Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Han, Rujun; Ning, Qiang; Peng, Nanyun

doi:10.18653/v1/d19-1041

Cited by 88 publications

(89 citation statements)

References 29 publications

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“…When the need arises to go beyond sentencelevel, some works combine the output scores of independently trained classifiers using inference (Beltagy et al, 2014;? ;Liu et al, 2016;Subramanian et al, 2017;Ning et al, 2018), whereas others implement joint learning for their specific domains (Niculae et al, 2017;Han et al, 2019). Our main differentiating factor is that we provide a general interface that leverages first order logic clauses to specify factor graphs and express constraints.…”

Section: Deep Classifiers and Probabilistic Inferencementioning

confidence: 99%

“…Local vs. Global Learning: The trade-off between local and global learning has been explored for graphical models (MEMM vs. CRF), and for deep structured prediction (Chen and Manning, 2014;Andor et al, 2016;Han et al, 2019). Although local learning is faster, the learned scoring functions might not be consistent with the correct global prediction.…”

Section: Modeling Strategiesmentioning

confidence: 99%

“…Although local learning is faster, the learned scoring functions might not be consistent with the correct global prediction. Following (Han et al, 2019), we initialize the parameters using local models.…”

Section: Modeling Strategiesmentioning

confidence: 99%

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Modeling Content and Context with Deep Relational Learning

Pacheco

Goldwasser

2021

Transactions of the Association for Computational Linguistics

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Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

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Section: Deep Classifiers and Probabilistic Inferencementioning

confidence: 99%

Section: Modeling Strategiesmentioning

confidence: 99%

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Modeling Content and Context with Deep Relational Learning

Pacheco

Goldwasser

2021

Transactions of the Association for Computational Linguistics

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“…在文本关系建模方面, Schuster 等 [50] 对文本进行图结构建模, 挖掘文本的关系, 提高对图像的检索性能. Han 等 [51] 不仅利用图结构对文本进行建模, 还考虑了文本中事件发生的先后顺序. 本文借鉴了场景图这一结构来表示视觉与文本关系.…”

Section: 视觉关系或文本关系建模unclassified

Cross-modal video moment retrieval based on visual-textual relationship alignment

Chen¹,

Du²,

Wu³

et al. 2020

Sci. Sin.-Inf.

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In recent years, increasing amounts of video resources have created a series of demands for fine retrieval of video moments, such as highlight moments in sports events and the recreation of specific video content. In this context, research on cross-modal video segment retrieval, which attempts to output a video moment that matches the input query text, is gradually emerging. Existing solutions primarily focus on global or local feature representation for query text and video moments. However, such solutions ignore matching semantic relations contained in query text and video moments. For example, given the query text "a person is playing basketball", existing retrieval systems may incorrectly return a video moment of "a person holding a basketball" without the considering the semantic relationship of "a person playing basketball". Therefore, this paper proposes a crossmodal relationship alignment framework, which we refer to as CrossGraphAlign, for cross-modal video moment retrieval. The proposed framework constructs a textual relationship graph and a visual relationship graph to model the query semantics in text and video segment relations, and then evaluates the similarity between text relations and visual relations through cross-modally aligned graph convolutional networks to help construct a more accurate video moment retrieval system. Experimental results on the publicly available cross-modal video retrieval datasets TACoS and ActivityNet Captions demonstrate that the proposed method can effectively utilize the semantic relationships to improve the recall rate in cross-modal video moment retrieval.

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“…Yoshikawa et al (2009); Ning et al (2017); Leeuwenberg and Moens (2017) explore structured learning for this task, and more recently, neural methods have also been shown effective (Tourille et al, 2017;Cheng and Miyao, 2017;Meng et al, 2017;Meng and Rumshisky, 2018). Ning et al (2018c) and Han et al (2019b) are the most recent work leveraging neural network and pre-trained language models to build an end-to-end system. Our work differs from these prior work in that we build a structured neural model with distributional constraints that combines both the benefits of both deep learning and domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

Deep Structured Neural Network for Event Temporal Relation Extraction

Han¹,

Hsu²,

Yang³

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns representations that account for long-term contexts to provide robust features for the structured model, while the SSVM incorporates domain knowledge such as transitive closure of temporal relations as constraints to make better globally consistent decisions. By jointly training the two components, our model combines the benefits of both data-driven learning and knowledge exploitation. Experimental results on three highquality event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that incorporated with pre-trained contextualized embeddings, the proposed model achieves significantly better performances than the stateof-the-art methods on all three datasets. We also provide thorough ablation studies to investigate our model.

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Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Cited by 88 publications

References 29 publications

Modeling Content and Context with Deep Relational Learning

Modeling Content and Context with Deep Relational Learning

Cross-modal video moment retrieval based on visual-textual relationship alignment

Deep Structured Neural Network for Event Temporal Relation Extraction

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