Exploring Accurate and Generic Simile Knowledge from Pre-trained Language Models (2023)

Abstract: “A simile is an important linguistic phenomenon in daily communication and an important taskin natural language processing (NLP). In recent years, pre-trained language models (PLMs) haveachieved great success in NLP since they learn generic knowledge from a large corpus. However,PLMs still have hallucination problems that they could generate unrealistic or context-unrelatedinformation.In this paper, we aim to explore more accurate simile knowledge from PLMs.To this end, we first fine-tune a single model to perform three main simile tasks (recognition,interpretation, and generation). In this way, the model gains a better understanding of the simileknowledge. However, this understanding may be limited by the distribution of the training data. To explore more generic simile knowledge from PLMs, we further add semantic dependencyfeatures in three tasks. The semantic dependency feature serves as a global signal and helpsthe model learn simile knowledge that can be applied to unseen domains. We test with seenand unseen domains after training. Automatic evaluations demonstrate that our method helps thePLMs to explore more accurate and generic simile knowledge for downstream tasks. Our methodof exploring more accurate knowledge is not only useful for simile study but also useful for otherNLP tasks leveraging knowledge from PLMs. Our code and data will be released on GitHub.”

## introduction a simile is a figure of speech that compares two things from different categories (called the tenor and the vehicle) via shared properties @xcite . a tenor and a vehicle are usually connected with comparator words such as "like" or "as". for example, the sentence "the girl is as pretty as an angel." is a simile where the tenor is "the girl", the vehicle is "an angel", the comparator is "as ... as" and the shared property is "pretty". simile plays an important role in human language to make utterances more vivid, interesting, and graspable @xcite , comprehending similes is essential to appreciate the inner connection between different concepts and is useful for other natural language processing (nlp) tasks @xcite . in recent years, pre-trained language models (plms) have achieved great success in nlp since they learn generic knowledge from a large corpus and could serve as a knowledge base @xcite . considerable attention has been paid to exploring simile knowledge from plms to solve downstream simile tasks, such as recognition, interpretation, and generation @xcite . however, plms are known to suffer from hallucination problems @xcite @xcite , they could generate unrealistic or unfaithful information about the provided source content, which will impact their performance on downstream tasks. for example, when completing the blank in a simile sentence "are you feeling ill ? you are as as a ghost .", a plm may generate "creepy" instead of the expected shared property "pale". in this paper, we study how to explore more accurate and generic simile knowledge from plms. specifically, we first train plms with three main simile tasks @xcite . in this way, the plms can learn the shared semantic feature among different tasks and gain a better understanding of the simile knowledge. however, this understanding may be limited by the distribution ## related work in this section, we will introduce previous work related to this paper. ## our proposed method in this section, we formalize the simile recognition/simile interpretation/simile generation (sr/si/sg) tasks and introduce our method in detail. for a fair comparison with previous work @xcite , we use bert-base @xcite as the backbone of our model. figure 1 shows the model structure of sr/si/sg tasks. ## results and analysis in this section, we introduce the experimental results and provide our analysis of the results. ## conclusion we propose a novel method to explore more accurate and generic simile knowledge from plms. we fine-tune plm with three simile tasks (recognition, interpretation, and generation) to explore local simile knowledge between key simile components (tenor, shared property, vehicle). then we use the semantic dependency feature for global simile knowledge among different examples. this global simile knowledge can help our model perform well across domains. experiments with seen and unseen test sets verify the effectiveness of our method. our exploring method may be useful for other nlp tasks that leverage knowledge from plms. since our method does not need an expensive pre-training process, it may also be useful for leveraging more large-scaled plms. future works include but are not limited to 1) testing our method on other knowledge-intensive tasks; 2) verifying whether our method can be transferred to auto-regressive-based plms.