Which dialogue data do they use to evaluate on?
Datasets from PersonaChat and Wizard of Wikipedia tasks.
While dialogue remains an important end-goal of natural language research, the difficulty of evaluation is an oft-quoted reason why it remains troublesome to make real progress towards its solution. Evaluation difficulties are actually two-fold: not only do automatic metrics not correlate well with human judgments, but also human judgments themselves are in fact difficult to measure. The two most used human judgment tests, single-turn pairwise evaluation and multi-turn Likert scores, both have serious flaws as we discuss in this work.  ::: We instead provide a novel procedure involving comparing two full dialogues, where a human judge is asked to pay attention to only one speaker within each, and make a pairwise judgment. The questions themselves are optimized to maximize the robustness of judgments across different annotators, resulting in better tests. We also show how these tests work in self-play model chat setups, resulting in faster, cheaper tests. We hope these tests become the de facto standard, and will release open-source code to that end.
Dialogue between human and machine is an important end-goal of natural language research. The open-ended nature of generating sequences in a multi-turn setup naturally makes the task difficult to evaluate – with full evaluation possessing many of the difficulties of the task itself as it requires deep understanding of the content of the conversation. As in many other natural language generation (NLG) tasks, automatic metrics have not been shown to have a clear correlation with human evaluations BIBREF0, BIBREF1. This means the current standard for all dialogue research involves human trials, which slows down research and greatly increases the cost of model development. Unfortunately, human judgments are themselves difficult to measure. The two most used approaches, single-turn pairwise evaluation BIBREF2, BIBREF3, and multi-turn Likert scores BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 have serious limitations. Single-turn pairwise evaluation provides the benefits and simplicity of an A/B test, allowing for cheap and fast annotations, with comparisons that are robust to annotator score bias, but fail to take into account the multi-turn aspect of conversations. To give a trivial example, such comparisons fail to capture whether the model would repeat itself in a multi-turn conversation because they only look at one turn; repetition is a known issue that humans dislike BIBREF6. Multi-turn Likert scores require the annotator to have a multi-turn conversation and then provide an integer score, which is more costly and time-consuming to run but evaluates full conversations more accurately. The integer scores however suffer from differing bias and variance per annotator, which researchers have tried to mitigate BIBREF9, but nevertheless due to its lack of sensitivity often yields comparisons that are not statistically significant. Furthermore, due to strong anchoring effects during model evaluation, i.e. that annotators are affected by the first systems they evaluate, Likert comparisons are generally not comparable across multiple papers. This mandates that evaluations of new models be simultaneously collected with baselines, further increasing the cost of developing additional models BIBREF6. In this work we introduce Acute-eval, a method that combines the benefits, and attempts to mitigate the deficiencies, of the above two approaches by introducing a pairwise relative comparison setup for multi-turn dialogues. In each trial, we show the annotator two whole conversations, with the second speaker in each conversation highlighted, as the judgment should be independent of the quality of the first speaker, see Figure FIGREF1. We then show a carefully worded question with two choices: speaker A or B, where the question measures a desired quality such as which speaker is more engaging, interesting or knowledgeable. Our experiments show that annotators perform well in this setup, and that our method can reveal subtle but significant differences between conversational models that other approaches, such as multi-turn Likert, cannot. Overall, our work provides the following contributions: A new evaluation method with a clear mechanism that provides fast, cheap iteration. This evaluation method allows efficient reuse of data from prior papers, allowing new models to be evaluated independently of baselines, and dramatically lowers the cost of annotation. We optimize question choices to find those with the highest agreement, increasing confidence in the desired test. We provide the wording of the questions that we found to work best for several questions of interest (most engaging, human, interesting or knowledgeable conversationalist) for further research use. We provide an explicit benchmark comparison between current best performing retrieval and generative models on two recent tasks, PersonaChat BIBREF5 and Wizard of Wikipedia BIBREF7 for several question choices, revealing the current state-of-the-art, and to be used for benchmarking on these tasks in the future. We show that our test can be applied to self-chats rather than human-model conversation logs, which can reveal problems with existing models at a cheaper price, and provides high agreement with the human-model evaluations. We will release the code for running these tests.