The Great Misalignment Problem in Human Evaluation of NLP Methods (2021)

Abstract: We outline the Great Misalignment Problem in natural language processing research, this means simply that the problem definition is not in line with the method proposed and the human evaluation is not in line with the definition nor the method. We study this misalignment problem by surveying 10 randomly sampled papers published in ACL 2020 that report results with human evaluation. Our results show that only one paper was fully in line in terms of problem definition, method and evaluation. Only two papers presented a human evaluation that was in line with what was modeled in the method. These results highlight that the Great Misalignment Problem is a major one and it affects the validity and reproducibility of results obtained by a human evaluation.

## introduction there has been a lot of academic discussion recently about different evaluation methods used and their validity @xcite @xcite . reproducibility is an important problem in our field of science and it is not currently archived in human evaluation, as some researches have found that trying to reproduce a human evaluation gives different results @xcite . however important reproducibility is, we have identified an even more severe problem in human evaluation. we call this problem the great misalignment problem that is a mismatch between a problem statement, a proposed model and a proposed evaluation method. it is typical in the field of nlp to work with ill-defined problems. for instance, many machine translation papers @xcite @xcite do not extensively define what they mean by translation, a topic that has multiple definitions in translation studies @xcite @xcite , but merely take it for granted and focus on proposing systems that achieve high scores in an automatic evaluation metric such as bleu @xcite . for as long as you work with a problem the solution of which you can objectively measure by automated metrics, the role of a problem definition is not that important. the situation changes, however, when your main evaluation method is a subjective human evaluation. the reason for this is simple: only when you have defined the problem clearly, can you derive the questions and methods for a human evaluation (c.f. @xcite . when one does not have a clear understanding of the problem one seeks to solve, the evaluation is usually not representative of the problem, thus they are misaligned. the great misalignment problem is not just about the misalignment between the problem definition and the evaluation, but also the proposed solution, let it be rule-based, algorithmic or a machine learning model. we can often see that the solution itself has very little to do with the human evaluation methods used. in this paper, we study the great misalignment problem (alignment of a problem definition, method and human evaluation) by surveying papers published in acl 2020 that use human evaluation. we focus on acl since it is supposed to be the most prestigious conference in the field. for courtesy reasons, we anonymize the papers surveyed, except paper 3 (mohankumar et al., 2020) which was the only paper that did not exhibit the great misalignment problem. we do not want single anyone out with our critique as that is not the goal of our paper. ## surveying the great misalignment problem we filter all papers that have the words "human" and "evaluat*" or "judge*" in their abstract. this way, we can find papers mentioning human evaluation, human evaluators and so on. we include all papers published in the acl 2020 foot_0 (excluding workshops) in the search. we sort these papers (79 in total) at random and take the first 10 papers that actually have used human evaluation, as some of the papers mentioned human and evaluation, but did not conduct a human evaluation. we did not consider papers that suggested automated evaluation metrics based on correlation with human evaluation as their main contribution. human evaluation is most common in natural language generation as 8 out of 10 papers deal with nlg. the papers, we considered for evaluation in terms of human evaluation, presented automatic evaluation metrics in addition to human evaluation. for all the 10 papers, we looked at the following questions: evaluation is in line with the definition, if the evaluation questions reflect the different aspects that were defined important in the problem definition. for the evaluation to be in line with the model, it should evaluate what the model was designed to do. if for example, a poem generator model takes meter and rhyme into account, but it is evaluated based on fluency and poeticness, the method and the evaluation are not in line. for them to be in line, meter and rhyme should have been evaluated. the evaluation can be in line with the overall topic of the paper: for example, evaluating poeticness is in line with poem generation. the results of our survey can be seen in paper 7 presented a very explicit mathematical statement for the problem they were to solve in the paper. although, this is very specific to the implementation the authors had, it is still better than a completely absent definition as seen in the majority of papers that took an abstract level topic # of samples n less than 20 1 100-500 5 500-1000 2 1000-1500 2 ## discussion a direct implication of the great misalignment problem is that the results of any human evaluation cannot be reproducible as they are measuring something else than what was modeled in the proposed solution. therefore, any results obtained by the human evaluation can only be due to some other variable such as the data used in training, a bias in the often too small evaluation sample or a bias in the often too few evaluators. furthermore, many factors affect the quality of the human evaluation. for instance, forcing the evaluators to provide answers to questions that they do not know how to answer without giving them the possibility to skip such questions could introduce noise in the evaluation data. on the contrary, some unfaithful evaluators (scammers) might abuse such an opportunity to finish the survey effortlessly and in a short time by submitting valid answers, i.e. "i do not know". some surveying platforms support defining criteria to discard scammers, such as test questions or a minimum response time. test questions are greatly useful to enhance the quality of answers. however, when used for evaluating subjective tasks they would add a bias as evaluators must share the same opinions of the authors or, else, they will be rejected from continuing the survey. the minimum response time is there to eliminate scammers who answer promptly without even reading the questions. other similar criteria exist, e.g., language and geographical restrictions that might aid in finding competent evaluators, the ordering of samples when presented side by side, and the bias of providing a single answer consistently to different questions @xcite . this just to show that many factors regarding the human evaluation setup contribute massively to the quality of the evaluation. there is no one fixed or correct way to conduct all human evaluations, but researchers in the field should consider such biases and aim towards reducing them in addition to revealing the full details of the evaluation setup and the intuition behind it to the reader to allow reproducibility of the scientific work. unfortunately, none of the papers surveyed described the human evaluation conducted in a clear fashion, where different biases or threats to the validity of the results would have been made clear. our field is very often focused on gaining the state of the art performance from our models. however, when the human evaluation metrics used have little to nothing to do with the problem or the method, knowing what truly is the state of the art becomes less clear. each system, regardless of their final evaluation score, will have a lot of advantages and disadvantages that do not become evident if the problem they are used to solve is ill-defined. this leads to the problem that evaluation scores are the only way of showcasing the superiority of your system, no matter how unrelated the evaluation scores were to the problem or to your method. the problem that comes from not evaluating what you have modeled in your method is that you cannot say whether what you modeled actually works as intended. this is especially problematic in the case of nlg, which represents a majority of papers surveyed. nowadays generating good sounding text is no longer an issue as very generic models such as gpt-2 @xcite can be used to generate many different kinds of text. this leads to the problem that if no clear definition is provided, any method that spits out text will satisfy the requirements, and if the evaluation does not capture anything about how the method was implemented, then it is impossible to tell whether your system actually improved anything but the very surface of the text. in our own experiments @xcite with human evaluation, we have found that questions that do not measure what has been modeled make it very difficult to say what should be improved in the system and how, although such an evaluation makes the end results look impressive. as gervás (2017) puts it, any feature not modeled in a generative system that happens to be in the output can hardly be a merit of the system, but is in the result due to mere serendipity. to complicate the things, @xcite points out that people are willing to read more content into the output of a system than what the system had planned. to solve these problems, we decided to follow an approach where we defined exactly what we need our system to be able to produce in its output (humorous headlines). in our first paper @xcite , we believed we had solved the problem, only to realize in our follow-up paper @xcite ) that the human evaluation results contradicted our own impression of the output produced by the different systems. as it turns out, the evaluation questions were too abstract and left enough room for people to read more into the output. while our latest trial in solving the issue has been using concrete evaluation questions @xcite ) that measure exactly what the system was designed to do in order to reduce subjectivity, such an evaluation practice cannot be embraced if there is no alignment between the definition, solution and evaluation. no matter how concrete the evaluation questions are or how sound the evaluation method is in terms of forming a good quantitative questionnaire, an evaluation that neither evaluates the method nor the problem can hardly be meaningful. all in all, we have had good experiences when conducting human evaluation in person by printing out questionnaires and presenting them to people. it is not at all difficult to find test subjects who are willing to participate. this way, one can avoid the problem of paid online questionnaires where the motives and skills of the human evaluators is difficult to assess. furthermore, conducting evaluation this way, opens the evaluation up for criticism and it is easy to get direct feedback from the participants on the test design and its difficulty. ## conclusions in this paper, we have described a fundamental issue in human evaluation in the field of nlp. our initial survey results show that the issue can be found extensively in the papers published in our field. the great misalignment problem makes it impossible to critically assess the advancements in the field, as usually problems papers are trying to solve, are not defined well enough to be thoroughly evaluated by human judges. in addition, if the method proposed does not align well with the problem nor the evaluation, any human evaluation results can hardly be a merit of the method. there are several uncontrolled variables involved and based on our survey results, human evaluation is not conducted in the same rigorous fashion as in other fields dealing with human questionnaires such as in social sciences (c.f. babbie 2015) or fields dealing with evaluation of computer systems such as design science (c.f. @xcite . there is a long way for our field to go from here in order to establish more sound and reproducible human evaluation practices. narrowing the problem definition down from an abstract definition such as "poem generation" or "diverse dialog generation" not only helps in understanding the problem from the point of view evaluation, but also makes it possible to ask more meaningful questions while proposing a solution. such an ideology can be useful also in domains where evaluation is conducted automatically in order to critically assess the validity of the approach and the evaluation method used. the results presented in this paper are based on only 10 papers published in acl 2020. the sample seems representative to the general feel of the state of human evaluation in the field, but it is important in the future to survey a larger sample of papers to better understand the problem. while conducting our survey, we also paid attention to other issues in human evaluation such as the fact that the evaluation methods are not usually adequately described in terms of presentation of the evaluation questions (many papers did not report the questions at all), selection of human judges, task instructions and so on. there were huge differences also in the number of human judges from only 3 to 30 judges, and also in the number of samples evaluated. our field does not have an established methodology for human evaluation, but at the current stage, the validity of many human evaluation methods is questionable. this is problematic as our field clearly has problems that rely on human evaluation. we do not believe that removing human evaluation altogether in favor of objective evaluation methods is the optimal solution either, as automatic evaluation metrics come with their own problems and biases. in order to reach to better human evaluation practices, a study of human evaluation itself is needed. from our experiences with human evaluation, we can say that it is certainly not a straight forward problem due to a variety of different reasons, the largest of them being subjective interpretation and limited understanding the human evaluators have of the evaluation task, questions and the actual output that is to be evaluated.