliding tray. We put her in it every time she eats to see if she will use it. So far she seems to be getting used to it. She likes to scratch the sand up behind her.
selected sentence: So far she seems to be getting used to it.
Ex Output:
The cat want(s) cleanliness >Motivates> She is getting used to the litter box
Ex Input:
story: It was Asher and Tiffany's anniversary. They had been married for a long time. She wanted to surprise him. She planned a great trip. He was stunned!
selected sentence: She planned a great trip.
Ex Output:
|
Tiffany feel(s) giving >Motivates> Tiffany plans a great trip
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 1
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Example: story: Rita loved riding her bike down the trails of the state park. While she was riding, she came across a bear. She immediately hit a side path. She pedaled as fast as she could out of the state park. Rita will now only go on the trails when she is with friends.
selected sentence: Rita will now only go on the trails when she is with friends.
Output: Rita feel(s) scared >Motivates> Rita will only go on the trails with her friends
The story is about Rita being scared in her experience, so the answer is correct.
New input case for you: story: I worked in a church for a while as a youth minister. The kids loved me, though they were a bit quiet and shy. We did things all summer, and had a fantastic time. Though, their parents didn't trust me because of my age. They had me fired, and I had to say goodbye to their kids.
selected sentence: We did things all summer, and had a fantastic time.
Output:
|
The kids and I like(s) doing things >Motivates> The kids and I do things
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 1
|
train
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Input: Consider Input: story: Thomas smoked 2 or 3 packs of cigarettes per day. After a few years, Thomas started to get sick. His doctor told him that if he didn't stop, he would die. Thomas wouldn't stop smoking. Thomas got COPD and died in a few years.
selected sentence: His doctor told him that if he didn't stop, he would die.
Output: His doctor want(s) him to live >Motivates> His doctor tells him to stop smoking
Input: Consider Input: story: A young boy named Bill was having trouble writing his name. He could not complete his assignment for school because of this. He went to his parents and asked them for help. His dad was able to teach him how to write his name. The boy was excited and completed his homework in time.
selected sentence: The boy was excited and completed his homework in time.
Output: Bill want(s) to avoid getting in trouble >Motivates> Bill completes his homework
Input: Consider Input: story: I bought a spaghetti squash to cook for dinner. It tried to cut it so I could cook it. Unfortunately, it was very hard and my knife slipped. It cut my hand very badly. I threw the squash away and ordered a pizza instead.
selected sentence: I threw the squash away and ordered a pizza instead.
|
Output: I like(s) ordering pizza >Motivates> I order pizza
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 2
|
train
|
Detailed Instructions: In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
Q: story: Vince wanted to go to the party on Saturday night. His parents asked him to clean his room before he can go. Vince decided not to clean his room at all. His parents explained that he can't go to the party. Vince stayed home and cleaned his room on Saturday night.
selected sentence: Vince wanted to go to the party on Saturday night.
A:
|
Vince feels social >Motivates> Vince wants to go to a party
|
task747_glucose_cause_emotion_detection
|
NIv2
|
zs_opt
| 9
|
test
|
In this task, you will be given a short story. One sentence from the story is chosen. Consider the likely emotions and basic human drives of the participants in that sentence. Does any of these states of mind/feelings motivate the participant to do what happens in that sentence? You should write your answer in the form " A >Motivates> B". Try to use phrases and sentences from the story to compose your answer when possible. For the motivation sentence, you must choose a verb from :feel(s), want(s) or like(s). There will always be some motivation in the given story.
[Q]: story: Nita saw a rabbit in her yard. She wanted to keep it as a pet. She went outside with a big net. But the rabbit hopped away very quickly! Nita was unable to catch it.
selected sentence: But the rabbit hopped away very quickly!
[A]: The rabbit feel(s) scared >Motivates> The rabbit hops away
[Q]: story: Anne loved comfortable shoes. She put on some flip flops. They were squishy and comfortable. Anne loved the sound they made as she walked. The flip flops were her favorite shoes!
selected sentence: The flip flops were her favorite shoes!
[A]: Anne want(s) comfort >Motivates> The flip flops are her favorite shoes
[Q]: story: It was Feliciano's birthday. His mother said there was one present left. He had no idea what it would be. His mother left the room and returned with a new kitten! Feliciano knew he and his kitten would be friends forever.
selected sentence: Feliciano knew he and his kitten would be friends forever.
[A]:
|
Feliciano feel(s) love >Motivates> Feliciano and his kitten would be friends forever
|
task747_glucose_cause_emotion_detection
|
NIv2
|
fs_opt
| 5
|
validation
|
question: "The sun was covered by a thick cloud all morning, but luckily, by the time the picnic started, it was out." is true.
So, is "The sun was out." true as well?
OPTIONS:
- no
- yes
prediction: yes
question: "Archaeologists have concluded that humans lived in Laputa 20,000 years ago. They hunted for deer on the river banks." is true.
So, is "Archaeologists hunted for deer on the river banks." true as well?
OPTIONS:
- no
- yes
prediction: no
question: "Thomson visited Cooper's grave in 1765. At that date he had been dead for five years." is true.
So, is "Thomson had been dead for five years." true as well?
OPTIONS:
- no
- yes
prediction: no
question: "The donkey wished a wart on its hind leg would disappear, and it did." is true.
So, is "The donkey wished a wart on its hind leg would disappear, and leg did." true as well?
OPTIONS:
- no
- yes
prediction:
|
no
|
glue_wnli_2_0_0
|
Flan2021
|
fs_opt
| 9
|
train
|
Let's say that "The path to the lake was blocked, so we couldn't use it."
Can we now say that "We couldn't use the lake."?
|
no
|
glue_wnli_2_0_0
|
Flan2021
|
zs_noopt
| 3
|
train
|
Multi-select: Is it possible to draw the conclusion that "The juggler was very impressive." if "John was jogging through the park when he saw a man juggling watermelons. He was very impressive."?
Pick from:
+ no;
+ yes;
|
yes
|
glue_wnli_2_0_0
|
Flan2021
|
zs_opt
| 7
|
train
|
Multi-select: Is it possible to draw the conclusion that "Mr. Schmidt's work was beautiful." if "Every day after dinner Mr. Schmidt took a long nap. Mark would let him sleep for an hour, then wake him up, scold him, and get him to work. He needed to get him to finish his work, because his work was beautiful."?
Choose your answer from:
(a). no
(b). yes
|
(b).
|
glue_wnli_2_0_0
|
Flan2021
|
zs_opt
| 7
|
train
|
Multi-choice problem: Is "We should make fewer of the chocolate chip cookies." true if "Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make fewer of them."?
Pick from: -- no; -- yes;
|
yes
|
glue_wnli_2_0_0
|
Flan2021
|
zs_opt
| 8
|
train
|
Problem: If "Of one thing Mark was sure. Harry knew much less than he did.", is "Harry knew much less than Harry did." correct?
Answer: no
Problem: If "The Wainwrights treated Mr. Crowley like a prince until he made his will in their favor; then they treated him like dirt. Folks said he died just to be rid of their everlasting nagging.", is "Folks said he died just to be ride of the folks' everlasting nagging." correct?
Answer: no
Problem: If "This book introduced Shakespeare to Goethe; it was a fine selection of his writing.", is "It was a fine selection of Goethe's writing." correct?
Answer:
|
no
|
glue_wnli_2_0_0
|
Flan2021
|
fs_noopt
| 3
|
train
|
Does "Since Jade always wears a red turban, Alice spotted her quickly." appear to be an accurate statement based on "Alice looked for her friend Jade in the crowd. Since she always wears a red turban, Alice spotted her quickly."?
|
yes
|
glue_wnli_2_0_0
|
Flan2021
|
zs_noopt
| 5
|
train
|
If "The firemen arrived before the police because they were coming from so far away.", can we conclude that "The police were coming from so far away."
Pick your answer from:
-- no.
-- yes.
I think the answer is
|
yes
|
glue_wnli_2_0_0
|
Flan2021
|
zs_opt
| 0
|
train
|
Input: Can we say "I put the knife in the drawer." if "I used an old rag to clean the knife, and then I put it in the drawer."?
Output: yes
Input: Can we say "We couldn't use the lake." if "The path to the lake was blocked, so we couldn't use it."?
Output: no
Input: Can we say "The train was delayed, so it worked out." if "My meeting started at 4:00 and I needed to catch the train at 4:30, so there wasn't much time. Luckily, it was delayed, so it worked out."?
Output:
|
yes
|
glue_wnli_2_0_0
|
Flan2021
|
fs_noopt
| 6
|
test
|
"I used an old rag to clean the knife, and then I put it in the trash." is a true sentence.
Does this mean that "I put the rag in the trash."?
Available choices: a). no b). yes
|
b).
|
glue_wnli_2_0_0
|
Flan2021
|
zs_opt
| 4
|
validation
|
Determine the topic of the passage. "The winner was 20-year-old Teyona Anderson from Woodstown, New Jersey ." Topic:
The answer to this question is:
|
America's Next Top Model, Cycle 12
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
zs_opt
| 7
|
train
|
Determine the topic of the passage. "At in length and or more in weight, it is the largest known animal to have ever existed." Topic:
Answer:
|
Blue whale
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
zs_opt
| 1
|
train
|
Please answer the following question: Determine the topic of the passage. "A major producer of natural gas , oil , and agriculture, Oklahoma relies on an economic base of aviation, energy, telecommunications, and biotechnology ." Topic:
A:
|
Oklahoma
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
zs_opt
| 8
|
train
|
[Q]: Determine the topic of the passage. "With advance orders exceeding one million copies in the United Kingdom, "I Want to Hold Your Hand" would ordinarily have gone straight to the top of the British record charts on its day of release (29 November 1963) had it not been blocked by the group's first million seller " She Loves You ", the Beatles' previous UK single, which was having a resurgent spell in the top position following intense media coverage of the group." Topic:
****
[A]: I Want to Hold Your Hand
input: Please answer the following: Determine the topic of the passage. "Ultimately, the Spanish Crown ceded the colony to United States rule." Topic:
++++++++++
output: Seminole Wars
Please answer this: Determine the topic of the passage. "The Rapture is a term in Christian eschatology which refers to the "being caught up" discussed in 1 Thessalonians 4:17, when the "dead in Christ" and "we who are alive and remain" will be "caught up in the clouds" to meet "the Lord in the air"." Topic:
++++++++
Answer: Rapture
Problem: Determine the topic of the passage. "The database forked from Borland 's open source edition of InterBase in 2000, but since Firebird 1.5 the code has been largely rewritten." Topic:
A: Firebird (database server)
Problem: Given the question: Determine the topic of the passage. "The ancient Egyptians had a system of medicine that was very advanced for its time and influenced later medical traditions." Topic:
++++++++++++++++++++++++++++++++
The answer is:
History of medicine
Problem: Determine the topic of the passage. "Nanotechnology may be able to create many new materials and devices with a vast range of applications , such as in medicine , electronics , biomaterials and energy production." Topic:
A:
|
Nanotechnology
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
fs_opt
| 6
|
train
|
input: Please answer the following: Determine the topic of the passage. "They are: wisdom, understanding, wonder and awe (fear of the Lord) , counsel, knowledge, fortitude, and piety (reverence)." Topic:
++++++++++
output: Seven gifts of the Holy Spirit
input: Please answer the following: Determine the topic of the passage. "The show is based on forensic anthropology and forensic archaeology , with each episode focusing on an FBI case file concerning the mystery behind human remains brought by FBI Special Agent Seeley Booth ( David Boreanaz ) to the forensic anthropologist Dr. Temperance "Bones" Brennan ( Emily Deschanel )." Topic:
++++++++++
output: Bones (TV series)
input: Please answer the following: Determine the topic of the passage. "The American Civil War (ACW), also known as the War between the States or simply the Civil War (see naming ), was a civil war fought from 1861 to 1865 between the United States (the "Union" or the "North") and several Southern slave states that declared their secession and formed the Confederate States of America (the "Confederacy" or the "South")." Topic:
++++++++++
output: American Civil War
input: Please answer the following: Determine the topic of the passage. "The Rhine (; ; ) is a European river that runs from the Swiss canton of Grisons in the southeastern Swiss Alps through Germany and eventually flows into the North Sea coast in the Netherlands and is the twelfth longest river in Europe , at about , with an average discharge of more than ." Topic:
++++++++++
output:
|
Rhine
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
fs_opt
| 5
|
train
|
Determine the topic of the passage. "The largest Muslim country is Indonesia , home to 12.7% of the world's Muslims, followed by Pakistan (11.0%), India (10.9%), and Bangladesh (9.2%)." Topic:
Answer:
|
Islam by country
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
zs_opt
| 1
|
train
|
Question: Determine the topic of the passage. "It occurs when using a photographic flash very close to the camera lens (as with most compact cameras ), in ambient low light." Topic:
Answer:
|
Red-eye effect
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
zs_opt
| 4
|
train
|
Please answer this: Determine the topic of the passage. "The term "effective" is used because the shielding effect of negatively charged electrons prevents higher orbital electrons from experiencing the full nuclear charge by the repelling effect of inner-layer electrons." Topic:
++++++++
Answer: Effective nuclear charge
Please answer this: Determine the topic of the passage. "The Northern Cardinal (Cardinalis cardinalis) is a North American bird in the genus Cardinalis ; it is also known colloquially as the redbird or common cardinal." Topic:
++++++++
Answer: Northern Cardinal
Please answer this: Determine the topic of the passage. "Mariel Margaret "Mia" Hamm (born March 17, 1972) is a retired American professional soccer player." Topic:
++++++++
Answer:
|
Mia Hamm
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
fs_opt
| 6
|
train
|
input: Please answer the following: Determine the topic of the passage. "As the second largest city in San Diego County , Chula Vista has quickly become a destination popular to many tourists." Topic:
++++++++++
output: Chula Vista, California
input: Please answer the following: Determine the topic of the passage. "Chaucer is a crucial figure in developing the legitimacy of the vernacular , Middle English , at a time when the dominant literary languages in England were French and Latin." Topic:
++++++++++
output: Geoffrey Chaucer
input: Please answer the following: Determine the topic of the passage. "This is the process by which an offspring cell or organism acquires or becomes predisposed to the characteristics of its parent cell or organism." Topic:
++++++++++
output: Heredity
input: Please answer the following: Determine the topic of the passage. "More than sixty percent of Vietnamese Americans reside in the states of California , Texas , Washington , Florida , and Virginia ." Topic:
++++++++++
output:
|
Vietnamese American
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
fs_opt
| 5
|
test
|
Q: Determine the topic of the passage. "An academic discipline, or field of study, is a branch of knowledge that is taught and researched at the college or university level." Topic:
A: List of academic disciplines
Q: Determine the topic of the passage. "Some tags are powered and read at short ranges (a few meters) via magnetic fields ( electromagnetic induction )." Topic:
A: Radio-frequency identification
Q: Determine the topic of the passage. "It was released on November 15, 2001 in North America, February 22, 2002 in Japan, and March 14, 2002 in Australia and Europe." Topic:
A: Xbox
Q: Determine the topic of the passage. "HCV is spread primarily by blood-to-blood contact associated with intravenous drug use , poorly sterilized medical equipment and transfusions ." Topic:
A:
|
Hepatitis C
|
wiki_qa_Topic_Prediction_Answer_Only
|
P3
|
fs_opt
| 2
|
validation
|
Detailed Instructions: In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Problem:1, ['K', 'i', 'j', '4979', 'F']
Solution:
|
i, j, 4979, F
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 8
|
train
|
Detailed Instructions: In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
See one example below:
Problem: 3, ['a', '34', 'f', '931', '7', '3432', '13245', '762']
Solution: 931, 7, 3432, 13245, 762
Explanation: Here, all the elements except the first 3 from the list are '931', '7', '3432', '13245', and '762'.
Problem: 5, ['P', '3317', 'K', '5615', '4055', 'W', '6687', 'x', 'V', '9097', 'G', 'f', '2815', '2133', 'K', '983', 'o']
Solution:
|
W, 6687, x, V, 9097, G, f, 2815, 2133, K, 983, o
|
task064_all_elements_except_first_i
|
NIv2
|
fs_opt
| 4
|
train
|
In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Example Input: 2, ['K', 'Y', '3597', '4527', '9037', 'o', '8797', 'o', '5875', '4173', '4819', 'I', '9995', 'C', 'B', 'A', 'q', '7691', '9033', 'U', '1917', 'C', 'o', 'y', '2821', '6723', 'N']
Example Output: 3597, 4527, 9037, o, 8797, o, 5875, 4173, 4819, I, 9995, C, B, A, q, 7691, 9033, U, 1917, C, o, y, 2821, 6723, N
Example Input: 6, ['P', 'Q', 'B', '1123', 'H', '5293', '5351', '5299', '6337', '3771', '1493', '5053', '1933', 'c', 'D', '761', 'R', '9275', '319', 'P', '5919', 'e']
Example Output: 5351, 5299, 6337, 3771, 1493, 5053, 1933, c, D, 761, R, 9275, 319, P, 5919, e
Example Input: 2, ['1735', '169', '1269', '9529', '3539', 'L', 'C', 'I', '5187', 'p', 'D', 'Z', 'd', '5575', 'n', 'Y', 'b', 'v', '3405', '8107', 'A', 'N', '8969', 'w', '4533']
Example Output:
|
1269, 9529, 3539, L, C, I, 5187, p, D, Z, d, 5575, n, Y, b, v, 3405, 8107, A, N, 8969, w, 4533
|
task064_all_elements_except_first_i
|
NIv2
|
fs_opt
| 3
|
train
|
Instructions: In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Input: 2, ['9837', 'p', '5587', 'L', '6723', 'K', 'f', '5443', 'o', 'W', 'R', '4595', '8277', 'm', '6075', 's', '421', 'S', 'i', '2859', 'A', '1873', '711', 'B', '4973', '4463', 'A', 'i', '9941', '6637']
Output:
|
5587, L, 6723, K, f, 5443, o, W, R, 4595, 8277, m, 6075, s, 421, S, i, 2859, A, 1873, 711, B, 4973, 4463, A, i, 9941, 6637
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 3
|
train
|
In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
[Q]: 2, ['4985', 'K', '61', '4915']
[A]: 61, 4915
[Q]: 2, ['8163', 'v', 'D', 'h', 'U', 'E', '9031', '6007', '3907', '9697']
[A]: D, h, U, E, 9031, 6007, 3907, 9697
[Q]: 2, ['J', 'g', 'X', 'm', 'Y', '2589', '283', 'b', 'Z', 'r', 'a']
[A]:
|
X, m, Y, 2589, 283, b, Z, r, a
|
task064_all_elements_except_first_i
|
NIv2
|
fs_opt
| 5
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
11, ['6185', '1071', 'b', 'L', '7765', '7491', '8839', 'T', '9439', 'I', 'N', 'O', '2179', 'h', '3575', 'G', '9469', 'l', '8359', 'g', '6135', '8971', '8537']
Output:
|
O, 2179, h, 3575, G, 9469, l, 8359, g, 6135, 8971, 8537
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 1
|
train
|
Detailed Instructions: In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Problem:2, ['i', '1497', 's', '9559', '9873', 'Y', '4729', 'n', '8131', 'u', 'c', '5011', 'z', 'b', 'u', 'o', '9165', 'N', '525']
Solution:
|
s, 9559, 9873, Y, 4729, n, 8131, u, c, 5011, z, b, u, o, 9165, N, 525
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 8
|
train
|
In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
[Q]: 2, ['c', 'Z', 'q', '7135', '4087', 'V', 'j', '761', '3841', 'Q', 'u']
[A]: q, 7135, 4087, V, j, 761, 3841, Q, u
[Q]: 7, ['8521', 'd', 'c', '1313', 'k', '6863', '2039', 'x', '533', 'a', '8409', '7947', '6473', 'v']
[A]: x, 533, a, 8409, 7947, 6473, v
[Q]: 2, ['t', '3801', 'B', '1793', '1561', 'a', '5063', '8057', 'V', '7919', 'e', '3103', '7643', '4795', 'R', 'd', 'U', 'U', 'u', '2103']
[A]:
|
B, 1793, 1561, a, 5063, 8057, V, 7919, e, 3103, 7643, 4795, R, d, U, U, u, 2103
|
task064_all_elements_except_first_i
|
NIv2
|
fs_opt
| 5
|
train
|
Instructions: In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Input: 2, ['441', 'Y', 'S', '4179', '1707', '3147', '6203', '1143', '9645', '269', '3369', '5573', 'V', 'K', '5979', 'a', '8909']
Output:
|
S, 4179, 1707, 3147, 6203, 1143, 9645, 269, 3369, 5573, V, K, 5979, a, 8909
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 3
|
test
|
Teacher:In this task, you are given inputs i and A, where i is an integer and A is a list. You need to list all the elements of A after the first i elements. i will always have a value less than the length of A.
Teacher: Now, understand the problem? Solve this instance: 3, ['4231', 'f', 'J', '9203', 'W', 's', '7523', '6713', 'C', '7549', 'E', 'U', 'i', 'd', '741', 'H', '9377']
Student:
|
9203, W, s, 7523, 6713, C, 7549, E, U, i, d, 741, H, 9377
|
task064_all_elements_except_first_i
|
NIv2
|
zs_opt
| 6
|
validation
|
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Example Input: Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.
Example Output: An Asynchronous Branch-and-Bound DCOP Algorithm
Example Input: Metric learning seeks a transformation of the feature space that enhances prediction quality for the given task at hand. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lowerand upper-bounds showing that the sample complexity scales with the representation dimension when no assumptions are made about the underlying data distribution. However, by leveraging the structure of the data distribution, we show that one can achieve rates that are fine-tuned to a specific notion of intrinsic complexity for a given dataset. Our analysis reveals that augmenting the metric learning optimization criterion with a simple norm-based regularization can help adapt to a dataset’s intrinsic complexity, yielding better generalization. Experiments on benchmark datasets validate our analysis and show that regularizing the metric can help discern the signal even when the data contains high amounts of noise.
Example Output: Sample Complexity of Learning Mahalanobis Distance Metrics
Example Input: In the election of a hierarchical clustering method, theoretic properties may give some insight to determine which method is the most suitable to treat a clustering problem. Herein, we study some basic properties of two hierarchical clustering methods: α-unchaining single linkage or SL(α) and a modified version of this one, SL∗(α). We compare the results with the properties satisfied by the classical linkage-based hierarchical clustering methods.
Example Output:
|
ON THE PROPERTIES OF α-UNCHAINING SINGLE LINKAGE HIERARCHICAL CLUSTERING
|
task1540_parsed_pdfs_summarization
|
NIv2
|
fs_opt
| 3
|
train
|
Detailed Instructions: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Problem:<lb>Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated<lb>equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal<lb>welfare. This work provides two main technical results that lift this conclusion to games of incomplete<lb>information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows directly from<lb>the smoothness-based proof of near-optimal welfare in the same game when the private information<lb>is public. Second, no-regret learning dynamics converge to Bayesian coarse correlated equilibrium in<lb>these incomplete information games. These results are enabled by interpretation of a Bayesian game<lb>as a stochastic game of complete information.
Solution:
|
No-Regret Learning in Repeated Bayesian Games
|
task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 8
|
train
|
Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
Student:
|
A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization
|
task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 6
|
train
|
Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism. Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image. Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information. To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features. We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.
|
Task-driven Visual Saliency and Attention-based Visual Question Answering
|
task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 5
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Example: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Output: A Neural Local Coherence Model
This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
New input case for you: Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our proposed model significantly outperforms the state-of-the-art pipeline models for both NLU and SAP, which indicates that our joint model is capable of mitigating the affects of noisy NLU outputs, and NLU model can be refined by error flows backpropagating from the extra supervised signals of system actions.
Output:
|
END-TO-END JOINT LEARNING OF NATURAL LANGUAGE UNDERSTANDING AND DIALOGUE MANAGER
|
task1540_parsed_pdfs_summarization
|
NIv2
|
fs_opt
| 1
|
train
|
Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Résumé Elections unleash strong political views on Twitter, but what do people really think about politics ? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author’s name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.
|
Active learning in annotating micro-blogs dealing with e-reputation
|
task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 5
|
train
|
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Example: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Example solution: A Neural Local Coherence Model
Example explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Problem: Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.
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Solution: A Review of Machine Learning based Anomaly Detection Techniques
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task1540_parsed_pdfs_summarization
|
NIv2
|
fs_opt
| 5
|
train
|
Detailed Instructions: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Problem:We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth manifold on which the skill policies lie. This manifold models how policy parameters change as task parameters vary. The method identifies the number of charts that compose the manifold and then applies non-linear regression in each chart to construct a parameterized skill by predicting policy parameters from task parameters. We evaluate our method on an underactuated simulated robotic arm tasked with learning to accurately throw darts at a parameterized target location.
Solution:
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Learning Parameterized Skills
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task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 8
|
train
|
Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection between parts, which can provide a more valid notion of similarity. In general however, optimal assignments yield indefinite functions, which complicates their use in kernel methods. We characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection. We apply these results by developing the Weisfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original Weisfeiler-Lehman kernel.
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On Valid Optimal Assignment Kernels and Applications to Graph Classification
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task1540_parsed_pdfs_summarization
|
NIv2
|
zs_opt
| 5
|
test
|
Detailed Instructions: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
See one example below:
Problem: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Solution: A Neural Local Coherence Model
Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Problem: To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a twolayered supervised learning model, or learn the features directly using a deep (multilayered) model. While the first approach is very problem-specific, the second approach has computational overheads in learning multiple layers and fine-tuning of the model. In this paper, we propose an approach called wide learning based on arc-cosine kernels, that learns a single layer of infinite width. We propose exact and inexact learning strategies for wide learning and show that wide learning with single layer outperforms single layer as well as deep architectures of finite width for some benchmark datasets.
Solution:
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To go deep or wide in learning?
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task1540_parsed_pdfs_summarization
|
NIv2
|
fs_opt
| 4
|
validation
|
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