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Matching a target text to a source text based on their meaning.
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Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
We introduce 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.
OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION TEXT MATCHING
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
• google-research/google-research
• 
In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.
INFORMATION RETRIEVAL LANGUAGE MODELLING NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING REPRESENTATION LEARNING TEXT MATCHING
Text Matching as Image Recognition
An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
CVPR 2018
• taoxugit/AttnGAN
• 
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Ranked #3 on
Text-to-Image Generation
on COCO
(SOA-C metric)
UNITER: UNiversal Image-TExt Representation Learning
ECCV 2020
• ChenRocks/UNITER
•
Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).
Ranked #1 on
Visual Reasoning
on NLVR2 Test
LANGUAGE MODELLING QUESTION ANSWERING REFERRING EXPRESSION COMPREHENSION REPRESENTATION LEARNING TEXT MATCHING VISUAL COMMONSENSE REASONING VISUAL ENTAILMENT VISUAL QUESTION ANSWERING
Stacked Cross Attention for Image-Text Matching
Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable.
Ranked #5 on
Cross-Modal Retrieval
on Flickr30k
Extractive Summarization as Text Matching
ACL 2020
• maszhongming/MatchSum
•
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Extractive Text Summarization
on CNN / Daily Mail
DOCUMENT SUMMARIZATION EXTRACTIVE SUMMARIZATION EXTRACTIVE TEXT SUMMARIZATION SEMANTIC TEXT MATCHING TEXT MATCHING
Simple and Effective Text Matching with Richer Alignment Features
ACL 2019
• hitvoice/RE2
•
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Ranked #3 on
Question Answering
on WikiQA
ANSWER SELECTION NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION TEXT MATCHING
Keyword-Attentive Deep Semantic Matching
• DataTerminatorX/Keyword-BERT
•
Deep Semantic Matching is a crucial component in various natural language processing applications such as question and answering (QA), where an input query is compared to each candidate question in a QA corpus in terms of relevance.
Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching
• BangLiu/ArticlePairMatching
•
That is because there are usually many noises in the setting of long-form text matching, and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information.
COMMUNITY QUESTION ANSWERING INFORMATION RETRIEVAL SEMANTIC TEXT MATCHING SENTENCE SIMILARITY TEXT MATCHING