Temporal Embeddings and Transformer Models for Narrative Text Understanding

• URL: http://arxiv.org/abs/2003.08811v1
• Date: Thu, 19 Mar 2020 14:23:12 GMT
• Title: Temporal Embeddings and Transformer Models for Narrative Text Understanding
• Authors: Vani K and Simone Mellace and Alessandro Antonucci
• Abstract summary: We present two approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters.
• Score: 72.88083067388155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. An empirical analysis of the corresponding character trajectories shows that such approaches are effective in depicting dynamic evolution. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters. The empirical validation shows that such events (e.g., two characters belonging to the same family) might be spotted with good accuracy, even when using automatically annotated data. This provides a deeper understanding of narrative plots based on the identification of key facts. Standard clustering techniques are finally used for character de-aliasing, a necessary pre-processing step for both approaches. Overall, deep learning models appear to be suitable for narrative text understanding, while also providing a challenging and unexploited benchmark for general natural language understanding.

Related papers

• Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing [71.29488677105127]
  Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. We propose a contrastive learning-based STR framework by leveraging synthetic and real unlabeled data without any human cost. Our method achieves SOTA performance (94.7% and 70.9% average accuracy on common benchmarks and Union14M-Benchmark.
  arXiv  Detail & Related papers  (2024-11-23T15:24:47Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution [1.3654846342364308]
  State-of-the-art approaches usually involve fine-tuning models on large annotated datasets, which are costly to produce. We propose and release a qualitative and versatile few-shot learning methodology as a common paradigm for any claim-based textual classification task. We illustrate this methodology in the context of three tasks: climate change contrarianism detection, topic/stance classification and depression-relates symptoms detection.
  arXiv  Detail & Related papers  (2024-05-09T12:03:38Z)
• Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework [68.1678127433077]
  We extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines.
  arXiv  Detail & Related papers  (2022-08-08T09:02:19Z)
• Unsupervised Mismatch Localization in Cross-Modal Sequential Data [5.932046800902776]
  We develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal data. We propose a hierarchical Bayesian deep learning model, named mismatch localization variational autoencoder (ML-VAE), that decomposes the generative process of the speech into hierarchically structured latent variables. Our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations.
  arXiv  Detail & Related papers  (2022-05-05T14:23:27Z)
• Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment [17.882688516249058]
  We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. Our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.
  arXiv  Detail & Related papers  (2021-07-09T07:26:17Z)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)
• Syntax-Enhanced Pre-trained Model [49.1659635460369]
  We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. We present a model that utilizes the syntax of text in both pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2020-12-28T06:48:04Z)

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.