Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation (2021)

Abstract: With the recent advances of open-domain story generation, the lack of reliable automatic evaluation metrics becomes an increasingly imperative issue that hinders the fast development of story generation. According to conducted researches in this regard, learnable evaluation metrics have promised more accurate assessments by having higher correlations with human judgments. A critical bottleneck of obtaining a reliable learnable evaluation metric is the lack of high-quality training data for classifiers to efficiently distinguish plausible and implausible machine-generated stories. Previous works relied on heuristically manipulated plausible examples to mimic possible system drawbacks such as repetition, contradiction, or irrelevant content in the text level, which can be unnatural and oversimplify the characteristics of implausible machine-generated stories. We propose to tackle these issues by generating a more comprehensive set of implausible stories using plots , which are structured representations of controllable factors used to generate stories. Since these plots are compact and structured, it is easier to manipulate them to generate text with targeted undesirable properties, while at the same time maintain the grammatical correctness and naturalness of the generated sentences. To improve the quality of generated implausible stories, we further apply the adversarial filtering procedure presented by (CITATION) to select a more nuanced set of implausible texts. Experiments show that the evaluation metrics trained on our generated data result in more reliable automatic assessments that correlate remarkably better with human judgments compared to the baselines.

## introduction the surge of downstream applications for opendomain natural language generation (nlg), such as dialog systems @xcite and story figure 1 : heuristically generated implausible stories (the second block) for a given human-written story (the first block) using sentence, keyword and union manipulations versus injecting implausible sources into the story plot (the third block, from the left plot to the right one) and generating a more natural implausible story (the last story). blue highlights show the implausible sections. recently, learning-based approaches have been proposed to overcome this limitation by training classifiers to distinguish between plausible and implausible texts @xcite . the choice of training data for learning such classifiers is a key determinant of the metric effectiveness. existing works take humanwritten texts as plausible (positive) examples, while the negative samples are heuristically generated by randomly substituting keywords or sentences (see figure 1 ) @xcite . @xcite further improved the quality of evaluators by applying heuristic rules such as adding repetition, reordering and negation (see the union story in figure 1 ). in this work, we hypothesize that heuristically generated data cannot adequately reflect the characteristics of the implausible texts generated by language models, thus result in suboptimal trained evaluation metrics. this deficiency can be mitigated by generating high-quality implausible examples that are closer to the test data. toward this goal, we propose an approach based on the manipulation of plots, which are high-level structured representations of generated texts originally used as a contentplanning tool for better text generation @xcite . specifically, we propose to manipulate plots by injecting incoherence sources into them. the generation models conditioned on such manipulated plots lead to implausible texts that have pertinent similarities with implausible machine-generated texts and thus can serve as good negative examples for training evaluation metrics. we further improve the quality of training data by incorporating the adversarial filtering technique proposed by @xcite to select more challenging negative samples generated from the manipulated plots (see figure 1 ). eventually, these samples result in more reliable evaluation metrics. the contributions of this work are four-fold: ## related work existing work on automatic evaluation of generation models can be classified into two subgroups, non-learning-based and learning-based methods, which we briefly summarize below. non-learning-based metrics. some metrics in this group consider the centrality of a text around a specific topic as a proxy for measuring its quality. the transitions of entities in neighbor sentences and their distribution across text have been served as a measurement for quality assessment @xcite . perplexity is another commonly used metric to evaluate the quality of text and story generation models @xcite . learning-based metrics. this group of metrics is based on neural-based classifiers trained on a set of positive (plausible) and negative (implausible) texts. the common point between these metrics is using random sentence substitution to construct training examples, while the architectures are slightly different. @xcite trained a neural network with a sigmoid function on top of sentence embeddings extracted from lstm. @xcite designed sentavg that gets the sentence vectors from lstm, takes the average of these vectors to represent the whole text, and then passes it through a hidden layer. recently, @xcite proposed a more accurate automatic evaluation metric called union. this metric achieved better performance by using bert @xcite as a more effective classification model and have a broader set of negative samples coming from different heuristics. for all learning-based metrics, the simplicity of heuristically generated data samples makes them inadequate for an accurate evaluation of plausibility in open-domain generated texts. ## implausible text construction we formulate the evaluation of open-domain story generation as a binary classification task where the goal is to distinguish plausible and implausible generated stories, also referred to as positive and negative examples. clearly, the availability of high-quality positive and negative examples is essential for training reliable and generalizable metrics. while human-generated stories can be considered as positive examples, what constitutes good negative examples is a non-trivial question. specifically, consider a hypothetical decision boundary that separates positive and negative stories. while any point on one side of the boundary will be a negative example, intuitively we want examples that are not too far away from that boundary. to achieve this, we will start from positive examples, and modify them in a controllable manner to generate corresponding negative samples. ## learnable evaluation models we assess the plausibility of a text by training a classification model on the data that consists of human-written texts (positive examples) and constructed implausible stories (negative examples). binary classifiers trained on this data can produce the probability of plausible/implausible labels for each text. the predicted probability of the positive class is interpreted as the text's plausibility score. ## experiments we investigate the effectiveness of our proposed approach versus heuristic negative sampling techniques by focusing on the evaluation of opendomain story generation models in two datasets with short and long stories. we show the generalizability of metrics trained on our proposed plot manipulation data. we also separately assess the impact of each manipulation technique on the metric accuracy. ## conclusion automatic plausibility evaluation models that are trained on heuristically generated data show low correlation with human judgement. we address this issue by creating a better quality set of implausible texts. in contrast to existing methods that modify text at token level, our approach introduces incoherence sources at a more abstract plot level, which helps to guide the generative model conditioned on those manipulated plots to generate negative samples that are more similar to machine-generated incoherent texts. we further improve the data quality by applying adversarial filtering to select more challenging and refined negative samples. our experiments demonstrate that negative examples generated according to the proposed method result in more realistic implausible texts and consequently lead to more accurate evaluation metrics that have higher correlation with human judgement.