I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023)

Abstract: A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The tenor and the vehicle are usually connected with comparator words such as “like” or “as”. The simile phenomena are unique and complex in a real-life dialogue scene where the tenor and the vehicle can be verbal phrases or sentences, mentioned by different speakers, exist in different sentences, or occur in reversed order. However, the current simile research usually focuses on similes in a triplet tuple (tenor, property, vehicle) or a single sentence where the tenor and vehicle are usually entities or noun phrases, which could not reflect complex simile phenomena in real scenarios. In this paper, we propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena. The MSD is the largest manually annotated simile data (~21K) and it contains both English and Chinese data. Meanwhile, the MSD data can also be used on dialogue tasks to test the ability of dialogue systems when using similes. We design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each task, we provide experimental results from strong pre-trained or state-of-the-art models. The experiments demonstrate the challenge of MSD and we will release the data/code on GitHub.

## introduction simile plays an important role in human language to make utterances more vivid, interesting, and graspable @xcite and is an increasingly studied phenomenon in computational linguistics @xcite . 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 the current simile research usually focuses on the simile in a triplet (tenor, shared property, vehicle) @xcite or a single sentence @xcite @xcite . for example, the simile recognition @xcite task is judging whether a sentence contains a simile, such as distinguishing which of the first and second examples in to facilitate the simile study, we release a humanannotated, high-quality simile dialogue dataset, which contains both english and chinese data. the complex simile phenomena in real-life dialogue scenarios not only bring more difficulties to traditional simile tasks such as recognition, interpretation @xcite , and generation @xcite but also raise challenges for dialogue research, e.g. generation and retrieval tasks. to address the simile dialogue tasks, dialogue models need to understand the simile relations between entities/phrases/sentences. our contributions are: for simile research, we design the dialogue simile recognition/interpretation/generation tasks. for dialogue research, we design the response retrieval and generation tasks. ## multilingual simile dialogue dataset in this section, we introduce the collection, annotation, and statistics of our msd data. ## conclusion we propose manually annotated multilingual simile dialogue (msd) data for both simile and dialogue research. we design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with msd. experiments with strong baselines show the challenge of each task. future works include but are not limited to 1) dataset enlargement (e.g., more annotated examples with more kinds of comparators); 2) model designing (e.g., models with a specific structure to address the proposed tasks); 3) new task designing (e.g., detecting tenor in the coarse/fine data). we encourage using the msd in future simile and dialogue research.