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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 Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
1975 - 76年のNBAシーズンは、全米バスケットボール協会の30番目のシーズンでした。
Solution: 1975 - 76年の全米バスケ... | アルゼンチン政府は戦争の最後の日まで中立を保ったが、1945年にヴィシー、フランス、ベルギー、そしてドイツから逃げるナチの指導者たちの参入を黙って容認した。 | task776_pawsx_japanese_text_modification | NIv2 | fs_opt | 0 | train |
Detailed Instructions: Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
See one example below:
Problem: 1975 - 76年のNBAシーズンは、全米バスケットボール協会の30番目のシーズンでした。
Solution: 1975 - 76年の全米バスケットボール協会のシーズンは、NBAの第30シーズンでした。
Explanation: The paraphrase of the or... | これはシーズンを完了する最後のAFLチャンピオンシップであり、1966年シーズンに続いて最初のスーパーボウルでした。 | task776_pawsx_japanese_text_modification | NIv2 | fs_opt | 4 | train |
Detailed Instructions: Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
Q: 画期的な「スマッシュ」（1994年）の4曲目と3曲目のシングルです。
A: | 画期的なアルバム「Smash」（1994年）の4曲目と3曲目のシングルです。 | task776_pawsx_japanese_text_modification | NIv2 | zs_opt | 9 | train |
Detailed Instructions: Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
Q: スズメはターナーにエリザベスが胸を見つけるために魔法の羅針盤で解放されることができると確信させます。
A: | スズメはターナーに、エリザベスが胸を見つけるために魔法の羅針盤を使うことによって解放されることができると確信させます。 | task776_pawsx_japanese_text_modification | NIv2 | zs_opt | 9 | 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 Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
1975 - 76年のNBAシーズンは、全米バスケットボール協会の30番目のシーズンでした。
Solution: 1975 - 76年の全米バスケ... | 彼は1685年頃にケベックに引っ越し、しばらくの間ニューフランスに住んでいました。 | task776_pawsx_japanese_text_modification | NIv2 | fs_opt | 0 | train |
Given the task definition and input, reply with output. Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
以前の編集者には、Michel Foucault、Emmanuel Le Roy Ladurie、Georges Dumezil、Francois Jacob、Jacques Le Goff、Francois Furet、Raymond Aronがあります。
| 過去の編集者には、Raymond Aron、Georges Dumezil、Francois Jacob、Michel Foucault、Emanuel Roy Ladurie、Francois Furet、およびJacques Le Goffが含まれます。 | task776_pawsx_japanese_text_modification | NIv2 | zs_opt | 5 | train |
Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
One example: 1975 - 76年のNBAシーズンは、全米バスケットボール協会の30番目のシーズンでした。
Solution is here: 1975 - 76年の全米バスケットボール協会のシーズンは、NBAの第30シーズンでした。
Explanation: The paraphrase of the original sentence in Japanese is cor... | 209年の初めに、サンカンは、サンカンの妹レディーサンと結婚し、彼と劉備との同盟関係を強化しました。 | task776_pawsx_japanese_text_modification | NIv2 | fs_opt | 6 | train |
Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
Example Input: Dantèsの友人、Fernand Mondego（Sidney Blackmer）は彼を刑務所に連れて行きます。
Example Output: Dantèsの友人Sidney Blackmer（Fernand Mondego）は彼を刑務所に連れて行きます。
Example Input: Taizhou Station（浙江省）は中華人民共和国浙江省Taizh... | 「非遺伝性球状赤血球症」という用語は、時折ではありますが、めったに使用されません。
| task776_pawsx_japanese_text_modification | NIv2 | fs_opt | 3 | train |
Instructions: Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
Input: デビーダウナーは2004年にデビューし、レイチェルドラッチによって描かれた架空の "サタデーナイトライブ"のキャラクターの名前です。
Output: | デビーダウナーは2004年にデビューし、レイチェルドラッチによって描かれた架空の「サタデーナイトライブ」のキャラクターの名前です。 | task776_pawsx_japanese_text_modification | NIv2 | zs_opt | 3 | test |
Given a sentence in Japanese, provide an equivalent paraphrased version from the original that retains the same meaning.
クィントゥスMetellusはローマの政治家と一般のルキウスMetellusクラウンの次男でした。 | クィントゥスMetellusは、ローマの政治家の次男、一般ルキウスMetellus冠でした。 | task776_pawsx_japanese_text_modification | NIv2 | zs_opt | 0 | validation |
In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's questions or follo... | user
| task1599_smcalflow_classification | NIv2 | fs_opt | 6 | train |
Instructions: In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's ques... | user | task1599_smcalflow_classification | NIv2 | zs_opt | 3 | train |
Instructions: In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's ques... | agent | task1599_smcalflow_classification | NIv2 | zs_opt | 3 | train |
Instructions: In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's ques... | user | task1599_smcalflow_classification | NIv2 | zs_opt | 3 | train |
In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's questions or follo... | user
| task1599_smcalflow_classification | NIv2 | fs_opt | 3 | train |
In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's questions or follo... | user | task1599_smcalflow_classification | NIv2 | zs_opt | 0 | train |
Given the task definition and input, reply with output. In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent'... | user | task1599_smcalflow_classification | NIv2 | zs_opt | 5 | train |
Teacher:In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's questions ... | user | task1599_smcalflow_classification | NIv2 | zs_opt | 6 | train |
Instructions: In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's ques... | agent | task1599_smcalflow_classification | NIv2 | zs_opt | 3 | test |
In this task, you are given an utterance, which is a part of a conversation between a user and an agent. Your job is to detect the speaker. The user usually commands the agent to schedule events, retrieve event information, and check the schedule. While the agent's response is an answer to the user's questions or follo... | user
| task1599_smcalflow_classification | NIv2 | fs_opt | 0 | validation |
TASK DEFINITION: In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
PROBLEM: 予見できないヒーロー&衝撃的なシーン . この映画には2つのショッキングな見せ場のシーンがあり、他はそこに至る前置きと後日... | POS
| task486_cls_japanese_dvd_classification | NIv2 | fs_opt | 8 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
Example: １~２話で... | NEG | task486_cls_japanese_dvd_classification | NIv2 | fs_opt | 1 | train |
Teacher:In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
Teacher: Now, understand the problem? Solve this instance: オー！駄作 . つまらない作品です。見る価値もな... | NEG | task486_cls_japanese_dvd_classification | NIv2 | zs_opt | 6 | train |
In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
スカッとしない映画でした . 観ている人を飽きさせない映画でしたが、物事が何も解決しないままに終了してしまうようなラストでスカッとしませんでした（それが監督の狙いなのでしょうが）。 | NEG | task486_cls_japanese_dvd_classification | NIv2 | zs_opt | 0 | train |
Detailed Instructions: In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
Q: ありゃりゃ？ . 北野武監督の映画が好きだったのと、予告編のビジュアルが綺麗だったので期待してたのですが、感想は・・・ありゃりゃ？... | NEG | task486_cls_japanese_dvd_classification | NIv2 | zs_opt | 9 | 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 dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentime... | POS | task486_cls_japanese_dvd_classification | NIv2 | fs_opt | 0 | train |
Given the task definition, example input & output, solve the new input case.
In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
Example: １~２話で... | POS | task486_cls_japanese_dvd_classification | NIv2 | fs_opt | 1 | train |
TASK DEFINITION: In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
PROBLEM: やめられないとまらない。 . 仕事が忙しい人は、軽い気持ちで見ないで下さい。 必ず寝不足になります。「え~っ！ここでこの人が！！」... | NEG
| task486_cls_japanese_dvd_classification | 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 are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
ニューカレドニアの美しい小... | POS | task486_cls_japanese_dvd_classification | NIv2 | zs_opt | 1 | test |
You will be given a definition of a task first, then some input of the task.
In this task, you are given dvd product reviews in Japanese language. The goal is to classify the review as "POS" if the overall sentiment of the review is positive or as "NEG" if the overall sentiment of the review is negative.
星１個以下です。 . はっ... | NEG | task486_cls_japanese_dvd_classification | NIv2 | zs_opt | 1 | validation |
Detailed Instructions: In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a ... | D | task611_mutual_multi_turn_dialogue | NIv2 | zs_opt | 8 | train |
Instructions: In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F ... | A | task611_mutual_multi_turn_dialogue | NIv2 | zs_opt | 3 | train |
instruction:
In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F a... | C
| task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 9 | train |
In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F and M indicate... | B | task611_mutual_multi_turn_dialogue | NIv2 | zs_opt | 4 | train |
In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F and M indicate... | C | task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 6 | train |
Detailed Instructions: In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a ... | C | task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 4 | train |
In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F and M indicate... | B
| task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 5 | train |
In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F and M indicate... | Output: C
| task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 2 | train |
Definition: In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a comma. F an... | A | task611_mutual_multi_turn_dialogue | NIv2 | zs_opt | 2 | test |
Detailed Instructions: In this task you are given a small conversation between two persons and 4 options on how the conversation should continue. Your job is to choose the most reasonable option. The conversation and the options are separated by a newline character. Each dialogue in the conversation are separated by a ... | C | task611_mutual_multi_turn_dialogue | NIv2 | fs_opt | 4 | validation |
You will be given a definition of a task first, then some input of the task.
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists o... | Her mom gave her a great recipe the one time she wanted to cook her own dinner. Kate bought all the ingredients and followed them exactly. The dinner she cooked was perfect! | task269_csrg_counterfactual_story_generation | NIv2 | zs_opt | 1 | train |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | I was scared that I would throw up on my friends. Luckily, it was a burp. Alas, my friends still made fun of me for burping. | task269_csrg_counterfactual_story_generation | NIv2 | zs_opt | 0 | train |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | Output: She tries to call and get back together with him. He ignores the phone calls. He's happier with someone else.
| task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 2 | train |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | Solution: The parents got frustrated because they had bought her presents. They decided to give her one more chance. She didn't come and so they decided not to give her the presents. | task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 5 | train |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | At first she couldn't find anything but then she found one. As the movie went on she started enjoying the movie. Nancy was happy that she had decided to stay home.
| task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 7 | train |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | The boss asked her boyfriend to sign a contract. He reads it and sign it. She is happy that he is finally signed.
****
| task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 4 | train |
Definition: Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a s... | Steele watched as his team practiced , while he sat tirelessly for a month. On the day of the big game, Steele was cheering the team on. Steele watched as his backup kicked the winning goal for his team | task269_csrg_counterfactual_story_generation | NIv2 | zs_opt | 2 | train |
TASK DEFINITION: Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence o... | Dan wanted to get his daughter in the picture, so he tried again. While looking through the pictures, Dan saw something funny. Dan noticed his dog had photo bombed the second tree photo.
| task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 8 | train |
Teacher: Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a stor... | When she woke up, she realized how much she missed her long hair. She cried and went to her mom. Her mom helped her take better care of her hair until it grew out again. | task269_csrg_counterfactual_story_generation | NIv2 | fs_opt | 2 | test |
Given a premise, an initial context, an original ending, and a counterfactual context, the task is to generate a new story ending aligned with the counterfactual context and as close to the original ending as possible. Each instance consists of a five-sentence story. The premise is the first sentence of a story, and th... | The trip was still going to happen as I caught the next flight. I had to be rushed to the next gate. Thankfully I was on time but it was still scary. | task269_csrg_counterfactual_story_generation | NIv2 | zs_opt | 0 | validation |
Detailed Instructions: In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Me... | Village | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 8 | train |
Definition: In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Mean of trans... | Company | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 2 | train |
Given the task definition and input, reply with output. In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist,... | Artist | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 5 | train |
Detailed Instructions: In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Me... | Building | task631_dbpedia_14_incorrect_answer_generation | NIv2 | fs_opt | 4 | train |
In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Mean of transportation, 7... | Natural place | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 4 | train |
In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Mean of transportation, 7... | Written work | task631_dbpedia_14_incorrect_answer_generation | NIv2 | fs_opt | 9 | 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're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the foll... | Building | task631_dbpedia_14_incorrect_answer_generation | NIv2 | fs_opt | 0 | train |
Q: In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Mean of transportation... | Educational Institution | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 7 | train |
Detailed Instructions: In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Me... | Animal | task631_dbpedia_14_incorrect_answer_generation | NIv2 | zs_opt | 9 | test |
In this task, you're given a text and question. The question is about the topic of the document. You are expected to generate an incorrect answer. The incorrect answer should be among one of the following categories: 1)Company, 2)Educational Institution, 3)Artist, 4)Athlete, 5)Office holder, 6)Mean of transportation, 7... | Solution: Athlete | task631_dbpedia_14_incorrect_answer_generation | NIv2 | fs_opt | 5 | validation |
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
Q: You ... | You are likely to name a lot in a group .
****
| task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 4 | train |
Detailed Instructions: Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of ... | You can use a glass of regular to dip a cookie in . | task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 4 | train |
Detailed Instructions: Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of ... | You are careful to lack newspapers at a train station . | task413_mickey_en_sentence_perturbation_generation | NIv2 | zs_opt | 8 | train |
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
Q: You w... | You would rate a commission because you paid money to see it . | task413_mickey_en_sentence_perturbation_generation | NIv2 | zs_opt | 4 | train |
You will be given a definition of a task first, then some input of the task.
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense pl... | You would shop by h uff because you have no cash . | task413_mickey_en_sentence_perturbation_generation | NIv2 | zs_opt | 1 | train |
Part 1. Definition
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it b... | You can use a chess king to play a chess game. | task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 7 | train |
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
Example... | You are likely to find a cat in the pack . | task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 3 | train |
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
You are... | You are likely to find a round trip translator in an airport .
| task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 0 | train |
Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is to have reasonable probability of it being true.
Let me ... | You are likely to find an apple tree around in a countryside. | task413_mickey_en_sentence_perturbation_generation | NIv2 | fs_opt | 8 | test |
Given the task definition and input, reply with output. Given a sentence, generate a new sentence by performing small changes on the sentence. Here, make sure that the changes are semantically related and syntactically similar to the input. And the generated sentence should have high commonsense plausibility, that is t... | Information can be stored as derivatives of magnetic charges . | task413_mickey_en_sentence_perturbation_generation | NIv2 | zs_opt | 5 | validation |
Detailed Instructions: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Q: and the CEO, National Payment Corporation of India (NPCI) will look into the industry cost structure of such transactions which ... | మంత్రిత్వ శాఖ కార్యదర్శి మరియు నేషనల్ పేమెంట్ కార్పొరేషన్ ఆఫ్ ఇండియా (ఎన్పిసిఐ) ముఖ్య కార్యనిర్వహణ అధికారి (సిఇఒ) లతో కూడిన ఒక సంఘం పరిశీలిస్తుంది. | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 9 | train |
Q: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Therefore, we are not in the competition.
A: | అందువల్ల, మేము పోటీలో లేము. | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 7 | train |
Instructions: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Input: Measures related to MSMEs:
Output: | చిన్న మధ్యతరహా సంస్థల కోసం చేపట్టే చర్యలు: | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 3 | train |
Given the task definition, example input & output, solve the new input case.
A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Example: Our nation has an experience of 200-225 years in manufacturing metal... | 72 కోట్లు - అదేవిధంగా ఈ ఆర్ధిక సంవత్సరం 2018-19 (2018 జనవరి నుండి 2019 ఫిబ్రవరి వరకు 14 నెలలకు) రూ|| 7090. 68 కోట్ల భారం పడుతుంది. | task1047_pib_translation_english_telugu | NIv2 | fs_opt | 1 | train |
You will be given a definition of a task first, then some input of the task.
A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Ministry of Mines VIKAS PARV ON 1ST JUNE IN HYDERABAD TO COMMEMORATE THE COM... | కేంద్రంలో మోదీ ప్రభుత్వం 3 సంవత్సరాల పాన పూర్తి సందర్భంలో 2017 జూన్ 1న హైదరాబాద్ లో ‘వికాస్ పర్వ్’ ప్రత్యేక కార్యక్రమం - కేంద్ర మంత్రి శ్రీ దత్తాత్రేయ చేతుల మీదుగా ఈ కార్యక్రమం ప్రారంభం కానుంది: శ్రీ స్వపన్ కుమార్ దత్తా, డిప్యూటీ డైరెక్టర్ జనరల్ ఆఫ్ మైన్స్ సేఫ్టీ, సౌత్ సెంట్రల్ జోన్ | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 1 | train |
Q: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
So, that year, several events were organized taking everyone together.
A: | అందువల్ల, ఆ ఏడాది, అందరి ని కలుపుకొని, అనేక కార్యక్రమాల ను నిర్వహించాము. | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 7 | train |
A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Decline in the Total Fertility Rate (TFR) to 2.
సంతాన సాఫల్య నిష్పత్తి 2011లో 2.
The Prime Minister, Shri Narendra Modi, today interacted with young in... | కెన్యాతో భారతేశానికి చాలాకాలం నుంచి వాణిజ్య సంబంధాలు ఉన్నాయి.
| task1047_pib_translation_english_telugu | NIv2 | fs_opt | 0 | train |
Instructions: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Input: I salute and I pay my homage to them.
Output: | వారికి శిరసాభివందనం చేస్తూ శ్రద్ధాంజలి ఘటిస్తున్నాను. | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 3 | train |
Instructions: A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
Input: use of spacecraft and space systems and ground system and application of space technology.
Output: | అంతరిక్ష నౌక, అంతరిక్ష వ్యవస్థలు మరియు గ్రౌండ్ సిస్టమ్ ల యొక్క వినియోగం; | task1047_pib_translation_english_telugu | NIv2 | zs_opt | 3 | test |
A text is given in English. Translate it from the English language to the Telugu language. The translation must not omit or add information to the original sentence.
One example: Our nation has an experience of 200-225 years in manufacturing metal
Solution is here: మా దేశం తయారీలో 200-225 సంవత్సరాలలో అనుభవం ఉంది
Explan... | 15 కోట్ల గ్రామీణ గృహాలకు సురక్షిత మంచినీరు సరఫరా చేయాలి. | task1047_pib_translation_english_telugu | NIv2 | fs_opt | 6 | validation |
question: What is the answer: As in the famous song Girl From Ipanema, in which country is Ipanema?
answer: brazil
question: What is the answer: Somerset Maugham's novel 'The Moon And Sixpence' is basedon the life of which painter?
answer: paul gaugin
question: What is the answer: What style of jazz which began and w... | julienne | trivia_qa_rc_1_1_0 | Flan2021 | fs_noopt | 9 | train |
Problem: What is the name of the only lake in the Lake District?
======
A: bassenthwaite
Q: What does a predator hunt?
A: prey
Problem: The Centers for Disease Control are headquartered near what major city?
Answer: atlanta
Q: How many species of tarantula actually possess bites which are fatal to humans
-----
A:... | cyanide | trivia_qa_rc_1_1_0 | Flan2021 | fs_opt | 4 | train |
Problem: What is the capital of the archipelago in the South Atlantic Ocean called The Falkland Islands?
======
A: stanley
Problem: Blue Castello cheese is made in which country?
======
A: denmark
Problem: Which personality of the American West is buried next to her partner Frank Butler?
======
A: annie oakley
Pr... | phantom | trivia_qa_rc_1_1_0 | Flan2021 | fs_noopt | 0 | train |
Problem: What part of a human cell has its own DNA - separate from human DNA?
======
A: mitochondria
Problem: What structure, designed by John Paxton for Prince Albert, was destroyed by fire on the 30th November 1936?
======
A: crystal palace
Problem: April 6, 1896 saw the start of the modern Olympics era when what... | raven by edgar allen poe | trivia_qa_rc_1_1_0 | Flan2021 | fs_noopt | 0 | train |
What is the answer: What contest, held between 24 teams, was played in Dubai between 5 and 7 March 2009, and was won by Wales? | rugby sevens world cup | trivia_qa_rc_1_1_0 | Flan2021 | zs_opt | 3 | train |
Problem: What was the name of the competitor to the Matchbox die cast car, introduced by Mattel in 1968?
======
A: hot wheels
Problem: According to Coleridge, where did Kubla Khan decree his pleasure dome?
======
A: xanadu
Problem: "If you went to Bizet's ""Carmen"", what would you expect to see?"
======
A: opera
... | nicaragua | trivia_qa_rc_1_1_0 | Flan2021 | fs_noopt | 0 | train |
Write the answer: "Billy Blaze, an 8-year-old genius who constructed a spaceship in his backyard from old soup cans and other household objects called ""The Bean-with-Bacon Megarocket"" puts on his brother's football helmet and becomes what character in a video game series?" | commander keen | trivia_qa_rc_1_1_0 | Flan2021 | zs_noopt | 2 | train |
Answer this question.
Which actress is the mother of actresses Joely and the late Natasha Richardson? | vanessa redgrave | trivia_qa_rc_1_1_0 | Flan2021 | zs_opt | 4 | train |
Mei Li:
Which nursery rhyme is based on the events at Mount Kenmel on the border of France and Belgium??
Shuai Zheng:
grand old duke of york
Mei Li:
The 'Du Maurier Classic' is competed for in which sport??
Shuai Zheng:
women s golf
Mei Li:
The Dragon Boat Festival is held in Taiwan during which month of the year??
Shu... | may | trivia_qa_rc_1_1_0 | Flan2021 | fs_opt | 6 | test |
Problem: Religous reformers - which 16th century Oxford scholar was the first to translate the Bible into English from the original Greek texts? He was charged with heresy, strangled and burned at the stake in 1535 by the Roman Catholic Church.
======
A: william tyndale
Problem: In which Scottish city is the football... | stethoscope | trivia_qa_rc_1_1_0 | Flan2021 | fs_noopt | 0 | validation |
Detailed Instructions: A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logical... | Yes. | task021_mctaco_grammatical_logical | NIv2 | zs_opt | 9 | train |
Teacher:A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct if t... | No. | task021_mctaco_grammatical_logical | NIv2 | zs_opt | 6 | train |
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct if the seman... | Yes.
| task021_mctaco_grammatical_logical | NIv2 | fs_opt | 7 | train |
Definition: A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct ... | Yes. | task021_mctaco_grammatical_logical | NIv2 | zs_opt | 2 | train |
Detailed Instructions: A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logical... | Yes. | task021_mctaco_grammatical_logical | NIv2 | fs_opt | 4 | train |
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct if the seman... | Output: Yes.
| task021_mctaco_grammatical_logical | NIv2 | fs_opt | 2 | train |
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct if the seman... | No. | task021_mctaco_grammatical_logical | NIv2 | fs_opt | 6 | train |
instruction:
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct... | Yes.
| task021_mctaco_grammatical_logical | NIv2 | fs_opt | 9 | train |
Part 1. Definition
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically c... | No. | task021_mctaco_grammatical_logical | NIv2 | fs_opt | 7 | test |
instruction:
A question that is free of any grammatical or logcal errors, should be labeled 'yes', otherwise it should be indicated as 'no'. A question is grammatically correct if all its entities i.e. nouns, verbs, adjectives, prepositions, pronouns, adverbs are at appropriate position. A question is logically correct... | Yes.
| task021_mctaco_grammatical_logical | NIv2 | fs_opt | 9 | validation |
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