BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation

Abstract

Natural language processing (NLP) systems are increasingly trained to generate open-ended text rather than classifying between responses. This makes research on evaluation metrics for generated language -functions that score system output given the context and/or human reference responses -of critical importance. However, different metrics have different strengths and biases, and reflect human intuitions better on some tasks than others. There is currently no simple, unified way to compare, analyse or evaluate metrics across a representative set of tasks. Here, we describe the Benchmark to Evaluate Automatic Metrics (BEAMetrics), a resource to make research into new metrics itself easier to evaluate. BEAMetrics users can quickly compare existing and new metrics with human judgements across a diverse set of tasks, quality dimensions (fluency vs. coherence vs. informativeness etc), and languages. As generation experts might predict, BEAMetrics reveals stark taskdependent differences between existing metrics, and consistently poor performance on tasks with complex answer spaces or high reliance on general knowledge. While this analysis highlights a critical issue facing current research practice, BEAMetrics also contribute to its resolution by facilitating research into better metrics -particularly those that can account for the complex interaction between context and general knowledge inherent to many modern NLP applications. 1

TRUE: Re-evaluating Factual Consistency Evaluation

“…Adding to the related work mentioned throughout the paper, works on unified evaluation of text generation across tasks include GEM , where the focus is on evaluating system outputs and not the factual consistency evaluation methods as in TRUE. BEAMetrics (Scialom and Hill, 2021) proposes meta-evaluation protocols across tasks, but does not focus on factual consistency. When discussing consistency ("correctness") they measure correlations, which are not sufficient as mentioned in Section 2.3.…”

TRUE: Re-evaluating Factual Consistency Evaluation

“…generation across tasks include GEM , where the focus is on evaluating system outputs and not the factual consistency evaluation methods as in TRUE. BEAMetrics (Scialom and Hill, 2021) proposes meta-evaluation protocols across tasks, but does not focus on factual consistency. When discussing consistency ("correctness") they measure correlations, which are not sufficient as mentioned in Section 2.3. present an adversarial meta-evaluation for factual consistency evaluators, focused on summarization.…”