Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets (2022)

Abstract: Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model’s output over another is often necessary.However, human evaluation is usually costly, difficult to reproduce, and non-reusable.In this paper, we propose a new and simple automatic evaluation method for NLG called Near-Negative Distinction (NND) that repurposes prior human annotations into NND tests.In an NND test, an NLG model must place a higher likelihood on a high-quality output candidate than on a near-negative candidate with a known error.Model performance is established by the number of NND tests a model passes, as well as the distribution over task-specific errors the model fails on.Through experiments on three NLG tasks (question generation, question answering, and summarization), we show that NND achieves a higher correlation with human judgments than standard NLG evaluation metrics. We then illustrate NND evaluation in four practical scenarios, for example performing fine-grain model analysis, or studying model training dynamics. Our findings suggest that NND can give a second life to human annotations and provide low-cost NLG evaluation.

## introduction pre-training of large language models has fueled recent progress in many natural language generation (nlg) tasks such as summarization @xcite , question answering @xcite , and question generation @xcite . however, quantifying this progress remains a challenge due to the open-ended nature of nlg. the gold standard for nlg evaluation is manual expert annotation: it can be highly precise and fully customized to an nlg task, helping identify model limitations, and setting the direction of future work. the main limitation of manual expert annotation is the complexity and cost associated with running an evaluation. the cost often increases linearly or quadratically with the number of models compared, restricting evaluation to a small number of models. to circumvent the cost of expert evaluation, many in the field rely on automatic metrics such as bleu @xcite , which compute text quality based on n-gram overlap between model outputs and human references. such metrics are easy to compute, and have been shown to moderately correlate with human judgments, but are limited in three ways: they only offer an aggregate score that is difficult to interpret, they do not offer a clear upper bound in performance, and they have limited generalized ability to some nlg tasks @xcite . in this paper, we propose a new and simple automatic framework for the evaluation of nlg models which we call near-negative distinction (nnd). at a high level, the nnd framework bridges the gap between expert annotation and automated metrics by repurposing existing annotations into a series of automatic tests which assess how likely a model is to avoid previously annotated errors. the first contribution of our work is the definition of the nnd framework, illustrated in figure 1 . in nnd, an existing human evaluation dataset d is repurposed to create a series of automated tests. for a given input context, d should contain annotations for several model outputs, some high-quality (candidates 1, 4) and some low-quality (candidates 2, 3, 5). a collection of nnd tests is created containing candidate pairs of differing quality. generation models pass an nnd test if they assign a higher likelihood to the high-quality candidate than the low-quality one. nnd evaluation produces an overall test pass rate, as well as a pass rate for each error category, which can be used to inspect model strengths and weaknesses. the second contribution is the creation of nnd datasets from existing human evaluations for three nlg tasks: question generation, generative question answering, and summarization. on these three tasks, verification experiments find that nnd pass rates correlate better with human judgments than existing evaluation metrics, both n-gram-based metrics such as bleu @xcite , and more recent metrics such as bertscore @xcite and questeval @xcite . the third contribution is a collection of practical experiments showcasing how to use nnd. the experiments demonstrate the flexibility of the nnd framework, showing it can be useful to extrapolate a model's performance in a user study, perform fine-grain model analysis, study scaling effects in model families, or discover trends during training. although we focus experiments on the english language, the nnd framework is not englishspecific, and we encourage the community to experiment with nnd evaluation, helping to expand it to new nlg domains and languages. we publicly release the nnd datasets we generated as well as the code needed to create new nnd datasets, and models used in experiments 1 . ## near-negative distinction we now detail the process of transforming preexisting human annotations into an nnd dataset and show how to perform nnd evaluation. ## nnd verification we now present results from running the verification experiments of section 2.3 on the three domains we study. in our analysis, we compare nnd to standard n-gram based evaluation metrics: bleu @xcite , rouge @xcite , meteor @xcite , as well as more recent transformer-based metrics: bertscore @xcite , bartscore @xcite and questeval @xcite . for each verification experiment, we are limited to evaluating models present in the annotation datasets that have been open-sourced as the nnd framework requires a running version of the model to compute candidate likelihoods. for qgen, verification experiments used all seven models present in the annotations dataset, with separate verification experiments run on each of the three error types. for qa, verification experiments involved three of the four available models foot_0 , and were run on each question category. for summarization, verification experiments were run with five summarizers from summeval (m9, m17, m20, m22, m23) with experiments run on each of the four summarization aspects. we do not run verification experiments on frank, as it contains fewer annotations of publicly released models. verification results summarized in we note an important conceptual difference between nnd and the metrics we compare to which are reference-based. reference-based metrics score a generator by establishing a similarity between the model's candidate outputs and human-written references. in contrast, nnd is reference-less and relies on human annotations of several model candidate outputs to evaluate models. we hypothesize that the use of near-negatives, and whether a model is likely to avoid them, provides a useful signal that leads to high-quality model evaluation. we next turn to use the nnd framework in practical situations and assume that nnd pass rates provide quality estimates of model ranks and performance gaps between models. in quiz design, the largest mixqg-3b model was not included in the annotations due to latency requirements for the interface @xcite . further, new qgen models have been released since the study's conduct. we leverage nnd's ability to provide category-specific estimates of performance to extrapolate how these unseen models would have performed in the quiz design study. ## nnd applications nnd pass rate (%) 65.0% 70.0% 75.0% 80.0% overall consistency coherence fluency relevance (a) summeval nnd nnd pass rate (%) 65.0% 70.0% 75.0% 80.0% 85.0% overall sem. frame discourse verifiability bart-large pegasus (b) frank nnd figure 2: fine-grain comparison of a pair of summarization models on based on two nnd test sets. sum-meval estimates performance on four general aspects, and frank focuses on factual consistency errors. we run nnd experiments for each of the seven models included in the study, as well as the unseen models. results are summarized in first, the three novel models all achieve strong performances, obtaining three of the best four overall nnd pass rates. the mixqg-3b achieves the highest performance overall, seeing a total improvement of 2% when compared to mixqg-l, the best performer at the time of the study, with gains on all three error categories. the macaw models achieve the strongest performance in disfluency, but lower performance on off target and wrong context lead to lower performance overall. these results show that nnd can be used to give a second life to human evaluation datasets by projecting model performance a posteriori. ## conclusion we introduce the near-negative distinction (nnd) framework for the evaluation of nlg models. in the nnd framework, a pre-existing human evaluation dataset is repurposed to create nnd test pairs comprised of text candidates of differing quality. models are evaluated on their ability to assign a higher likelihood to high-quality candidates, giving an estimate of whether models would avoid the errors of previously evaluated models. we apply the nnd framework to three nlg tasks: question generation, question answering, and summarization, and show that nnd results correlate better with human preference than prior nlg evaluation methods. the nnd framework allows the breaking down of model performance by error category, and we illustrate how the framework's flexibility can be used to understand model strengths and weaknesses, for instance extrapolating how newer models would perform in an existing human study or how a summarization model can lose factual consistency ability during training. nnd is a simple, automatic, and versatile evaluation method that we hope can accelerate nlg research.