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Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-a74670ded9154c71bad9d456068ebbc8
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-686810c536cc46c0838afde6f794bad4
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-0d8ddd5027ed42f7a09e890061ea8e78
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-19488eaaaae54275a310c926bb36f38f
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-c130197662cd49c8abbc27dbabbdbfda
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-9a6791111ad641eb8da452b55ff948e0
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-18d15fc27b16421782c8e50897301562
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-4d1ca521553f46d0942e28b2613cc508
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-352e31233fcd45068669916a03a3162b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-8ec3ff1c7dba43ce805950e753ff1ad6
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-217d191fa5b04c2db114bdb0201e7b76
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-725d576776af4f03976df6d59e7acfc8
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-431622ba3b4a48f4bd6554bdd12ff465
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-d0cbb625666e4082bcc19d86296ad7a0
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-7fc7bdf7a2ed4285b4fca4b2f83f4c12
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-c5986b7e3cb840fca11c716c21474792
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-4a44139c55d84ef6bfb1c304f3054404
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-b657a72d593e4e68abaae6cef621ac93
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-4f338b01045d41f19f33981986ab8e6b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-023d61281a1745ee8dfcaa610f7da59e
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-f7d517ddb9e749dd85a2dae1dd9ae839
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-c098ad3c94684e82b734cd52ad9b72e2
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-23995ea5c9154ed6993770c4ada0a5bb
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-5f8b6df243924a67a3bfc88a00525cb2
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-90c174950a2b4899b2fc9560119a7197
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-10ab4bde95a44a99ba3ff90100218675
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-95341b252a5b4d50ad9569d0cb086a64
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-97bcaa39655a4b298ea2610d6e76a12c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-6d1a9ef89f8647a7b3acdaa41ad3f4ea
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-082d7afe25414d8e9b40aae935fa9489
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-34edf459817e4ed488ef4a339d10f395
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-64a50dfbe3c44273b9ff03c99081287c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-eb65a50f5df3465bb8df945a29323316
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-8668696b98d14642b1d01c0b902ef84b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-1d2312723ba5486aaa6a24c8724e8047
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-fe65194d75ba4ff796d28aec543db201
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-a2a88f97d670480eb688621c68061add
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-939837f64ee8401889fa8c3ef7354fc1
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-efccdf7556a54a5897d49b47ea5e2f8e
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-90ca79905bea41ef9d297ade2b2370ec
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-c19f87b7d8e8445ebd3e799479f1b1cb
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-a5f49c3b56984d1994adcfdb905378e5
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-32b9d42a15684059942e3b3bacf9ce38
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-7f1760e25c7f4358849b7e7c69b47b2b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-c416daf2cb5c4e5eb367e7034bbf1958
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-b28d587aa03744f5b038d53093fcc9a4
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-3401f3b54044409aa64acf8252807d32
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-60920954e6804c44a72b3ba8b539f0fa
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-18d0618f824f48b99739b7872fb4ca5c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-d0b08e4efd184fb9a189acfe72d25c73
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-38c732b5858649329a8e108d1d3383c1
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-1a385917e42c4f429ef965e3abb3a654
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-5c137175da9d4bc7a80f786cf3580e54
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-b95406b5b1864b85804996a08907617e
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-4fdeeb435bd44e97bf2b9341ec812e60
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-18dcbc4f0fd7401895c8209a01d572d0
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-7f0fe14cc4584192959c41d8f2a9b4ab
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-5bbf1b662ef7404db4f926b16585c63a
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-a45c984613da40e18574521722e8a0dc
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-f681b334779e42afaba0ddab90f6eab1
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-89fa1ee914da454f97ec43f9d8cb048d
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-a225e3541d33474d8373fb3b67665ca4
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-b3614735b66e4a19aaedeeaaf3de8685
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-8945b58cf6ba41b78ce4ec366071450a
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-cdb1ec86290b48b2bd74322fc6e2b06c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-93772d662d604e5fbedd0fe15c5cd4d2
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-4e848337d778439fac371e911635d716
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-ba0175bcc4a8462ea977af366df70cc5
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-69f27f81e81645d1976e7440f8d6d94b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-73574fbffacf4835942e88f2f8301aa6
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-06b35854064a4149a3bc74c8e165f544
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-e4377a93207849a089f903f70d512def
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-3ff0a03727c34901868936899c70f970
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-3ebd242783db46228647cb9960b6a062
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-dcc598ff596240f0a57e4d550fcc53fd
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-ab20556a1b5d4c38b6ea2ddc565e2b9d
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-5c5a092607a849feb673e4bec2c9f1b8
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-0474f2851c56468aaa5e8fe1ef7d3feb
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-36350957a21e4a45bba5cab3ef9f8cae
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-12b3a4d4a7d04435b1846082923b5108
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-5dba4ba3d0934be295e523f553d1220c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-da658adc13604027b78fcef2d4e4a9be
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-aae051a5015a4b0f812d9b02c08ed0b0
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-c4403a41039a4031803ee444cb099e28
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-ac9807aeb74c447b8bced2dcadf9da4b
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-496220a73b714e698685ccbe1a973f2c
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-5f7c5aa3571343b4960bb8c8d5a222f1
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-bf0115e5b00a4d13b78720342d917e80
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-e89a2c7b069540019110dbea00fd10a9
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-09f3fa34ddff4709ba75cdf7f6c3d89e
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-ce485dd27bba405691d2ff82ebae120e
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-4d08b184c11f49d89930e15155956be5
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-de0ff39fa44d4b15bf19454250091e01
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "neutral" ]
task209-a98c815f19344e2d89d1be46f100ec2f
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-094f1ebdf1f64946b83b2fb1e2315cee
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-fea28b82a2094aaa89c71ac3cf612956
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-179042d0c3f24379afd0506a619a43b3
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-4856ecb5f50d43a394e4f9dd0fc4c501
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "in favor" ]
task209-19e9d0a8be20415fb883df88fcea6e63
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral". Positive Example 1 - Input: Topic: Three Gorges Dam Argument: The Three Gorges Dam is a cause of corruption. Output: against Positive Examp...
[ "against" ]
task209-0fa2b206d6294a71a6d08681a11ad1ca
End of preview. Expand in Data Studio

Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task209_stancedetection_classification

Additional Information

Citation Information

The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:

@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
    title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, 
    author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
    year={2022},
    eprint={2204.07705},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2204.07705}, 
}

More details can also be found in the following paper:

@misc{brüelgabrielsson2024compressserveservingthousands,
    title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, 
    author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
    year={2024},
    eprint={2407.00066},
    archivePrefix={arXiv},
    primaryClass={cs.DC},
    url={https://arxiv.org/abs/2407.00066}, 
}

Contact Information

For any comments or questions, please email Rickard Brüel Gabrielsson

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