Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural language questions, while arguments represent their answers, making the annotations intuitive, semantically rich, and easily attainable by laymen. For example, in Table TABREF4, the question Who cut something captures the traditional “agent” role.Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability. Naturally, employing crowd workers raises challenges when annotating semantic structures like SRL. As BIBREF5 acknowledged, the main shortage of the large-scale 2018 dataset is the lack of recall, estimated by experts to be in the lower 70s.In light of this and other annotation inconsistencies, we propose an improved QA-SRL crowdsourcing protocol for high-quality annotation, allowing for substantially more reliable performance evaluation of QA-SRL parsers. To address worker quality, we systematically screen workers, provide concise yet effective guidelines, and perform a short training procedure, all within a crowd-sourcing platform. To address coverage, we employ two independent workers plus an additional one for consolidation — similar to conventional expert-annotation practices. In addition to yielding 25% more roles, our coverage gain is demonstrated by evaluating against expertly annotated data and comparison with PropBank (Section SECREF4). To foster future research, we release an assessed high-quality gold dataset along with our reproducible protocol and evaluation scheme, and report the performance of the existing parser BIBREF5 as a baseline.In QA-SRL, a role question adheres to a 7-slot template, with slots corresponding to a WH-word, the verb, auxiliaries, argument placeholders (SUBJ, OBJ), and prepositions, where some slots are optional BIBREF4 (see appendix for examples). Such question captures the corresponding semantic role with a natural easily understood expression. The set of all non-overlapping answers for the question is then considered as the set of arguments associated with that role. This broad question-based definition of roles captures traditional cases of syntactically-linked arguments, but also additional semantic arguments clearly implied by the sentence meaning (see example (2) in Table TABREF4).The original 2015 QA-SRL dataset BIBREF4 was annotated by non-expert workers after completing a brief training procedure. They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. In subsequent work, BIBREF5 constructed a large-scale corpus and used it to train a parser. They crowdsourced 133K verbs with 2.0 QA pairs per verb on average. Since crowd-workers had no prior training, quality was established using an additional validation step, where workers had to ascertain the validity of the question, but not of its answers. Instead, the validator provided additional answers, independent of the other annotators. Each verb in the corpus was annotated by a single QA-generating worker and validated by two others.In a reserved part of the corpus (Dense), targeted for parser evaluation, verbs were densely validated with 5 workers, approving questions judged as valid by at least 4/5 validators. Notably, adding validators to the Dense annotation pipeline accounts mostly for precision errors, while role coverage solely relies upon the single generator's set of questions. As both 2015 and 2018 datasets use a single question generator, both struggle with maintaining coverage. Also noteworthy, is that while traditional SRL annotations contain a single authoritative and non-redundant annotation, the 2018 dataset provides the raw annotations of all annotators. These include many overlapping or noisy answers, without settling on consolidation procedures to provide a single gold reference.We found that these characteristics of the dataset impede its utility for future development of parsers.Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 1 out of 3 participants were selected after exhibiting good performance, tested against expert annotations.We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. In this consolidation task, the worker validates questions, merges, splits or modifies answers for the same role according to guidelines, and removes redundant roles by picking the more naturally phrased questions. For example, in Table TABREF4 ex. 1, one worker could have chosen “47 people”, while another chose “the councillor”; in this case the consolidator would include both of those answers. In Section SECREF4, we show that this process yields better coverage. For example annotations, please refer to the appendix.We refine the previous guidelines by emphasizing several semantic features: correctly using modal verbs and negations in the question, and choosing answers that coincide with a single entity (example 1 in Table TABREF4).We annotated a sample taken from the Dense set on Wikinews and Wikipedia domains, each with 1000 sentences, equally divided between development and test. QA generating annotators are paid the same as in fitz2018qasrl, while the consolidator is rewarded 5¢ per verb and 3¢ per question. Per predicate, on average, our cost is 54.2¢, yielding 2.9 roles, compared to reported 2.3 valid roles with an approximated cost of 51¢ per predicate for Dense.Evaluation in QA-SRL involves aligning predicted and ground truth argument spans and evaluating role label equivalence. Since detecting question paraphrases is still an open challenge, we propose both unlabeled and labeled evaluation metrics.Unlabeled Argument Detection (UA) Inspired by the method presented in BIBREF5, arguments are matched using a span matching criterion of intersection over union $\ge 0.5$ . To credit each argument only once, we employ maximal bipartite matching between the two sets of arguments, drawing an edge for each pair that passes the above mentioned criterion. The resulting maximal matching determines the true-positive set, while remaining non-aligned arguments become false-positives or false-negatives.Labeled Argument Detection (LA) All aligned arguments from the previous step are inspected for label equivalence, similar to the joint evaluation reported in BIBREF5. There may be many correct questions for a role. For example, What was given to someone? and What has been given by someone? both refer to the same semantic role but diverge in grammatical tense, voice, and presence of a syntactical object or subject. Aiming to avoid judging non-equivalent roles as equivalent, we propose Strict-Match to be an equivalence on the following template slots: WH, SUBJ, OBJ, as well as on negation, voice, and modality extracted from the question. Final reported numbers on labelled argument detection rates are based on bipartite aligned arguments passing Strict-Match. We later manually estimate the rate of correct equivalences missed by this conservative method.As we will see, our evaluation heuristics, adapted from those in BIBREF5, significantly underestimate agreement between annotations, hence reflecting performance lower bounds. Devising more tight evaluation measures remains a challenge for future research.We extend our metric for evaluating manual or automatic redundant annotations, like the Dense dataset or the parser in BIBREF5, which predicts argument spans independently of each other. To that end, we ignore predicted arguments that match ground-truth but are not selected by the bipartite matching due to redundancy. After connecting unmatched predicted arguments that overlap, we count one false positive for every connected component to avoid penalizing precision too harshly when predictions are redundant.To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Individual worker-vs-worker agreement yields 79.8 F1 over 10 experiments with 150 predicates, indicating high consistency across our annotators, inline with results by other structured semantic annotations (e.g. BIBREF6). Overall consistency of the dataset is assessed by measuring agreement between different consolidated annotations, obtained by disjoint triplets of workers, which achieves F1 of 84.1 over 4 experiments, each with 35 distinct predicates. Notably, consolidation boosts agreement, suggesting it is a necessity for semantic annotation consistency.We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. To construct the expert set, we blindly merged the Dense set with our worker annotations and manually corrected them. We further corrected the evaluation decisions, accounting for some automatic evaluation mistakes introduced by the span-matching and question paraphrasing criteria. As seen in Table TABREF19, our gold set yields comparable precision with significantly higher recall, which is in line with our 25% higher yield.Examining disagreements between our gold and Dense, we observe that our workers successfully produced more roles, both implied and explicit. To a lesser extent, they split more arguments into independent answers, as emphasized by our guidelines, an issue which was left under-specified in the previous annotation guidelines.It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. In Table TABREF22, we replicate the experiments in BIBREF4 for both our gold set and theirs, over a sample of 200 sentences from Wall Street Journal (agreement evaluation is automatic and the metric is somewhat similar to our UA). We report macro-averaged (over predicates) precision and recall for all roles, including core and adjuncts, while considering the PropBank data as the reference set. Our recall of the PropBank roles is notably high, reconfirming the coverage obtained by our annotation protocol.The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not covered in PropBank. To examine this, we analyzed 100 false positive arguments. Only 32 of those were due to wrong or incomplete QA annotations in our gold, while most others were outside of PropBank's scope, capturing either implied arguments or roles not covered in PropBank. Extrapolating from this manual analysis estimates our true precision (on all roles) to be about 91%, which is consistent with the 88% precision figure in Table TABREF19. Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. Overall, the comparison to PropBank reinforces the quality of our gold dataset and shows its better coverage relative to the 2015 dataset.To illustrate the effectiveness of our new gold-standard, we use its Wikinews development set to evaluate the currently available parser from BIBREF5. For each predicate, the parser classifies every span for being an argument, independently of the other spans. Unlike many other SRL systems, this policy often produces outputs with redundant arguments (see appendix for examples). Results for 1200 predicates are reported in Table TABREF23, demonstrating reasonable performance along with substantial room for improvement, especially with respect to coverage. As expected, the parser's recall against our gold is substantially lower than the 84.2 recall reported in BIBREF5 against Dense, due to the limited recall of Dense relative to our gold set.We sample and evaluate 50 predicates to detect correct argument and paraphrase pairs that are skipped by the IOU and Strict-Match criteria. Based on this inspection, the parser completely misses 23% of the 154 roles present in the gold-data, out of which, 17% are implied. While the parser correctly predicts 82% of non-implied roles, it skips half of the implied ones.We introduced a refined crowdsourcing pipeline and a corresponding evaluation methodology for QA-SRL. It enabled us to release a new gold standard for evaluations, notably of much higher coverage of core and implied roles than the previous Dense evaluation dataset. We believe that our annotation methodology and dataset would facilitate future research on natural semantic annotations and QA-SRL parsing.For completeness, we include several examples with some questions restructured into its 7 template slots in Table TABREF26As described in section 3 The consolidator receives two sets of QA annotations and merges them according to the guidelines to produce an exhaustive and consistent QA set. See Table TABREF28 for examples.As mentioned in the paper body, the Fitzgerald et al. parser generates redundant role questions and answers. The first two rows in Table TABREF30 illustrate different, partly redundant, argument spans for the same question. The next two rows illustrate two paraphrased questions for the same role. Generating such redundant output might complicate downstream use of the parser output as well as evaluation methodology.

Q: How much more coverage is in the new dataset?
A: 278 more annotations

Q: How was coverage measured?

Q: How was quality measured?
A: Inter-annotator agreement, comparison against expert annotation, agreement with PropBank Data annotations.

Q: How was the corpus obtained?

Q: How are workers trained?

Q: What is different in the improved annotation protocol?
A: a trained worker consolidates existing annotations

Q: How was the previous dataset annotated?

Q: How big is the dataset?