wikiHow is a resource of how-to guides that describe the steps necessary to accomplish a goal. Guides in this resource are regularly edited by a community of users, who try to improve instructions in terms of style, clarity and correctness. In this work, we test whether the need for such edits can be predicted automatically. For this task, we extend an existing resource of textual edits with a complementary set of approx. 4 million sentences that remain unedited over time and report on the outcome of two revision modeling experiments.

We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in English-language instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. 1 The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.

The usage of (co-)referring expressions in discourse contributes to the coherence of a text. However, text comprehension can be difficult when referring expressions are non-verbalized and have to be resolved in the discourse context. In this paper, we propose a novel dataset of such implicit references, which we automatically derive from insertions of references in collaboratively edited how-to guides. Our dataset consists of 6,014 instances, making it one of the largest datasets of implicit references and a useful starting point to investigate misunderstandings caused by underspecified language. We test different methods for resolving implicit references in our dataset based on the Generative Pre-trained Transformer model (GPT) and compare them to heuristic baselines. Our experiments indicate that GPT can accurately resolve the majority of implicit references in our data. Finally, we investigate remaining errors and examine human preferences regarding different resolutions of an implicit reference given the discourse context.

This paper describes the data, task setup, and results of the shared task at the First Workshop on Understanding Implicit and Underspecified Language (UnImplicit). The task requires computational models to predict whether a sentence contains aspects of meaning that are contextually unspecified and thus require clarification. Two teams participated and the best scoring system achieved an accuracy of 68%.

In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis. Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement. We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that 'revised means better' actually holds. The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy. Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70%, when taking context into account. In a human annotation study, we observe that annotators identify the revised sentence as the 'better version' with similar performance. Our analysis reveals a fair agreement among annotators when a revision improves fluency. In contrast, noun substitutions that involve common lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements. While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.