region:us
Question-Answer Meaning Representations (QAMR) are a new paradigm for representing predicate-argument structure, which makes use of free-form questions and their answers in order to represent a wide range of semantic phenomena. 
The semantic expressivity of QAMR compares to (and in some cases exceeds) that of existing formalisms, while the representations can be annotated by non-experts (in particular, using crowdsourcing).
Formal Notes: 
* The `answer_ranges` feature here has a different meaning from that of the `qanom` and `qa_srl` datasets, although both are structured the same way; 
while in qasrl/qanom, each "answer range" (i.e. each span, represented as [begin-idx, end-idx]) stands for an independant answer which is read separately 
(e.g., "John Vincen", "head of marketing"), in this `qamr` dataset each question has a single answer who might be conposed of non-consecutive spans; 
 that is, all given spans should be read successively.
* Another difference is that the meaning of `predicate` in QAMR is different and softer than in QASRL/QANom - here, the predicate is not necessarily within the question, 
it can also be in the answer; it is generally what the annotator marked as the focus of the QA.
@inproceedings{michael-etal-2018-crowdsourcing,
    title = "Crowdsourcing Question-Answer Meaning Representations",
    author = "Michael, Julian  and
      Stanovsky, Gabriel  and
      He, Luheng  and
      Dagan, Ido  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-2089",
    doi = "10.18653/v1/N18-2089",
    pages = "560--568",
    abstract = "We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available QAMR data and annotation scheme should support significant future work.",
}
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