Generating Contrastive Narratives Using the Brownian Bridge Process for Narrative Coherence Learning (2024)

Abstract: A major challenge for narrative reasoning is to learn narrative coherence. Existing works mainly follow the contrastive learning paradigm. However, the negative samples in their methods can be easily distinguished, which makes their methods unsatisfactory. In this work, we devise two strategies for mining hard negatives, including (1) crisscrossing a narrative and its contrastive variants; and (2) event-level replacement. To obtain contrastive variants, we utilize the Brownian Bridge process to guarantee the quality of generated contrastive narratives. We evaluate our model on several tasks. The result proves the effectiveness of our method, and shows that our method is applicable to many applications.

## introduction narrative reasoning @xcite is an account of the development of events, along with explanations of how and why these events happened @xcite , which has provoked a variety of applcations, including commonsense causal reasoning @xcite @xcite , abductive reasoning @xcite , and so on. a major challenge for narrative reasoning is to evaluate narrative coherence @xcite . existing methods mainly focus on devising self-supervised tasks, in which positive samples are from large-scale real narratives @xcite , and negative samples are created by sampling-based strategies. for example, @xcite ; @xcite ; @xcite create negative samples by shuffling or masking real narratives. @xcite incorporates randomly sampled sequences and model-completed @xcite sequences as negative samples. however, these strategies are generally coarse-grained and superficial. the resulting negatives still face problems of low quality, such as being irrelevant or repetitive @xcite , making them less representative, and easily distinguishable. hard negatives are critical in the contrastive learning framework @xcite @xcite . the ideal of hard negative samples should be that are similar to a real narrative but actually less coherent. to mine such negatives, a possible approach is to introduce contrastive narratives. contrastive narratives are examples that are similar in content, but convey different semantics @xcite . due to this property, we can crisscross foot_0 a narrative and its contrastive variants to obtain negative samples, as shown in figure 1 . the resulting negatives should be similar to the real narratives but less coherent, making them good candidates for hard negatives. however, existing works for collecting contrastive narratives rely heavily on manual annotation, which is costly and not scalable. to solve this problem, exploiting automated methods has great value, but is difficult since it requires preserving subtle differences while providing a clear delineation between the observed narrative and the generated ones. actually, the generation of contrastive narratives involves exploring the latent space surrounding a given narrative, enabling the creation of similar narratives with distinct characteristics. assuming that the evolution tendency of an observed narrative can be represented as a continuous trajectory in latent space, which can be modeled by brownian motion @xcite . consequently, we can sample the latent trajectories which exhibit proximity to the observed trajectory, and then decode the sampled trajectories into explicit narratives. but the problem is that the figure 1 : we define that an example consists of a prefix (p) and a suffix (s). left: an ideal contrastive variant x c , which is similar to x but conveys different semantics. text with red color denotes the difference. right: the solid line denotes the data manifold. the dashed line represents the methods for creating negatives, such as mixup @xcite or crisscrossing. as x c approaches x, the created negative example should be more "hard". in this paper, we devise two strategies to create hard negatives for narrative coherence learning. the first strategy crisscrosses a narrative with its contrastive variants, and the second strategy performs an event-level replacement. to obtain contrastive narratives, we first sample different latent trajectories from the brownian bridge region, then fix the start and end points of the narrative, and generate diverse contrastive narratives. our contributions can be summarized as follows. (1) based on the brownian bridge process, we generate high-quality contrastive narratives, which are used to synthesize hard negatives. (2) we propose a new coherence evaluator (coheval), which is enhanced by diverse and high-quality hard negatives. our model is trained entirely through selfsupervised contrastive learning, and can be applied to a wide range of downstream tasks. we evaluate our model on multi-choice tasks and one narrative generation task. we also conduct an in-depth analysis of our negative sample synthesis strategies. the experimental results demonstrate the effectiveness of our method. code is released at github.com/mufeiteng/contrastivenarratives. ## related work counterfactual story generation counterfactual story generation @xcite @xcite requires predicting how alternative events, contrary to what actually happened, might have resulted in different story endings. existing works for counterfactual story generation mainly include manual annotation @xcite or supervised fine-tuning @xcite methods. in our work, contrastive narratives can be seen as a special case of counterfactual narratives, where we confine that an observed narrative and its contrastive variants have the same start and end. we generate contrastive narratives in a selfsupervised manner, which is based on the brownian bridge process @xcite . ## conclusion in this paper, we propose to use the brownian bridge process to generate contrastive narratives, then we crisscross a positive story and its contrastive variants to create negative examples for contrastive learning. in addition, we devise the event-level replacement, which is an effective supplement to the crisscrossing strategy. the experiment verifies that (1) the generated contrastive narratives are qualified, and (2) our coheval is effective and is a general coherence evaluator that is applicable to many downstream tasks. and end events. however, this assumption may not be consistent with reality. in addition, under limited computing resources, we are unable to explore our method on larger data scales and larger pre-trained models. the experiment shows that our method is not able to surpass chatgpt. but this does not mean that our work has no value in the era of large language models. our method is essentially a discriminative model, while llms are generative models. they have different advantages. for example, llm is better at generating coherent text, and our coheval is better at multi-choice tasks. in fact, on timetravel, we use mcmc to make our coheval applicable to generating tasks. therefore, the gap between our method and llm has been magnified. on discriminative tasks, although our model is not as good as chatgpt, it outperforms the smaller chatglm on most multi-choice tasks. on the other hand, it is inherently unfair to directly compare small models with llms, as large models are obtained with massive resources, e.g., data, hardware, funding, etc. due to resource limitations, our method is not as good as chatgpt, but it is superior to chatglm, which also indicates that our method is valuable in low-resource scenarios. with sufficient computational resources, we can use a larger backbone and more data for training, which is expected to yield better results. we leave this in future works.