| |
|
|
| """ |
| Run this script as ./conversion_script.py to convert SemEval-2020 Task 5 |
| ("Modelling Causal Reasoning in Language: Detecting Counterfactuals") |
| into HF-compatible parquet files. |
| |
| Citation / original source |
| --------------------------- |
| Yang, X., Obadinma, S., Zhao, H., Zhang, Q., Matwin, S., & Zhu, X. (2020). |
| "SemEval-2020 Task 5: Counterfactual Recognition." Proceedings of the |
| 14th Workshop on Semantic Evaluation (SemEval-2020), pages 322-335. |
| https://aclanthology.org/2020.semeval-1.40/ |
| Repo (verified live, public, no login, real rows fetched and offsets |
| independently re-verified before trusting it): |
| github.com/arielsho/SemEval-2020-Task-5. No LICENSE file (verified: 404) |
| -- the README only asks for a citation, so usage here is citation-gated/ |
| academic-use-implied rather than under an explicit open license; flagged, |
| not resolved. This repo is a POST-competition release with real gold |
| labels for both splits (verified: `subtask1_test.csv`'s ``gold_label`` |
| column and `subtask2_test.csv`'s span columns are both populated with |
| real values, not blind/withheld) -- unlike the original CodaLab |
| competition, which would have hidden test labels during the shared task. |
| |
| Subtask 1 (``Subtask-1/subtask1_{train,test}.csv``, columns |
| ``sentenceID,gold_label,sentence``) is used for causality-detection: |
| 13000 train + 7000 test = 20000 sentences, 1454+738 = 2192 causal |
| (gold_label=1) / 17808 noncausal -- verified directly, an EXACT match to |
| Hagen et al. 2026 (arXiv:2510.08224) Table 2's citation of this dataset |
| (2192 causal + 17808 noncausal), confirming train+test combined (not |
| train alone, which is only 13000) is the intended full dataset size. |
| |
| Subtask 2 (``Subtask-2/subtask2_{train,test}.csv``, columns |
| ``sentenceID,sentence,antecedent,consequent,antecedent_startid, |
| antecedent_endid,consequent_startid,consequent_endid``) is used for |
| causal-candidate-extraction/causality-identification: 3551 train + 1950 |
| test = 5501 sentences. This is a DIFFERENT, disjoint sentence set from |
| Subtask 1 (sentenceIDs 200000+ vs. 100000+) -- it only covers the |
| causal/counterfactual sentences re-collected for span annotation, not |
| all 20000 Subtask-1 sentences; same situation as several other datasets |
| here (e.g. FinCausal 2020) where detection and extraction/identification |
| draw from different upstream tables. |
| ``antecedent``/``consequent`` are the hypothetical condition and |
| hypothetical result of a counterfactual statement respectively -- mapped |
| onto this project's Cause/Effect roles (antecedent=cause, |
| consequent=effect), matching the paper's own "antecedents and consequent |
| [are connected] with causal relations" framing. |
| Offsets: ``antecedent_startid``/``antecedent_endid`` are INCLUSIVE on |
| both ends (need ``sentence[start:end+1]``) -- verified: 0/5501 mismatches |
| against the ``antecedent`` column with this convention. ``consequent`` |
| offsets use the SAME inclusive convention, EXCEPT 788/5501 (14.3%) rows |
| have ``consequent == "{}"`` with sentinel offsets ``(-1, -1)`` -- a |
| literal "no consequent span annotated" marker (verified: every "{}" |
| row has exactly (-1,-1), and every non-"{}" row's offsets are exact) -- |
| not a parsing bug, a genuine share of counterfactuals with only an |
| antecedent span and no separate consequent span. Such rows contribute |
| only the antecedent entity (no relation) to causality-identification -- |
| matching this project's "still include the entity, just no relation" |
| convention used for BioCause's Effect-only events. |
| """ |
|
|
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| from causalatee.data.constants import ClassLabel, Relation, Task |
| from causalatee.data.utils import insert_entity_markers, verify_dataset |
|
|
| _BASE_URL = "https://raw.githubusercontent.com/arielsho/SemEval-2020-Task-5/master" |
| _DETECTION_FILES = {"train": "Subtask-1/subtask1_train.csv", "test": "Subtask-1/subtask1_test.csv"} |
| _SPAN_FILES = {"train": "Subtask-2/subtask2_train.csv", "test": "Subtask-2/subtask2_test.csv"} |
|
|
|
|
| def convert_for_causality_detection(split: str) -> None: |
| df = pd.read_csv(f"{_BASE_URL}/{_DETECTION_FILES[split]}") |
| rows = [ |
| {"index": f"semeval2020t5_{split}_{r.sentenceID}", "text": r.sentence, |
| "label": ClassLabel.Causal if r.gold_label else ClassLabel.Uncausal} |
| for r in df.itertuples() |
| ] |
| df = pd.DataFrame(rows).set_index("index") |
| for error in verify_dataset(df, Task.CausalityDetection): |
| print(f"WARNING [SemEval2020T5 causality detection/{split}]: {error}") |
| df.to_parquet(f"./causality-detection/{split}.parquet", engine="pyarrow") |
|
|
|
|
| def _spans(df_row) -> tuple[tuple[int, int], tuple[int, int] | None]: |
| """(antecedent_span, consequent_span_or_None), inclusive-end offsets resolved.""" |
| ante = (int(df_row.antecedent_startid), int(df_row.antecedent_endid) + 1) |
| if df_row.consequent == "{}": |
| return ante, None |
| conseq = (int(df_row.consequent_startid), int(df_row.consequent_endid) + 1) |
| return ante, conseq |
|
|
|
|
| def convert_for_causal_candidate_extraction(split: str) -> None: |
| df = pd.read_csv(f"{_BASE_URL}/{_SPAN_FILES[split]}") |
| out = [] |
| for r in df.itertuples(): |
| ante, conseq = _spans(r) |
| entity = sorted({ante, conseq} if conseq else {ante}) |
| out.append({"index": f"semeval2020t5_{split}_{r.sentenceID}", "text": r.sentence, |
| "entity": [list(s) for s in entity]}) |
| df = pd.DataFrame(out).set_index("index") |
| for error in verify_dataset(df, Task.CausalCandidateExtraction): |
| print(f"WARNING [SemEval2020T5 causal candidate extraction/{split}]: {error}") |
| df.to_parquet( |
| f"./causal-candidate-extraction/{split}.parquet", engine="pyarrow" |
| ) |
|
|
|
|
| def convert_for_causality_identification(split: str) -> None: |
| df = pd.read_csv(f"{_BASE_URL}/{_SPAN_FILES[split]}") |
| out = [] |
| for r in df.itertuples(): |
| ante, conseq = _spans(r) |
| segments = {"e1": [ante]} |
| relations = [] |
| if conseq: |
| segments["e2"] = [conseq] |
| relations.append({"relationship": Relation.Procausal, "first": "e1", "second": "e2"}) |
| marked_text = insert_entity_markers(r.sentence, segments) |
| out.append({"index": f"semeval2020t5_{split}_{r.sentenceID}", "text": marked_text, "relations": relations}) |
| df = pd.DataFrame(out).set_index("index") |
| for error in verify_dataset(df, Task.CausalityIdentification): |
| print(f"WARNING [SemEval2020T5 causality identification/{split}]: {error}") |
| df.to_parquet( |
| f"./causality-identification/{split}.parquet", engine="pyarrow" |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| for split in ["train", "test"]: |
| convert_for_causality_detection(split) |
| convert_for_causal_candidate_extraction(split) |
| convert_for_causality_identification(split) |
|
|
