biu-nlp/Multilingual_QASem_Datasets

🧠 Multilingual QASem Dataset

A multilingual dataset for QA-based Semantic Parsing (QASem) covering Hebrew, Russian, and French.
It includes automatically projected training data and manually validated gold data (dev + test) for evaluating cross-lingual QASem parsers.

It is the dataset from the paper: Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages (Davidov et al., EACL 2026)

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📘 Overview

This dataset provides QA-based Semantic Representations (QASem) in three typologically diverse languages.
Each instance corresponds to a predicate and its associated question–answer pairs, representing the underlying semantic roles in natural language.

It is intended for training and evaluating multilingual QASem parsers, and for studying cross-lingual projection, predicate preservation, and semantic role alignment.

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📂 Dataset Structure

Column	Description
sent_id	Sentence identifier
predicate	Predicate in the target language
predicate_idx	Token index of the predicate
question	Generated or annotated question
answer	Corresponding answer span
type	Predicate type (VERB/NOM)
source_data	Original UD source or split name

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🔧 Splits

• train — automatically projected QASem data
• gold — manually validated dev+test data

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📜 License

This dataset is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
See license details.

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Cite

@inproceedings{davidov-etal-2026-effective,
    title = "Effective {QA}-Driven Annotation of Predicate{--}Argument Relations Across Languages",
    author = "Davidov, Jonathan  and
      Slobodkin, Aviv  and
      Klein, Shmuel Tomi  and
      Tsarfaty, Reut  and
      Dagan, Ido  and
      Klein, Ayal",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-long.112/",
    doi = "10.18653/v1/2026.eacl-long.112",
    pages = "2484--2502",
    ISBN = "979-8-89176-380-7",
    abstract = "Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such semantic structures requires costly annotation efforts and has remained largely confined to English. We leverage the Question-Answer driven Semantic Role Labeling (QA-SRL) framework {---} a natural-language formulation of predicate-argument relations {---} as the foundation for extending semantic annotation to new languages. To this end, we introduce a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. Applied to Hebrew, Russian, and French {---} spanning diverse language families {---} the method yields high-quality training data and fine-tuned, language-specific parsers that outperform strong multilingual LLM baselines (GPT-4o, LLaMA-Maverick). By leveraging QA-SRL as a transferable natural-language interface for semantics, our approach enables efficient and broadly accessible predicate-argument parsing across languages."
}