"""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