Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension

Abstract

Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1).

Dynabench: Rethinking Benchmarking in NLP

“…Creating model-fooling examples is not as easy as it used to be, and finding interesting examples is rapidly becoming a less trivial task. In ANLI, the verified model error rate for crowd workers in the later rounds went below 1-in-10 ( , while in "Beat the AI", human performance decreased while time per valid adversarial example went up with stronger models in the loop (Bartolo et al, 2020). For expert linguists, we expect the model error to be much higher, but if the platform actually lives up to its virtuous cycle promise, that error rate will go down quickly.…”

“…Research progress has traditionally been driven by a cyclical process of resource collection and architectural improvements. Similar to Dynabench, recent work seeks to embrace this phenomenon, addressing many of the previously mentioned issues through an iterative human-and-model-in-the-loop annotation process (Yang et al, 2017;Dinan et al, 2019;Chen et al, 2019;Bartolo et al, 2020;, to find "unknown unknowns" (Attenberg et al, 2015) or in a never-ending or life-long learning setting (Silver et al, 2013;Mitchell et al, 2018). The Adversarial NLI (ANLI) dataset , for example, was collected with an adversarial setting over multiple rounds to yield "a 'moving post' dynamic target for NLU systems, rather than a static benchmark that will eventually saturate".…”

“…The QA task takes the same format as SQuAD1.1 (Rajpurkar et al, 2016), i.e., given a context and a question, extract an answer from the context as a continuous span of text. The first round of adversarial QA (AQA) data comes from "Beat the AI" (Bartolo et al, 2020). During annotation, crowd workers were presented with a context sourced from Wikipedia, identical to those in SQuAD1.1, and asked to write a question and select an answer.…”

“…Second, if we rely solely on dynamic adversarial collection, then we increase the risks of creating unnatural datasets. For instance, Bartolo et al (2020) show that training solely on adversarially-collected data for QA was detrimental to performance on non-adversarially collected data. However, they also show that models are capable of simultaneously learning both distributions when trained on the combined data, retaining if not slightly improving performance on the original distribution (of course, this may not hold if we have many more examples of one particular kind).…”

“…Having the model in the loop is obviously useful for evaluation, but it's less clear if the resultant data is necessarily also useful in general for training. So far, there is evidence that adversarially collected data provides performance gains irrespective of the model in the loop Dinan et al, 2019;Bartolo et al, 2020). For example, ANLI shows that replacing equal amounts of "normally collected" SNLI and MNLI training data with ANLI data improves model performance, especially when training size is small , suggesting higher data efficiency.…”