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0ab56fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | """SemanticQA: A Semantic Reasoning Benchmark for Language Models."""
import json
import os
import datasets
_CITATION = """\
@article{liu2024revisiting,
title={Revisiting a Pain in the Neck: Semantic Phrase Processing Benchmark for Language Models},
author={Liu, Yang and Qin, Melissa Xiaohui and Li, Hongming and Huang, Chao},
journal={arXiv preprint arXiv:2405.02861},
year={2024}
}
"""
_DESCRIPTION = """\
SemanticQA is a comprehensive benchmark for evaluating language models on semantic \
phrase processing tasks, covering idioms, noun compounds, lexical collocations, and \
verbal multiword expressions (VMWEs). It includes 11 core evaluation subsets spanning \
4 phrase types with tasks such as detection, extraction, categorization, interpretation, \
and retrieval.
"""
_HOMEPAGE = "https://github.com/jacklanda/SemanticQA"
_LICENSE = "MIT"
_DATA_DIR = "data"
_CONFIGS = {
"collocate_retrieval": {
"description": "Collocate Retrieval (CR): Given a base word, its lexical function label, and a sentential context with a [MASK] token, retrieve the correct collocate.",
"data_files": {"test": "collocate_retrieval/collocate_retrieval.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"base": datasets.Value("string"),
"collocate": datasets.Value("string"),
"collocation": datasets.Value("string"),
"label": datasets.Value("string"),
"context": datasets.Value("string"),
}),
},
"collocation_categorization": {
"description": "Lexical Collocation Categorization (LCC): Classify a collocation into its lexical function category.",
"data_files": {"test": "collocation_categorization/collocation_categorization.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"base": datasets.Value("string"),
"collocate": datasets.Value("string"),
"collocation": datasets.Value("string"),
"label": datasets.Value("string"),
"label_id": datasets.Value("string"),
"context": datasets.Value("string"),
}),
},
"collocation_extraction": {
"description": "Lexical Collocation Extraction (LCE): Extract the collocation from a given context.",
"data_files": {"test": "collocation_extraction/collocation_extraction.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"keyword": datasets.Value("string"),
"value": datasets.Value("string"),
"collocation": datasets.Value("string"),
"label": datasets.Value("string"),
"label_id": datasets.Value("string"),
"context": datasets.Value("string"),
}),
},
"collocation_paraphrase": {
"description": "Lexical Collocation Interpretation (LCI): Generate paraphrases for a collocation in context.",
"data_files": {"test": "collocation_paraphrase/collocation_paraphrase.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"base": datasets.Value("string"),
"collocate": datasets.Value("string"),
"collocation": datasets.Value("string"),
"label": datasets.Value("string"),
"label_id": datasets.Value("string"),
"context": datasets.Value("string"),
"paraphrases": datasets.Sequence(datasets.Value("string")),
}),
},
"idiom_detection": {
"description": "Idiomatic Expression Detection (IED): Multiple-choice task to identify the meaning of an idiom in context.",
"data_files": {"test": "idiom_detection/idiom_detection.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"idiom": datasets.Value("string"),
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string"),
"target": datasets.Value("string"),
}),
},
"idiom_extraction": {
"description": "Idiomatic Expression Extraction (IEE): Extract the idiomatic expression from a given context.",
"data_files": {"test": "idiom_extraction/idiom_extraction.json"},
"features": datasets.Features({
"context": datasets.Value("string"),
"idiom": datasets.Value("string"),
}),
},
"idiom_paraphrase": {
"description": "Idiomatic Expression Interpretation (IEI): Generate a literal paraphrase of an idiomatic expression in context.",
"data_files": {"test": "idiom_paraphrase/idiom_paraphrase.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"idiom": datasets.Value("string"),
"paraphrase": datasets.Value("string"),
"context_idiomatic": datasets.Value("string"),
"context_literal": datasets.Value("string"),
}),
},
"noun_compound_compositionality": {
"description": "Noun Compound Compositionality (NCC): Multiple-choice task to judge the compositionality level of a noun compound in context.",
"data_files": {"test": "noun_compound_compositionality/noun_compound_compositionality.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"noun_compound": datasets.Value("string"),
"context": datasets.Value("string"),
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string"),
"target": datasets.Value("string"),
}),
},
"noun_compound_compositionality_ft": {
"description": "Noun Compound Compositionality fine-tuning splits (NCC-FT): Train/test/validation data for fine-tuning compositionality classifiers.",
"data_files": {
"train": "noun_compound_compositionality/noun_compound_compositionality_ft_train.json",
"test": "noun_compound_compositionality/noun_compound_compositionality_ft_test.json",
"validation": "noun_compound_compositionality/noun_compound_compositionality_ft_valid.json",
},
"features": datasets.Features({
"text": datasets.Value("string"),
"options": datasets.Value("string"),
"answer": datasets.Value("string"),
}),
},
"noun_compound_extraction": {
"description": "Noun Compound Extraction (NCE): Extract the noun compound from a given context.",
"data_files": {"test": "noun_compound_extraction/noun_compound_extraction.json"},
"features": datasets.Features({
"context": datasets.Value("string"),
"start_index": datasets.Value("string"),
"end_index": datasets.Value("string"),
"noun_compound": datasets.Value("string"),
"interpretation": datasets.Value("string"),
}),
},
"noun_compound_interpretation": {
"description": "Noun Compound Interpretation (NCI): Generate free-form interpretations of a noun compound.",
"data_files": {"test": "noun_compound_interpretation/noun_compound_interpretation.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"noun_compound": datasets.Value("string"),
"references": datasets.Sequence(datasets.Value("string")),
}),
},
"verbal_mwe_extraction": {
"description": "Verbal Multiword Expression Extraction (VMWE): Extract the verbal MWE from a given context and identify its type (VPC, LVC, VID).",
"data_files": {"test": "verbal_mwe_extraction/verbal_mwe_extraction.json"},
"features": datasets.Features({
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"vmwe": datasets.Value("string"),
"label": datasets.Value("string"),
}),
},
}
class SemanticQA(datasets.GeneratorBasedBuilder):
"""SemanticQA benchmark dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=name, description=cfg["description"])
for name, cfg in _CONFIGS.items()
]
DEFAULT_CONFIG_NAME = "idiom_detection"
def _info(self):
cfg = _CONFIGS[self.config.name]
return datasets.DatasetInfo(
description=cfg["description"],
features=cfg["features"],
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
cfg = _CONFIGS[self.config.name]
urls = {
split: os.path.join(_DATA_DIR, path)
for split, path in cfg["data_files"].items()
}
downloaded = dl_manager.download_and_extract(urls)
split_map = {
"test": datasets.Split.TEST,
"train": datasets.Split.TRAIN,
"validation": datasets.Split.VALIDATION,
}
return [
datasets.SplitGenerator(
name=split_map[split_key],
gen_kwargs={"filepath": path},
)
for split_key, path in downloaded.items()
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for idx, row in enumerate(data):
yield idx, row
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