From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture

• URL: http://arxiv.org/abs/2004.03254v1
• Date: Tue, 7 Apr 2020 10:46:58 GMT
• Title: From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture
• Authors: Laurent Vanni, Marco Corneli, Damon Mayaffre, Fr\'ed\'eric Precioso
• Abstract summary: We propose a novel approach to inspect the hidden layers of a fitted CNN in order to extract interpretable linguistic objects from texts exploiting classification process. We empirically demonstrate the efficiency of our approach on corpora from two different languages: English and French.
• Score: 2.064612766965483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A lot of effort is currently made to provide methods to analyze and understand deep neural network impressive performances for tasks such as image or text classification. These methods are mainly based on visualizing the important input features taken into account by the network to build a decision. However these techniques, let us cite LIME, SHAP, Grad-CAM, or TDS, require extra effort to interpret the visualization with respect to expert knowledge. In this paper, we propose a novel approach to inspect the hidden layers of a fitted CNN in order to extract interpretable linguistic objects from texts exploiting classification process. In particular, we detail a weighted extension of the Text Deconvolution Saliency (wTDS) measure which can be used to highlight the relevant features used by the CNN to perform the classification task. We empirically demonstrate the efficiency of our approach on corpora from two different languages: English and French. On all datasets, wTDS automatically encodes complex linguistic objects based on co-occurrences and possibly on grammatical and syntax analysis.

Related papers

• LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model [20.007650672107566]
  Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. Recent methods track the zero-shot results of state-of-the-art image text spotters directly. Fine-tuning transformer-based text spotters on specific datasets could yield performance enhancements.
  arXiv  Detail & Related papers  (2024-05-29T15:35:09Z)
• Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation [26.262887028563163]
  Referring Image (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. We propose a novel bidirectional token-masking autoencoder (BTMAE) inspired by the masked autoencoder (MAE) BTMAE learns the context of image-to-language and language-to-image by reconstructing missing features in both image and language features at the token level.
  arXiv  Detail & Related papers  (2023-11-29T07:33:38Z)
• Contextual Object Detection with Multimodal Large Language Models [78.30374204127418]
  We introduce a novel research problem of contextual object detection. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. We present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts.
  arXiv  Detail & Related papers  (2023-05-29T17:50:33Z)
• Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks [18.12933868289846]
  We integrate the linguistic information into the vision-based U-Structure networks designed for salient object detection tasks. We propose a new module called efficient Cross-Modal Self-Attention (eCMSA) to combine visual and linguistic features. To reduce the heavy burden of labeling, we employ a semi-supervised learning method by training an image caption model.
  arXiv  Detail & Related papers  (2022-08-08T18:39:37Z)
• A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis [31.05169054736711]
  Cross-lingual structured sentiment analysis task aims to transfer the knowledge from source language to target one. We propose a Knowledge-Enhanced Adversarial Model (textttKEAM) with both implicit distributed and explicit structural knowledge. We conduct experiments on five datasets and compare textttKEAM with both the supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2022-05-31T03:07:51Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer [76.3906723777229]
  We present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset. In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models.
  arXiv  Detail & Related papers  (2021-07-06T15:41:32Z)
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision [57.031588264841]
  We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
  arXiv  Detail & Related papers  (2021-02-11T10:08:12Z)
• AttViz: Online exploration of self-attention for transparent neural language modeling [7.574392147428978]
  We propose AttViz, an online toolkit for exploration of self-attention---real values associated with individual text tokens. We show how existing deep learning pipelines can produce outputs suitable for AttViz, offering novel visualizations of the attention heads and their aggregations with minimal effort, online.
  arXiv  Detail & Related papers  (2020-05-12T12:21:40Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

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.