Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes

• URL: http://arxiv.org/abs/2203.03835v1
• Date: Tue, 8 Mar 2022 03:54:02 GMT
• Title: Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes
• Authors: Hua Lu, Zhen Guo, Chanjuan Li, Yunyi Yang, Huang He, Siqi Bao
• Abstract summary: This paper presents our solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of DSTC10. We leverage a large-scale pre-trained dialogue model for coherent and informative response generation. Based on interaction-based text-matching, our approach can retrieve appropriate memes with good generalization ability.
• Score: 19.57042922215698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Internet memes have been widely used in online chatting. Compared with text-based communication, conversations become more expressive and attractive when Internet memes are incorporated. This paper presents our solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of DSTC10, where three tasks are involved: text response modeling, meme retrieval, and meme emotion classification. Firstly, we leverage a large-scale pre-trained dialogue model for coherent and informative response generation. Secondly, based on interaction-based text-matching, our approach can retrieve appropriate memes with good generalization ability. Thirdly, we propose to model the emotion flow (EF) in conversations and introduce an auxiliary task of emotion description prediction (EDP) to boost the performance of meme emotion classification. Experimental results on the MOD dataset demonstrate that our methods can incorporate Internet memes into dialogue systems effectively.

Related papers

• Meme Similarity and Emotion Detection using Multimodal Analysis [0.0]
  This study employs a multimodal methodological approach, analyzing both the visual and textual elements of memes. We extract low-level visual features and high-level semantic features to identify similar meme pairs. Results indicate that anger and joy are the dominant emotions in memes, with motivational memes eliciting stronger emotional responses.
  arXiv  Detail & Related papers  (2025-03-21T19:07:16Z)
• Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes [5.243460995467895]
  This study introduces ClassicMemes-50-templates (CM50), a large-scale dataset consisting of over 33,000 memes, centered around 50 popular meme templates. We also present an automated knowledge-grounded annotation pipeline leveraging large vision-language models to produce high-quality image captions, meme captions, and literary device labels.
  arXiv  Detail & Related papers  (2025-01-23T17:18:30Z)
• XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
  Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines. We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning. textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 3.71% and 4.82%, respectively.
  arXiv  Detail & Related papers  (2024-07-24T10:51:46Z)
• Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation [55.043492250775294]
  We introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response. We also introduce MultiDialog, the first large-scale multimodal spoken dialogue corpus.
  arXiv  Detail & Related papers  (2024-06-12T04:48:36Z)
• MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing [53.30190591805432]
  We introduce MemeMQA, a multimodal question-answering framework to solicit accurate responses to structured questions. We also propose ARSENAL, a novel two-stage multimodal framework to address MemeMQA.
  arXiv  Detail & Related papers  (2024-05-18T07:44:41Z)
• Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations [48.82168723932981]
  We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
  arXiv  Detail & Related papers  (2024-01-18T11:24:30Z)
• Contextualizing Internet Memes Across Social Media Platforms [8.22187358555391]
  We investigate whether internet memes can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG -- a recently released knowledge graph of internet memes.
  arXiv  Detail & Related papers  (2023-11-18T20:18:18Z)
• A Template Is All You Meme [83.05919383106715]
  We release a knowledge base of memes and information found on www.knowyourmeme.com, composed of more than 54,000 images. We hypothesize that meme templates can be used to inject models with the context missing from previous approaches.
  arXiv  Detail & Related papers  (2023-11-11T19:38:14Z)
• MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization [31.209594252045566]
  We propose a novel task, MEMEX, given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme. To benchmark MCC, we propose MIME, a multimodal neural framework that uses common sense enriched meme representation and a layered approach to capture the cross-modal semantic dependencies between the meme and the context.
  arXiv  Detail & Related papers  (2023-05-25T10:19:35Z)
• Detecting and Understanding Harmful Memes: A Survey [48.135415967633676]
  We offer a comprehensive survey with a focus on harmful memes. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual.
  arXiv  Detail & Related papers  (2022-05-09T13:43:27Z)
• Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark [28.255324166852535]
  We propose a new task named as textbfMeme incorporated textbfOpen-domain textbfDialogue (MOD) MOD is much more challenging since it requires the model to understand the multimodal elements as well as the emotions behind them. We construct a large-scale open-domain multimodal dialogue dataset incorporating abundant Internet memes into utterances.
  arXiv  Detail & Related papers  (2021-09-04T10:39:52Z)
• Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification [0.16863755729554888]
  We try to explore the significance of visual features of images in classifying memes. We try to incorporate the memes as troll and non-trolling memes based on the images and the text on them.
  arXiv  Detail & Related papers  (2021-08-09T09:04:42Z)
• Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset [47.65948529524281]
  We collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background.
  arXiv  Detail & Related papers  (2021-07-09T09:04:05Z)
• SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor! [20.55903557920223]
  The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes, with human-annotated labels namely sentiment (positive, negative, neutral), type of emotion (sarcastic, funny, offensive, motivation) and corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humour, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion.
  arXiv  Detail & Related papers  (2020-08-09T18:17:33Z)

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.