Matching Text with Deep Mutual Information Estimation

• URL: http://arxiv.org/abs/2003.11521v1
• Date: Mon, 9 Mar 2020 15:25:37 GMT
• Title: Matching Text with Deep Mutual Information Estimation
• Authors: Xixi Zhou (1), Chengxi Li (1), Jiajun Bu (1), Chengwei Yao (1), Keyue Shi (1), Zhi Yu (1), Zhou Yu (2) ((1) Zhejiang University, (2) University of California, Davis)
• Abstract summary: We present a neural approach for general-purpose text matching with deep mutual information estimation incorporated. Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations. We evaluate our text matching approach on several tasks including natural language inference, paraphrase identification, and answer selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text matching is a core natural language processing research problem. How to retain sufficient information on both content and structure information is one important challenge. In this paper, we present a neural approach for general-purpose text matching with deep mutual information estimation incorporated. Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output. We use both global and local mutual information to learn text representations. We evaluate our text matching approach on several tasks including natural language inference, paraphrase identification, and answer selection. Compared to the state-of-the-art approaches, the experiments show that our method integrated with mutual information estimation learns better text representation and achieves better experimental results of text matching tasks without exploiting pretraining on external data.

Related papers

• Towards Unified Multi-granularity Text Detection with Interactive Attention [56.79437272168507]
  "Detect Any Text" is an advanced paradigm that unifies scene text detection, layout analysis, and document page detection into a cohesive, end-to-end model. A pivotal innovation in DAT is the across-granularity interactive attention module, which significantly enhances the representation learning of text instances. Tests demonstrate that DAT achieves state-of-the-art performances across a variety of text-related benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T07:25:23Z)
• KETM:A Knowledge-Enhanced Text Matching method [0.0]
  We introduce a new model for text matching called the Knowledge Enhanced Text Matching model (KETM) We use Wiktionary to retrieve the text word definitions as our external knowledge. We fuse text and knowledge using a gating mechanism to learn the ratio of text and knowledge fusion.
  arXiv  Detail & Related papers  (2023-08-11T17:08:14Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Uzbek text summarization based on TF-IDF [0.0]
  This article presents an experiment on summarization task for Uzbek language. The methodology was based on text abstracting based on TF-IDF algorithm. We summarize the given text by applying the n-gram method to important parts of the whole text.
  arXiv  Detail & Related papers  (2023-03-01T12:39:46Z)
• Universal Multimodal Representation for Language Understanding [110.98786673598015]
  This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively.
  arXiv  Detail & Related papers  (2023-01-09T13:54:11Z)
• Open-set Text Recognition via Character-Context Decoupling [16.2819099852748]
  The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limited performance for current methods is the confounding effect of contextual information over the visual information of individual characters. A Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information.
  arXiv  Detail & Related papers  (2022-04-12T05:43:46Z)
• DEIM: An effective deep encoding and interaction model for sentence matching [0.0]
  We propose a sentence matching method based on deep encoding and interaction to extract deep semantic information. In the encoder layer,we refer to the information of another sentence in the process of encoding a single sentence, and later use a algorithm to fuse the information. In the interaction layer, we use a bidirectional attention mechanism and a self-attention mechanism to obtain deep semantic information.
  arXiv  Detail & Related papers  (2022-03-20T07:59:42Z)
• Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation [49.89831914386982]
  We propose a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. Our approach outperforms the pre-training of plain text using only 1/4 of the data.
  arXiv  Detail & Related papers  (2021-09-02T16:05:24Z)
• Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge [62.46091695615262]
  We aim to extract commonsense knowledge to improve machine reading comprehension. We propose to represent relations implicitly by situating structured knowledge in a context. We employ a teacher-student paradigm to inject multiple types of contextualized knowledge into a student machine reader.
  arXiv  Detail & Related papers  (2020-09-12T17:20:01Z)
• TRIE: End-to-End Text Reading and Information Extraction for Document Understanding [56.1416883796342]
  We propose a unified end-to-end text reading and information extraction network. multimodal visual and textual features of text reading are fused for information extraction. Our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-05-27T01:47:26Z)

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.