natural-language-processing-9

Text Matching

3260 papers • 126 benchmarks • 313 datasets

Matching a target text to a source text based on their meaning.

(Image credit: Papersgraph)

Benchmarks

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Datasets

COVID-19 Twitter Chatter Dataset

Composed Quora

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Most implemented papers

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Zhe Gan, Xiaodong He, Pengchuan Zhang, Xiaolei Huang, Han Zhang, Tao Xu, Qiuyuan Huang•Mon Nov 27 2017

An Attentional Generative Adversarial Network that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation and for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.

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Text Matching as Image Recognition

J. Guo, Yanyan Lan, Xueqi Cheng, Liang Pang, Jun Xu, Shengxian Wan•Thu Feb 11 2016

This model, which resembles the compositional hierarchies of patterns in image recognition, can successfully identify salient signals such as n-gram and n-term matchings and demonstrates its superiority against the baselines.

574 0

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UNITER: UNiversal Image-TExt Representation Learning

Zhe Gan, Linjie Li, Licheng Yu, Yen-Chun Chen, Jingjing Liu, Yu Cheng, Ahmed El Kholy, Faisal Ahmed•Tue Sep 24 2019

UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets is introduced, which can power heterogeneous downstream V+L tasks with joint multimodal embeddings.

2505 0

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Stacked Cross Attention for Image-Text Matching

Xiaodong He, Houdong Hu, G. Hua, Kuang-Huei Lee, Xi Chen•Tue Mar 20 2018

Stacked Cross Attention to discover the full latent alignments using both image regions and words in sentence as context and infer the image-text similarity achieves the state-of-the-art results on the MS-COCO and Flickr30K datasets.

1309 0

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Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

Hannaneh Hajishirzi, Danqi Chen, Sewon Min, Luke Zettlemoyer•Sat Nov 09 2019

This work introduces an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.

111 0

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Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning

Qi Wu, Shizhe Chen, Qin Jin, Yida Zhao•Sat Feb 29 2020

A Hierarchical Graph Reasoning (HGR) model is proposed, which decomposes video-text matching into global-to-local levels and generates hierarchical textual embeddings via attention-based graph reasoning.

360 0

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Simple and Effective Text Matching with Richer Alignment Features

Feng Ji, Haiqing Chen, Runqi Yang, Jianhai Zhang, Xing Gao•Sun Jun 30 2019

A fast and strong neural approach for general purpose text matching applications and proposes to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components.

164 0

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DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis

Changsheng Xu, Hao Tang, Ming Tao, Fei Wu, Xiaoyuan Jing, Bingkun Bao•Wed Aug 12 2020

Compared with current state-of-the-art methods, the proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images and achieves better performance on widely used datasets.

277 0

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Dual Attention Networks for Multimodal Reasoning and Matching

Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim•Tue Nov 01 2016

This work proposes Dual Attention Networks which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language and introduces two types of DANs for multimodal reasoning and matching, respectively.

703 0

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Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking

Heng Tao Shen, Tan Wang, Jingkuan Song, Xing Xu, Yang Yang, A. Hanjalic•Sun Aug 11 2019

This work proposes a novel Multi-modal Tensor Fusion Network (MTFN) to explicitly learn an accurate image-text similarity function with rank-based tensor fusion rather than seeking a common embedding space for each image- text instance.

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