I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset

• URL: http://arxiv.org/abs/2306.05672v2
• Date: Fri, 18 Oct 2024 09:12:45 GMT
• Title: I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset
• Authors: Longxuan Ma, Weinan Zhang, Shuhan Zhou, Churui Sun, Changxin Ke, Ting Liu,
• Abstract summary: A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The current simile research usually focuses on similes in a triplet (tenor, property, vehicle) or a single sentence. We propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena.
• Score: 26.42431190718335
• License:
• 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 ($\sim$20K) 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 have released the data/code on GitHub.

Related papers

• Toward Joint Language Modeling for Speech Units and Text [89.32163954508489]
  We explore joint language modeling for speech units and text. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks.
  arXiv  Detail & Related papers  (2023-10-12T20:53:39Z)
• A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection [27.85834441076481]
  Hyperbole and metaphor are common in day-to-day communication. Existing approaches to automatically detect metaphor and hyperbole have studied these language phenomena independently. We propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously.
  arXiv  Detail & Related papers  (2023-05-27T14:17:59Z)
• Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation [51.22712675266523]
  Dialogue Topic (DTS) plays an essential role in a variety of dialogue modeling tasks. We propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data.
  arXiv  Detail & Related papers  (2023-05-04T11:35:23Z)
• A Benchmark for Understanding and Generating Dialogue between Characters in Stories [75.29466820496913]
  We present the first study to explore whether machines can understand and generate dialogue in stories. We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
  arXiv  Detail & Related papers  (2022-09-18T10:19:04Z)
• M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database [139.08528216461502]
  We propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED. M3ED contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. To the best of our knowledge, M3ED is the first multimodal emotional dialogue dataset in Chinese.
  arXiv  Detail & Related papers  (2022-05-09T06:52:51Z)
• MSCTD: A Multimodal Sentiment Chat Translation Dataset [66.81525961469494]
  We introduce a new task named Multimodal Chat Translation (MCT) MCT aims to generate more accurate translations with the help of the associated dialogue history and visual context. Our work can facilitate research on both multimodal chat translation and multimodal dialogue sentiment analysis.
  arXiv  Detail & Related papers  (2022-02-28T09:40:46Z)
• We've had this conversation before: A Novel Approach to Measuring Dialog Similarity [9.218829323265371]
  We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants.
  arXiv  Detail & Related papers  (2021-10-12T07:24:12Z)
• Who says like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning [2.251583286448503]
  We focus on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. We employ multi-task learning of both syntax-aware information and dialogue summarization.
  arXiv  Detail & Related papers  (2021-09-29T05:30:39Z)
• Writing Polishment with Simile: Task, Dataset and A Neural Approach [9.38000305423665]
  We propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Our model firstly locates where the simile should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile dataset containing 5 million similes with context.
  arXiv  Detail & Related papers  (2020-12-15T06:39:54Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.