Barriers and enabling factors for error analysis in NLG research (2023)

Abstract: Earlier research has shown that few studies in Natural Language Generation (NLG) evaluate their system outputs using an error analysis, despite known limitations of automatic evaluation metrics and human ratings. This position paper takes the stance that error analyses should be encouraged, and discusses several ways to do so. This paper is based on our shared experience as authors as well as a survey we distributed as a means of public consultation. We provide an overview of existing barriers to carrying out error analyses, and propose changes to improve error reporting in the NLG literature.

## introduction error analysis is a formalised procedure through which researchers identify and categorise errors in system output. in the context of natural language generation (nlg), error identification often means manually annotating the output text, ideally with multiple annotators @xcite . the results of this analysis are often presented in a table, ranking the error categories by their frequency. this goes beyond the more common practice of providing some (strategically) hand-picked examples of 'cherries' (showing good model performance) and 'lemons' (showing the opposite). 1 while error analysis is relatively labour-intensive, it has some important advantages over commonly used evaluation metrics (see @xcite for an overview) or human ratings @xcite . these metrics only provide overall scores, and they do not explain what aspects of the output show room for improvement. error analysis does provide this information, and as such it is an essential step towards tackling issues with the output. based on an error analysis, one might for example establish a benchmark that targets common weaknesses of nlg systems. @xcite for further discussion.) moreover, error analyses provide a healthy dose of skepticism with regard to system performance, and as such help avoid the fallacy of ai functionality @xcite foot_0 . finally, it is simply not possible to automatically evaluate all aspects of nlg output @xcite . error analysis is flexible enough to identify the issues that are most salient to the human eye. uninformative metrics there is also an awareness of poor correlation between human judgements and automatic metrics @xcite @xcite , as well as the need to move beyond a single number to evaluate the performance of a given system with diverse sets of evaluation suites @xcite . ## method we asked nlg researchers and practitioners for their opinions about error analysis, as well as factors that affect the likelihood of including one in their work. we purposefully did not posit any hypotheses, since our aim is to describe the current perceptions of error analysis, and to sketch a path towards greater adoption of it in nlg research. ## results our results are generally organised by the topics identified above in section 3, but there are several themes (such as the importance of resources such as time and money) that recur throughout this section. ## limitations of this study because our participants are volunteers, we run the risk of possible self-selection bias: only people that are interested in error analysis may have taken the time to respond to our survey. this means that our survey may overestimate the support for error analysis in our community. this issue is inherent to any voluntary survey. (for example, @xcite report this limitation as well). given this limitation, we are still able to make existential claims about the barriers that exist for researchers wanting to carry out and publish error analyses; at least some researchers are held back by the barriers listed above. another limitation is that our sample size is relatively small, with 72 participants. as we discussed above, this is not very surprising, given the limited size of the nlg community. our participants were also allowed to skip as many questions as they liked in our survey. as a result, several questions were answered by less than half of our 72 participants. this may be seen as a limitation of our study, because a small group of researchers may not be representative of the larger research community. but our study does serve its original purpose: to consult other researchers about potential barriers and enabling factors for the use of error analysis in nlg, and to ensure that our list of barriers and enabling factors does not have any glaring omissions. two participants indicated that they were not familiar with the concept of error analysis before this study. one of them also noted that, because of this, they would have liked to see an "i don't know" option for the likert scale questions (although it was possible to leave these questions blank). ## conclusion & future work we have carried out a survey among nlg researchers and practitioners. our respondents were generally positive about error analysis, but they did see multiple barriers to the general adoption of this approach. by removing or minimizing these barriers (as discussed in section 4.3) and motivating authors to include error analyses in their work (section 5.1), we may see greater adoption of error analysis in the future. in the future, we would like to focus on developing tools and resources, such as error taxonomies, annotation tools, and clear guidelines that would help to encourage more routine and robust error analyses. in addition to development of resources, there also needs to be a structural change in the incentives around research publication that encourages prospective authors to conduct such analyses. more work is still needed to help enable error analyses by researchers and practitioners, but we are optimistic about the future of eval-uation within nlg.