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93956 | Selena Quintanilla-Pérez ( [ seˈlena kintaˈniʝa ˈpeɾes ] or [ seˈlena kintaˈniʎa ˈpeɾeθ ] April 16 , 1971 -- March 31 , 1995 ) was an American singer , songwriter , spokesperson , model , actress , and fashion designer . However , her popularity grew after she won the Tejano Music Award for Female Vocalist of the Year in 1987 , which she won nine consecutive times . | Selena recorded music. | ENTAILMENT | {
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93956 | Selena . Selena signed with EMI Latin in 1989 and released her self-titled debut album the same year , while her brother became her principal music producer and songwriter . One of its singles , `` Como La Flor '' , became one of her most popular signature songs . ( 1993 ) won Best Mexican/American Album at the 1994 Grammy Awards , becoming the first recording by a Tejano artist to do so . In 1994 , Selena released Amor Prohibido , which became one of the best-selling Latin albums in the United States . Her posthumous crossover album , Dreaming of You ( 1995 ) , debuted atop the Billboard 200 , making Selena the first Latin artist to accomplish this feat . | Selena recorded music. | ENTAILMENT | {
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},
{
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{
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},
{
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},
{
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"text": "contestant",
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"text": "Sophie",
"pos": "PROPN",
"lemma": "Sophie",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
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{
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"pos": "PROPN",
"lemma": "Turner",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
},
{
"index": 2,
"text": "was",
"pos": "AUX",
"lemma": "be",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
},
{
"index": 3,
"text": "born",
"pos": "VERB",
"lemma": "bear",
"bnSynsetId": "bn:00083222v",
"wnSynsetOffset": "56930v",
"nltkSynset": "give_birth.v.01"
},
{
"index": 4,
"text": "in",
"pos": "ADP",
"lemma": "in",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
},
{
"index": 5,
"text": "the",
"pos": "DET",
"lemma": "the",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
},
{
"index": 6,
"text": "1990s",
"pos": "NUM",
"lemma": "1990s",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
},
{
"index": 7,
"text": ".",
"pos": "PUNCT",
"lemma": ".",
"bnSynsetId": "O",
"wnSynsetOffset": "O",
"nltkSynset": "O"
}
]
} | {
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"rawText": "Sophie"
},
{
"index": 1,
"rawText": "Turner"
},
{
"index": 2,
"rawText": "("
},
{
"index": 3,
"rawText": "born"
},
{
"index": 4,
"rawText": "21"
},
{
"index": 5,
"rawText": "February"
},
{
"index": 6,
"rawText": "1996"
},
{
"index": 7,
"rawText": ")"
},
{
"index": 8,
"rawText": "is"
},
{
"index": 9,
"rawText": "an"
},
{
"index": 10,
"rawText": "English"
},
{
"index": 11,
"rawText": "actress"
},
{
"index": 12,
"rawText": "."
},
{
"index": 13,
"rawText": "Sophie"
},
{
"index": 14,
"rawText": "Turner"
},
{
"index": 15,
"rawText": "("
},
{
"index": 16,
"rawText": "born"
},
{
"index": 17,
"rawText": "30"
},
{
"index": 18,
"rawText": "April"
},
{
"index": 19,
"rawText": "1984"
},
{
"index": 20,
"rawText": ")"
},
{
"index": 21,
"rawText": "is"
},
{
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"rawText": "an"
},
{
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"rawText": "Australian"
},
{
"index": 24,
"rawText": "model"
},
{
"index": 25,
"rawText": ","
},
{
"index": 26,
"rawText": "reality"
},
{
"index": 27,
"rawText": "television"
},
{
"index": 28,
"rawText": "personality"
},
{
"index": 29,
"rawText": "and"
},
{
"index": 30,
"rawText": "lawyer"
},
{
"index": 31,
"rawText": "who"
},
{
"index": 32,
"rawText": "first"
},
{
"index": 33,
"rawText": "came"
},
{
"index": 34,
"rawText": "to"
},
{
"index": 35,
"rawText": "prominence"
},
{
"index": 36,
"rawText": "as"
},
{
"index": 37,
"rawText": "a"
},
{
"index": 38,
"rawText": "contestant"
},
{
"index": 39,
"rawText": "on"
},
{
"index": 40,
"rawText": "the"
},
{
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"rawText": "Australian"
},
{
"index": 42,
"rawText": "television"
},
{
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"rawText": "series"
},
{
"index": 44,
"rawText": "Search"
},
{
"index": 45,
"rawText": "for"
},
{
"index": 46,
"rawText": "a"
},
{
"index": 47,
"rawText": "Supermodel"
},
{
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"rawText": "."
}
],
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"roles": [
{
"role": "Patient",
"score": 1,
"span": [
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},
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"score": 1,
"span": [
4,
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]
}
]
},
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}
]
}
},
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]
}
]
},
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}
]
}
},
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"verbatlas": {
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"roles": [
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"score": 1,
"span": [
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]
},
{
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"score": 1,
"span": [
22,
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]
}
]
},
"englishPropbank": {
"frameName": "be.01",
"roles": [
{
"role": "ARG1",
"score": 1,
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]
},
{
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"score": 1,
"span": [
22,
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]
}
]
}
},
{
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"verbatlas": {
"frameName": "RESULT_CONSEQUENCE",
"roles": [
{
"role": "Agent",
"score": 1,
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]
},
{
"role": "Time",
"score": 1,
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},
{
"role": "Goal",
"score": 1,
"span": [
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},
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]
}
]
},
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"score": 1,
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"score": 1,
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}
]
},
"hypothesis": {
"tokens": [
{
"index": 0,
"rawText": "Sophie"
},
{
"index": 1,
"rawText": "Turner"
},
{
"index": 2,
"rawText": "was"
},
{
"index": 3,
"rawText": "born"
},
{
"index": 4,
"rawText": "in"
},
{
"index": 5,
"rawText": "the"
},
{
"index": 6,
"rawText": "1990s"
},
{
"index": 7,
"rawText": "."
}
],
"annotations": [
{
"tokenIndex": 3,
"verbatlas": {
"frameName": "GIVE-BIRTH",
"roles": [
{
"role": "Patient",
"score": 1,
"span": [
0,
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]
},
{
"role": "Time",
"score": 1,
"span": [
4,
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]
}
]
},
"englishPropbank": {
"frameName": "bear.02",
"roles": [
{
"role": "ARG1",
"score": 1,
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4,
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]
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}
]
}
} |
Semantically-augmented FEVER for NLI
This dataset is a random downsample of the FEVER dataset adapted for NLI. We downsampled the training and development set to 25% of the original, and recovered labels for the development set from the original FEVER dataset.
The dataset is also augmented with semantic annotations such as Word Sense Disambiguation (WSD) and Semantic Role Labeling (SRL) information.
We annotated the whole downsampled dataset (both premise
and hypothesis
) with AMuSE-WSD (Orlando et al., EMNLP 2021) and InVeRo (Conia et al., EMNLP 2020).
Dataset Creation
The idea was to curate a version of the FEVER dataset adapted to the NLI task for Homework 2 of the Multilingual Natural Language Processing 2024 course at Sapienza University of Rome.
We sourced the data following the instructions in this repo, modifying the labels to the following schema:
{
"id": ..., # the FEVER dataset ID
"premise": ..., # the context in FEVER
"hypothesis": ..., # the query in FEVER
"label": ..., # mapped version of FEVER, where 'supports' -> 'entailment', 'refutes' -> 'contradiction' and 'not enough info' -> 'neutral'
}
We filtered out any sample with an empty premise (a minority of the data) and downsampled both train_fitems.jsonl
and dev_fitems.jsonl
to 25% of their total size.
We then recovered the labels for the development set from the labelled_dev
split in the original FEVER and split the development set into the final dev.jsonl
and test.jsonl
.
Finally, we ran both AMuSE-WSD and InVeRo to augment our samples with WSD and SRL annotations, respectively.
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