SlowGuess/NAACL_2022_OCR huggingface dataset:
In summary, these results show that we can encourage annotators to come up with more challenging examples without requiring any adversarially-collected data or an adversarial model in the loop, simply through the use of GAAs paired with an appropriate sampling strategy. However, using adversarial data collection still provides substantially better downstream performance. These observations are in line with our initial hypothesis that sampling generated prompts from regions of known model uncertainty, or prompts that we know the model finds challenging to answer, irrespective of generated sample quality, provides annotators with a better starting point for example creation.
4.3 Improving Adversarial Data Collection
Following the efficiency gains observed for standard data collection, we investigate whether it is possible for GAAs to provide further improvements over adversarial data collection. As for the previous experiments, we investigate GAAs trained on three different datasets: SQuAD1.1, AdversarialQA, and the combination of both. We combine each of these with the three previously discussed sampling strategies resulting in nine different experimental settings. Results are shown in Table 3.
We find that when annotators are incentivised to try to beat an adversarial QA model-in-the-loop, the previously seen efficiency gains are not as clear cut. In fact, annotators are slightly slower than for the adversarial data collection baseline when using a SQuAD-trained GAA. When using a GAA that has been trained on adversially-sourced questions, standard likelihood sampling provides efficiency gains over the baseline, however, both adversarial and uncertainty sampling (which naturally
GAA Training	Sampling	t (s)	vMER (%)	t/vMFE (s)	SQuADdev	DBiDAF	DBERT	DRoBERTa	MRQA
SQuAD	Likelihood	61.131.9	2.25	3,501	86.5	50.1	30.1	24.1	57.6
SQuAD	Adversarial	62.622.7	4.66	1,750	83.2	48.1	27.7	24.2	55.0
SQuAD	Uncertainty	62.126.3	2.41	3,317	86.1	51.8	31.1	24.4	58.4
AdversarialQA	Likelihood	54.224.4	3.09	2,458	84.7	49.8	36.9	29.8	56.8
AdversarialQA	Adversarial	63.925.6	6.30	1,262	83.3	49.4	34.9	28.1	56.7
AdversarialQA	Uncertainty	72.130.9	5.20	1,776	83.6	50.3	37.3	27.0	55.7
Combined	Likelihood	53.625.6	2.64	2,724	85.1	48.9	33.4	24.7	56.5
Combined	Adversarial	69.629.6	4.86	1,922	82.9	48.0	33.6	28.6	54.5
Combined	Uncertainty	62.025.0	4.87	1,690	85.3	50.5	33.9	28.8	56.5
Table 3: Results for the investigation into supporting adversarial data collection using GAAs. We investigate three different GAA training dataset sources, and three sampling strategies. The adversarial QA model used in the annotation loop is identical for all settings.
GAA Training	Sampling	t (s)	vMER (%)	t/vMFE (s)	SQuADdev	DBiDAF	DBERT	DRoBERTa	MRQA
AdversarialQA	Likelihood	38.522.7	5.63	988	83.8	49.7	40.3	30.7	55.9
AdversarialQA	Adversarial	44.221.6	9.46	668	83.9	48.7	36.2	30.3	55.2
AdversarialQA	Uncertainty	49.924.8	7.80	854	84.8	51.3	38.9	30.6	56.3
Combined	Likelihood	45.923.4	2.90	2,196	85.2	51.1	37.5	28.1	56.4
Combined	Adversarial	56.327.1	9.53	785	83.5	48.4	35.5	29.3	55.5
Combined	Uncertainty	57.627.7	4.48	1,841	83.4	47.4	36.6	27.8	55.2
Table 4: Results for the investigation into supporting adversarial data collection using GAAs equipped with answer prompting. We investigate two different GAA training dataset sources, and three sampling strategies. The adversarial QA model-in-the-loop is identical for all settings.
lead to more complex prompts that might be more challenging to work with) actually slow annotators down, although they do provide improved validated model error rates and overall better adversarial example generation efficiency measured by the time taken per validated model-fooling example.
In terms of downstream performance, there is no clear best option, but the best settings consistently outperform the adversarial data collection baseline on the most challenging examples  $(\mathcal{D}_{\mathrm{BERT}}$  and  $\mathcal{D}_{\mathrm{RoBERTa}})$  while providing comparable results in the other evaluation settings. Surprisingly, we find that various settings, particularly those involving a SQuAD-trained GAA can provide performance gains over the standard data collection baseline on SQuAD1.1. We also observe that a SQuAD-trained GAA with uncertainty sampling gives best performance on the less challenging evaluation sets, while an AdversarialQA-trained GAA gives best performance on the evaluation datasets collected using a more performant adversary. This is also in line with the observations made by Bartolo et al. (2020) showing a distributional shift in question type and complexity with an increasingly stronger model-in-the-loop.
The general takeaway in terms of the ideal experimental setting from the perspective of down
stream performance is that it depends on the particular evaluation setting, with GAAs trained on examples from a particular setting yielding better performance when the downstream model is also evaluated in similar conditions. Another key observation is that both the validated model error rate and time per validated model-fooling example comfortably outperform the baselines across the board, highlighting the enhancements to the effectiveness of the annotation process provided by incorporating GAAs in the loop.
4.4 Investigating Answer Prompting
The settings explored in the previous sections focus on investigating the effects of assisting free-text generation of the questions using GAAs. However, the QA crowdsourcing setting also involves annotation of answer spans, which we also explore in the search for efficiency gains. Here, we explore GAAs trained on datasets with adversarially-sourced components and the same three sampling strategies as previously (likelihood, uncertainty and adversarial), while additionally providing annotators with an answer suggestion.
In essence, this is similar to an answer and question validation setting, with the difference that annotators have the ability to freely modify both an
swer and question, or request additional suggestions. Results for our experiments involving answer assistance are shown in Table 4.
We find that answer prompting is very effective at improving annotation efficiency, providing gains in all six experimental settings while also providing improved vMER results in all cases. We also see very similar downstream performance result patterns to the previous set of experiments - for performance on the more challenging evaluation sets  $(\mathcal{D}_{\mathrm{BERT}}$  and  $\mathcal{D}_{\mathrm{RoBERTa}})$ , an AdversarialQA-trained GAA with likelihood sampling gives best performance, while for performance on SQuAD, a GAA trained on examples including SQuAD gives the best results. As previously discussed and as shown in Appendix B, adding SQuAD examples to the training data mitigates this effect.
The consistency in performance patterns serves to further highlight the previous observation that, while using GAAs provides considerable gains in both the efficiency of the annotation process and effectiveness in terms of downstream results, the ideal annotation setup should be selected based on the target downstream evaluation. It is also worth highlighting the considerable performance improvements on the more challenging AdversarialQA evaluation sets observed when using an AdversarialQA-trained GAA even over adversarial data collection.
5 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 Table 5, indicate that annotators query the GAA less frequently when being shown simpler prompts i.e. those obtained using a GAA trained on non-adversarily sourced examples, or selected using likelihood sampling which tends to provide higher quality and less complex generated texts. We also find that annotators query the GAA more frequently when an answer prompt is also provided. We believe that this can be attributed to the fact that the answer and question prompt setting is more similar to a validation work
Feature	Setting	Avg. #Generations per Example
GAA Training	SQuAD	0.69
AdversarialQA	0.86
Combined	0.83
Sampling	Likelihood	0.67
Adversarial	0.87
Uncertainty	0.83
Answer Prompt?	×	0.73
✓	0.91
Table 5: Results showing how often annotators query the GAA for different experimental settings.
flow, allowing annotators to generate prompts until a satisfactory one is found.
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 likelihood-sampled 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  $\mathrm{SQuAD}_{\mathrm{dev}}$  by up to  $0.8\mathrm{F}_1$  and improve over adversarial data collection by up to  $6.1\mathrm{F}_1$  on  $\mathcal{D}_{\mathrm{BERT}}$ , and  $3.8\mathrm{F}_1$  on  $\mathcal{D}_{\mathrm{RoBERTa}}$ . 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.
We collect a training datasets as a part of the analysis in this work. The passages are sourced from Wikipedia through KILT. As described in the main text, our incentive structure is designed to ensure that crowdworkers were fairly compensated. Our datasets focus on the English language. As this data is not collected for the purpose of designing NLP applications, we do not foresee any risks associated with the use of this data.
The authors would like to thank the Dynabench team for their feedback and continuous support.
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019. Synthetic QA corpora generation with roundtrip consistency. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6168-6173, Florence, Italy. Association for Computational Linguistics.
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020. Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension. Transactions of the Association for Computational Linguistics, 8:662-678.
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021. Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation.
Samuel R Bowman and George E Dahl. 2021. What will it take to fix benchmarking in natural language understanding? arXiv preprint arXiv:2104.02145.
Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, and Emily Pitler. 2020. New protocols and negative results for textual entailment data collection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8203-8214, Online. Association for Computational Linguistics.
Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. A thorough examination of the CNN/Daily Mail reading comprehension task. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2358-2367, Berlin, Germany. Association for Computational Linguistics.
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. Electra: Pretraining text encoders as discriminators rather than generators. In International Conference on Learning Representations.
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston. 2019. Build it break it fix it for dialogue safety: Robustness from adversarial human attack. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4537-4546, Hong Kong, China. Association for Computational Linguistics.
Xinya Du and Claire Cardie. 2018. Harvesting paragraph-level question-answer pairs from Wikipedia. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1907-1917, Melbourne, Australia. Association for Computational Linguistics.
Xinya Du, Junru Shao, and Claire Cardie. 2017. Learning to ask: Neural question generation for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1342-1352, Vancouver, Canada. Association for Computational Linguistics.
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019. DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2368-2378, Minneapolis, Minnesota. Association for Computational Linguistics.
Allyson Ettinger, Sudha Rao, Hal Daumé III, and Emily M. Bender. 2017. Towards linguistically generalizable NLP systems: A workshop and shared task. CoRR, abs/1711.01505.
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019. MRQA 2019 shared task: Evaluating generalization in reading comprehension. In Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP.
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019. Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1161-1166, Hong Kong, China. Association for Computational Linguistics.
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 107-112, New Orleans, Louisiana. Association for Computational Linguistics.
Robin Jia and Percy Liang. 2017. Adversarial examples for evaluating reading comprehension systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2021-2031, Copenhagen, Denmark. Association for Computational Linguistics.
Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, and Wen-tau Yih. 2021. On the efficacy of adversarial data collection for question answering: Results from a large-scale randomized study. In Proceedings of the 59th Annual Meeting of the Association
for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6618-6633, Online. Association for Computational Linguistics.
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams. 2021. Dynabench: Rethinking benchmarking in NLP. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4110-4124, Online. Association for Computational Linguistics.
David D. Lewis and William A. Gale. 1994. A sequential algorithm for training text classifiers. In SIGIR, pages 3-12. ACM/Springer.
Mike Lewis and Angela Fan. 2019. Generative question answering: Learning to answer the whole question. In International Conference on Learning Representations.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online. Association for Computational Linguistics.
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Kuttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021. PAQ: 65 million probably-asked questions and what you can do with them. arXiv preprint arXiv:2102.07033.
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3428-3448, Florence, Italy. Association for Computational Linguistics.
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Adversarial NLI: A new benchmark for natural language understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4885-4901, Online. Association for Computational Linguistics.
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings of NAACL-HLT 2019: Demonstrations.
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Roktaschel, and Sebastian Riedel. 2021. KILT: a benchmark for knowledge intensive language tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2523-2544, Online. Association for Computational Linguistics.
Jason Phang, Angelica Chen, William Huang, and Samuel R Bowman. 2021. Adversarily constructed evaluation sets are more challenging, but may not be fair. arXiv preprint arXiv:2111.08181.
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021. DynaSent: A dynamic benchmark for sentiment analysis. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2388-2404, Online. Association for Computational Linguistics.
Raul Puri, Ryan Spring, Mohammad Shoeybi, Mostofa Patwary, and Bryan Catanzaro. 2020. Training question answering models from synthetic data. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5811-5826, Online. Association for Computational Linguistics.
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics.
Alexis Ross, Tongshuang Wu, Hao Peng, Matthew E. Peters, and Matt Gardner. 2021. Tailor: Generating and perturbing text with semantic controls. arXiv preprint arXiv:2107.07150.
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017. The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 15-25, Vancouver, Canada. Association for Computational Linguistics.
Burr Settles. 2009. Active learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences.
Yoshimasa Tsuruoka, Jun'ichi Tsujii, and Sophia Ananiadou. 2008. Accelerating the annotation of sparse named entities by dynamic sentence selection. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, pages 30-37, Columbus, Ohio. Association for Computational Linguistics.