Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications (2022)

Abstract: There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess varying quality criteria depending on the deployment setting. While the landscape of NLG evaluation has been well-mapped, practitioners’ goals, assumptions, and constraints—which inform decisions about what, when, and how to evaluate—are often partially or implicitly stated, or not stated at all. Combining a formative semi-structured interview study of NLG practitioners (N=18) with a survey study of a broader sample of practitioners (N=61), we surface goals, community practices, assumptions, and constraints that shape NLG evaluations, examining their implications and how they embody ethical considerations.

## introduction evaluating natural language generation (nlg) models and systems-that generate new or altered text-is notoriously difficult, as often there are many valid ways to express similar content in text. this difficulty is often compounded by the need for nlg systems to meet a variety of goalsoften measured in competing and imperfect waysdepending on their deployment settings @xcite . such challenges have been mapped out for a variety of nlg tasks, systems, and settings @xcite @xcite , including examinations of how their design, deployment, and evaluation can give rise to ethical concerns @xcite @xcite @xcite . these challenges also mean that practitioners seeking to evaluate their nlg systems must decide on their goals for the system (e.g., help users write fluently), what quality criteria to use to reflect those goals (e.g., output fluency), and how to operationalize the measurement of those criteria (e.g., perplexity or crowd judgments). such decisions are often guided by deployment settings, by evaluation norms in a research community @xcite , by assumptions about "proper" nlg model or system behavior, as well as by realworld constraints @xcite . although the landscape of quality criteria and operationalizations has been surveyed, much less is known about how practitioners' goals, assumptions, and constraints shape their decisions about which criteria to use, when, how, and why. limited visibility into how these factors shape nlg evaluations can, however, make it difficult to anticipate issues these factors might give rise to, or even what we can learn about research progress. in this paper, we surface goals, assumptions, community practices, and constraints that nlg practitioners foot_0 work with when evaluating nlg systems, including limitations and ethical implications. these tacit elements of nlg evaluation are challenging to ascertain from literature surveys, as they are often unstated or only partially stated. to reveal otherwise latent aspects of nlg evaluation practice, we apply a mixed-methods approach from human-computer interaction (hci). specifically, we conduct a formative semi-structured interview study (@xmath1.1), practices ( §3.2), and assumptions and constraints ( §3.3) when evaluating nlg systems, including ethical considerations ( §3.4). by recognizing more tacit elements of nlg evaluation, our work aims to provide scaffolding for discerning the issues they may give rise to and help re-think nlg evaluations. ## background and methods to examine nlg evaluation practices-along with the goals, assumptions, and constraints that inform them-and whether they incorporate assessments of possible adverse impacts, we first conducted a formative semi-structured interview study of nlg practitioners ( §2.1). to probe the themes emerging from our interviews at greater scale, we subsequently conducted a survey study with a broader sample of practitioners ( §2.2). participant recruitment and irb. we recruited nlg practitioners using snowball sampling @xcite , via targeted emails and social media posts, with a request to share with relevant groups. each interviewee received a @xmath050 gift cards. both studies were irb approved, and we obtained informed consent from all participants. ## interview and survey findings across both studies, we observe common themes in participants' responses that we overview below. interview participant quotes-anonymized and paraphrased for brevity and clarity-are followed by "p" and a participant id, while quotes from survey responses are only marked by "sp." statistics based on survey responses are followed by "sq" and the question number. we report disaggregated statistics as (x% academic, y% non-academic) when gaps exist between academic and non-academic participants. we note, however, that the statistics we report are only indicative as our respondent sample is skewed towards industry and academic researchers ( ## conclusion we identify practices and assumptions that shape the evaluations of nlg systems, which we argue deserve further attention due to their potential adverse impacts, particularly when those practices and assumptions go partly stated, or are not stated at all. our findings suggest a number of approaches and future directions, including some raised by survey participants when asked how to improve nlg evaluation @xcite . first, the range of implicit assumptions and choices we uncover indicates that future evaluations might be strengthened by making these assumptions and choices explicit, including how quality criteria are conceptualized, how manual analysis is conducted and shapes decisions, and whether some evaluation approaches gate others (as automatic evaluations often gate human ones). participants also suggested several opportunities for evaluation; one respondent raised the possibility of lab studies to explore people's perceptions and uncover failure modes without fully deploying a system, while another emphasized the need to study systems in interaction given the importance of social and task context. industry participants described a range of practices, including user engagement metrics, red teaming, and blocklists, which may be less well-studied in academia. the challenges surrounding the use of automatic metrics-particularly the pressure towards a "kitchen sink" approach despite widespread recognition of many existing metrics' limitations-may not have an easy remedy. a "kitchen sink" approach should be thoroughly justified, and not used only because practitioners lack clarity about their goals and quality criteria. beyond clarity about goals and quality criteria, a better understanding of evaluation datasets and metrics-whose perspectives and language are contained in datasets, what datasets and metrics can and cannot capture, and how metrics are connected to user or downstream outcomes-may also clarify their appropriateness for different settings, and disincentivize simply evaluating with as many of them as possible. furthermore, human evaluations, particularly those grounded in specific deployment settings or involving extensive participation, tend to be costly. not only is such work expensive, but it may also involve qualitative or participatory approaches less familiar to nlp practitioners (than using automatic metrics), or may slow down product releases or publications. industry teams often constrained by tight deadlines may be especially unlikely to adopt qualitative or participatory approaches in the absence of readily available, well-validated methods and guidance for their use. while such approaches will never be as inexpensive as many automatic metrics, user studies or design workshops with users to help develop and solidify methods and guidance might lower barriers to their broader adoption. finally, shifting publication incentives towards work grounded in deployment scenarios, as well as work engaging with methods from other disciplines (e.g., hci or sociolinguistics) may encourage the investment of time and resources needed to carry out more thorough evaluations. turning to ethical considerations, our participants often raised the need for investment in the development of resources for conceptualizing and measuring f&i issues. these include better frameworks of harms (to help anticipate issues beyond toxicity), appropriate datasets, additional frameworks to guide (and scale) qualitative evaluations, task-specific metrics to combat automatic metric reuse without proper validation, and approaches for measuring latent qualities such as voice, style, and topic (which remain longstanding open questions). other opportunities include broader disciplinary shifts. several participants desired clearer community standards addressing not only best practices in modeling, but also meta-level questions-e.g., who makes decisions on what ethical considerations to prioritize (for evaluation and beyond), or how to develop repeatable mechanisms for surfacing, reporting, and addressing failures as they arise.