Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis (2022)

Abstract: There is an increasing trend in using neural methods for dialogue model evaluation. Lack of a framework to investigate these metrics can cause dialogue models to reflect their biases and cause unforeseen problems during interactions. In this work, we propose an adversarial test-suite which generates problematic variations of various dialogue aspects, e.g. logical entailment, using automatic heuristics. We show that dialogue metrics for both open-domain and task-oriented settings are biased in their assessments of different conversation behaviors and fail to properly penalize problematic conversations, by analyzing their assessments of these problematic examples. We conclude that variability in training methodologies and data-induced biases are some of the main causes of these problems. We also conduct an investigation into the metric behaviors using a black-box interpretability model which corroborates our findings and provides evidence that metrics pay attention to the problematic conversational constructs signaling a misunderstanding of different conversation semantics.

## introduction automatic evaluation of natural language models in general and dialogue models in specific has been a focus of ongoing research. the gold standard for evaluation of dialogues is human judgement @xcite @xcite @xcite but human judgements are hard to obtain. other than human judgements, dialogue simulations are used to judge different aspects of a model behavior @xcite @xcite @xcite . neural models of dialogue rely on text similarity metrics like bleu, rouge or meteor @xcite @xcite , however these do not correlate well with the human a: do you like singing? b: yes, i do. a: let's go to a etv tonight. b: but i can't sing it well. a: it doesn't matter. no one will laugh at you. b: ok, i'il go with you. when shall we go? actual response: what about six? adversarial response: ok. let's make it. human score: 0.05; adversarial score: 0.43 ## conversation properties of interest dialogue systems-specially those based on neural architectures, show poor performance in generating consistent responses by keeping track of information in a dialogue context and are prone to generating repetitive, unnatural and bland responses @xcite @xcite . these problems also guide the research in the area of dialogue system evaluation with many metrics focusing on evaluating coherence and consistency of dialogue models @xcite @xcite @xcite . these findings help us choose relevant dialogue attributes to manipulate so the insights from the performance of different metrics can be used to address different problems in dialogue modeling. to generate adversarial examples we focus on the following aspects of conversations: 1) coherence 2) naturalness 3) interestingness. we manipulate each of these attributes by targeting specific aspects which contribute to these: coherence: i) entailment ii) pronoun use iii) named-entity use iv) co-reference v) contradictions vi) speaker sensitiveness naturalness: vii) unnatural repetitions viii) vocabulary diversity ix) unnatural paraphrasing x) natural paraphrasing xi) entrainment interestingness: xii) dullness. one example for manipulation of each of the major attributes is shown in the coherence naturalness interestingness contradiction unnatural paraphrasing dullness a: could you explain what you saw? b: i was in the bank at the time of the robbery. a: what did you see? b: i saw a man come in with a gun. a: did you see his face? actual: no . he had a mask on. ----------adversarial: i did not see a man come in with a gun. a: you saved my life yesterday, rachel. i can't believe i forgot to bring my wallet when we went to lunch with those clients. b: it was a good thing i had enough on me. actual: let me buy you lunch today to pay you back. ----------adversarial: allow me purchase you luncheon today to give you back. a: what are you doing tonight? b: i have to run to the grocery store. a: don't you hate fighting the crowds on the weekends? actual: yes, but i am out of food and milk. ## adversarial techniques we specifically focus on four conversational datasets daily dialogue (ddial) @xcite , persona chat (perch) @xcite , reddit @xcite and multiwoz @xcite to test our heuristics. we test an implementation of our heuristics using these datasets and release it with this work 1 . ## experiment results and analysis we use three different evaluation metrics dialo-grpt @xcite , pang-evaluation @xcite and user satisfaction @xcite to test our adversarial test-suite. the first two metrics we test are for open-domain conversations while the last one is trained to judge taskoriented conversations. we focus on both opendomain dialogue and task-oriented dialogue metrics to highlight the diversity of our test-suite and show how neural metrics in both of these settings contain fundamental problems. both dialogrpt and user satisfaction metrics use human feedback from our analysis of metric behavior on adversarial conversations, we have the following key takeaways: 1) neural metrics are not suited to judge overall quality of the conversations; 2) they are unable to correctly understand conversation semantics; and 3) they are prone to data and traininginduced biases. ## choice of evaluation metrics the metrics evaluated in this work are some of the recently proposed with varied training methodolo-gies e.g., training using human judgments versus some measure of text similarity. also, our choice of these metrics is rooted in the fact that these metrics are primarily judged by a singular measure of improvement which in most cases is the correlation with human judgments. our analysis highlights that such singular measures of improvement are not enough to capture the variability of performance exhibited by the evaluation metrics in judging complex conversation behaviors like contradictions. our goal is not to provide an exhaustive accounting of the performance of all available neural metrics but empirically highlight the problems and performance variability which arise because of different training methodologies. given our results, we hypothesize that other metrics like @xcite @xcite trained similarly have deeper problems which need to be highlighted using a fine-grained methodology similar to ours. ## conclusion and future work our test-suite helps us draw various insights about performance of different metrics, and shows that all the metrics we test have room for improvement. it points at several flaws in the metrics: 1) lack of generalization ability to unseen data, 2) inability to correctly understand different conversation semantics and 3) prone to training and data-induced biases. furthermore, our interpretability analysis further corroborates these shortcomings and helps us conclude that metrics need to be exposed to more information about conversation behaviors to make them more robust. adversarial behaviors from our test-suite help point to many shortcomings in the metrics we test, but many behaviors are very simplistic e.g. speaker sensitivity attacks. further research can help make these attacks more human-like which may help reveal more information about the evaluation metrics. our test-suite can also be used directly to make the metrics more robust e.g. by augmenting their training data with adversarial examples or by using it as a reward signal in a rl setup for the training of evaluation metrics. this shows several use-cases of our test-suite and directions for future research.