Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants (2022)

Abstract: In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected training data have been shown to be more robust in adversarial and out-of-domain settings, and are considerably harder for humans to fool. However, DADC is more time-consuming than traditional data collection and thus more costly per annotated example. In this work, we examine whether we can maintain the advantages of DADC, without incurring the additional cost. To that end, we introduce Generative Annotation Assistants (GAAs), generator-in-the-loop models that provide real-time suggestions that annotators can either approve, modify, or reject entirely. We collect training datasets in twenty experimental settings and perform a detailed analysis of this approach for the task of extractive question answering (QA) for both standard and adversarial data collection. We demonstrate that GAAs provide significant efficiency benefits with over a 30% annotation speed-up, while leading to over a 5x improvement in model fooling rates. In addition, we find that using GAA-assisted training data leads to higher downstream model performance on a variety of question answering tasks over adversarial data collection.

## introduction natural language processing has become increasingly reliant on large datasets obtained using crowd sourcing. however, crowdsourcing as an unconstrained annotation approach is known to result in machine-exploitable annotator artefacts @xcite @xcite , leading to poor outof-distribution generalisation @xcite @xcite . dynamic adversarial data collection (dadc) aims to address these issues a: zdeno chara gaa qa figure 1 : example interaction between an annotator and the models in the loop. the annotator selects an answer from the passage, for which the generative annotation assistant (gaa) prompts a question. the annotator can then freely modify the question and/or answer, or generate another prompt. in the adversarial data collection setting, a model-in-the-loop provides predictions with the aim of encouraging annotators to find model-fooling examples. in the answer prompting setting, an answer suggestion is prompted by the assistive model instead of being selected by the annotator. by introducing state-of-the-art models into the data collection loop and asking human annotators to produce examples that these models find challenging @xcite . the intuition behind this approach is that it leads human annotators to better explore the space of possible examples. previous work has found that dadc leads to improved model robustness on adversarial datasets @xcite , increased sample diversity @xcite , better training data @xcite and better domain generalisation @xcite . despite these advantages, a downside to dadc is that it increases the human effort necessary to annotate a single example and thus the overall annotation cost. in fact, to date, only a limited number of large-scale training datasets have been produced using dadc and its application has been primarily restricted to producing challenge sets or as additional training data to improve the performance of models already trained on non-dadc curated datasets. to make better use of dadc data, @xcite propose generating synthetic adversarial training sets to further improve model robustness. however, this approach inevitably limits example diversity as it relies on examples ultimately generated by a model with no additional human input, and provides no guarantees that useful synthetic examples would transfer across target adversary models of varying capabilities or across annotation rounds. in this work, we propose assisting annotators by having generative models aid human annotators in the data collection loop. concretely, we utilise a generative annotation assistant (gaa) model that provides prompt suggestions to crowdworkers, while allowing full flexibility for edits and rewrites to support example generation while still allowing for human creativity as shown in figure 1 . we explore gaas in a broad range of experimental settings, including standard and adversarial data collection approaches, training on various source datasets, and employing sampling methodologies based on likelihood, adversarial feedback, and uncertainty. we showcase the value of this approach on the task of extractive question answering (qa), and find that gaas can help improve both the standard and adversarial data collection paradigms. we find considerable efficiency gains, with over 30% observed annotation speed-ups, as well as improved data effectiveness with up to a 6.1f 1 improvement in downstream performance over adversarial data collection. ## experimental setup our study focuses on the effects of incorporating generative annotation assistants, and understanding their interactions with annotators and discriminative models-in-the-loop in a dadc context for qa. we provide crowdworkers with a short passage from wikipedia and ask them to write five questions and highlight the span in the passage that best answers the question for each (see figure 2 ). we pay workers equally across experiment modes to avoid creating an incentive imbalance and pay out an additional bonus for each question that successfully beats the discriminative qa model i.e., for each question that the model fails to answer correctly. finally, we validate all collected examples using a separate worker pool that also undergoes rigorous onboarding and validation. we ask three of these additional workers to report on the validity of each annotated example. selected passages we select passages from kilt @xcite to allow for the possibility of future investigation into cross-domain and task transfer. we restrict kilt passages to those with between 100 and 600 tokens that are used by at least 5 of the kilt tasks. furthermore, we filter out any passages with any 8-gram overlap (after normalisation) to the squad1.1 training or development sets, seeking to ensure that all passages used in our study are novel and previously unseen by the discriminative qa models in the loop. this leaves a total of 10,109 passages from 421 wikipedia pages. we retain and supply all passage-relevant kilt metadata (such as ids and provenances) with our collected datasets to facilitate future work. ## results our study allows us to perform a thorough investigation into both the efficiency and effectiveness of the different data annotation methodologies. it also allows us to build on work investigating the various differences between standard and adversarial data collection @xcite . ## annotator interaction with gaas while we provide annotators with instructions explaining how they can use the gaas to aid their annotation, they are free to query the generative models as many times as they like, if at all, during annotation. we are interested to see how the three main factors affecting interaction with the gaas that we explore -training data, sampling strategy, and answer prompting -affect the ways in which annotators interact or use the gaas. results, shown in ## discussion and conclusion in this work, we introduce generative annotation assistants (gaas) and investigate their potential to aid crowdworkers with creating more effective training data more efficiently. we perform a thorough analysis of how gaas can be used for improving qa dataset annotation in different settings, including different generative model training data, sampling strategies, and whether to also provide annotators with answer suggestions. we find that gaas are beneficial in both the standard and adversarial data collection settings. in the standard data collection setting, and under the assumption of no access to adversarially-collected data, gaas with prompts sampled based on likelihood provide annotation speed-ups, while prompts sampled by adversarial performance or uncertainty metrics provide benefits to both the model error rates on the collected data as well as subsequent downstream qa performance. we find that while gaas are effective for improving standard data collection, we still do not approach the performance obtained when using adversarial data collection. for adversarial data collection, we demonstrate improved effectiveness of the annotation process over the non-gaa baseline, although this comes at a cost of reduced annotation efficiency. we show that also aiding annotators with answer prompts boosts data collection efficiency even beyond that of standard data collection, while retaining overall downstream performance. we find that the ideal annotation setting differs for different intended evaluations, with an uncertainty-sampled gaa trained on data that was not adversarially-collected providing best perfor-mance on simpler questions, while a gaa trained on adversarially-collected data provides best downstream performance on more challenging evaluation sets. however, we also find that combining with a small sample of squad training examples can boost performance on these less-challenging questions, and that in this setting a likelihoodsampled adversarially-trained gaa consistently provides the best results. in terms of efficiency, we see annotation speedups over baseline of 34.4% for standard data collection and 37.4% for adversarial data collection. in terms of effectiveness, we see over a 5x improvement in vmer over adversarial data collection, along with downstream performance gains. we improve over standard data collection on squad dev by up to 0.8f 1 and improve over adversarial data collection by up to 6.1f 1 on d bert , and 3.8f 1 on d roberta . furthermore, we see benefits in domain generalisation over standard data collection, and show that annotators interact with the gaa more frequently when it has been trained on adversarially-collected data, is sampled based on adversarial or uncertainty feedback, and also provides answer prompts. while our analysis is limited by the size of the collected data, we believe that gaas can help drive further innovation into improved data collection methodologies based on these findings. we hope that our analysis of various aspects of gaa incorporation into the annotation pipeline and the interactions between annotators and multiple models in the loop can help inform future work exploring broader aspects of gaa use, such as for other nlp tasks or for larger scale annotation efforts.