Exploiting BERT For Multimodal Target SentimentClassification Through Input Space Translation

• URL: http://arxiv.org/abs/2108.01682v1
• Date: Tue, 3 Aug 2021 18:02:38 GMT
• Title: Exploiting BERT For Multimodal Target SentimentClassification Through Input Space Translation
• Authors: Zaid Khan and Yun Fu
• Abstract summary: We introduce a two-stream model that translates images in input space using an object-aware transformer. We then leverage the translation to construct an auxiliary sentence that provides multimodal information to a language model. We achieve state-of-the-art performance on two multimodal Twitter datasets.
• Score: 75.82110684355979
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal target/aspect sentiment classification combines multimodal sentiment analysis and aspect/target sentiment classification. The goal of the task is to combine vision and language to understand the sentiment towards a target entity in a sentence. Twitter is an ideal setting for the task because it is inherently multimodal, highly emotional, and affects real world events. However, multimodal tweets are short and accompanied by complex, possibly irrelevant images. We introduce a two-stream model that translates images in input space using an object-aware transformer followed by a single-pass non-autoregressive text generation approach. We then leverage the translation to construct an auxiliary sentence that provides multimodal information to a language model. Our approach increases the amount of text available to the language model and distills the object-level information in complex images. We achieve state-of-the-art performance on two multimodal Twitter datasets without modifying the internals of the language model to accept multimodal data, demonstrating the effectiveness of our translation. In addition, we explain a failure mode of a popular approach for aspect sentiment analysis when applied to tweets. Our code is available at \textcolor{blue}{\url{https://github.com/codezakh/exploiting-BERT-thru-translation}}.

Related papers

• M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets [4.478789600295492]
  This paper transforms an existing textual Twitter sentiment dataset into a multimodal format through a straightforward curation process. Our work opens up new avenues for sentiment-related research within the research community.
  arXiv  Detail & Related papers  (2024-04-02T09:11:58Z)
• A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking [17.847936914174543]
  Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia. We formulate multimodal entity linking as a neural text matching problem where each multimodal information (text and image) is treated as a query. This paper introduces a dual-way enhanced (DWE) framework for MEL.
  arXiv  Detail & Related papers  (2023-12-19T03:15:50Z)
• TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild [102.93338424976959]
  We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models.
  arXiv  Detail & Related papers  (2023-09-14T15:34:01Z)
• Kosmos-2: Grounding Multimodal Large Language Models to the World [107.27280175398089]
  We introduce Kosmos-2, a Multimodal Large Language Model (MLLM) It enables new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world. Code and pretrained models are available at https://aka.ms/kosmos-2.
  arXiv  Detail & Related papers  (2023-06-26T16:32:47Z)
• TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models [5.668457303716451]
  We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks. Our approach significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-03-27T17:54:32Z)
• Language Is Not All You Need: Aligning Perception with Language Models [110.51362453720458]
  We introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context, and follow instructions. We train Kosmos-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. Experimental results show that Kosmos-1 achieves impressive performance on (i) language understanding, generation, and even OCR-free NLP. We also show that MLLMs can benefit from cross-modal transfer, i.e., transfer knowledge from language to multimodal, and from multimodal to language
  arXiv  Detail & Related papers  (2023-02-27T18:55:27Z)
• ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training [40.05046655477684]
  ERNIE-ViL 2.0 is a Multi-View Contrastive learning framework to build intra-modal and inter-modal correlations between diverse views simultaneously. We construct sequences of object tags as a special textual view to narrow the cross-modal semantic gap on noisy image-text pairs. ERNIE-ViL 2.0 achieves competitive results on English cross-modal retrieval.
  arXiv  Detail & Related papers  (2022-09-30T07:20:07Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation [77.69102711230248]
  We propose the use of a multilingual transformer model, that we pre-train over English tweets and apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.
  arXiv  Detail & Related papers  (2020-10-07T15:44:55Z)

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.