Challenge: Existing studies on the topic of common sense story understanding focus on generating guesses for a missing event or concentrating on unsupervised learning.
Approach: They propose to extend attention-based neural network with external knowledge resources to understand temporal stories and predict their endings.
Outcome: The proposed model outperforms state-of-the-art models and external knowledge resources.

Similar Papers

Narrative Modeling with Memory Chains and Semantic Supervision (P18-2)

Copied to clipboard

Challenge: Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task.
Approach: They propose a method that tracks various semantic aspects with external neural memory chains . they propose to encourage each to focus on a particular semantic aspect .
Outcome: The proposed method outperforms baselines on the task of story ending prediction.
A Hierarchical Neural Attention-based Text Classifier (D18-1)

Copied to clipboard

Challenge: Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus.
Approach: They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents.
Outcome: The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)

Copied to clipboard

Challenge: Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction).
Approach: They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model.
Outcome: The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations.
Attention-based Conditioning Methods for External Knowledge Integration (P19-1)

Copied to clipboard

Challenge: Existing approaches for incorporating external knowledge into deep neural networks (RNNs) lexicon features are used to concatenate external information into the input or hidden network layers.
Approach: They propose a method for conditioning external knowledge into RNNs by concatenating a representation of the external information to the input or hidden network layers.
Outcome: The proposed approach improves performance on six benchmark datasets.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)

Copied to clipboard

Challenge: Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence.
Approach: They propose an interactive multi-task learning network which can learn multiple tasks simultaneously . they use a shared set of latent variables to iteratively pass information between tasks .
Outcome: The proposed method outperforms existing methods on three benchmark datasets.
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)

Copied to clipboard

Challenge: Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories.
Approach: They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges.
Outcome: The proposed model outperforms baselines on automatic and human evaluation.
Improving Relation Extraction with Knowledge-attention (D19-1)

Copied to clipboard

Challenge: Existing attention mechanisms are data-driven, but most are data driven.
Approach: They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task.
Outcome: The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset.
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (N18-2)

Copied to clipboard

Challenge: Existing sentiment analysis systems are prone to word shortening, exaggeration, lack of grammar and appropriate punctuation.
Approach: They propose a two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis using the Knowledge Graph Embedding generated using the WordNet.
Outcome: The proposed model outperforms the state-of-the-art system on the benchmark dataset of SemEval 2017 Task 5 by 1.7 and 3.7 points respectively.
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge (P18-1)

Copied to clipboard

Challenge: a new model for reading comprehension integrates external commonsense knowledge . cloze-style reading comprehension is a language understanding task similar to question answering .
Approach: They propose a reading comprehension model that integrates external commonsense knowledge in a cloze-style setting.
Outcome: The proposed model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of more complex models.
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)

Copied to clipboard

Challenge: Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs.
Approach: They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information.
Outcome: The proposed method outperforms a general GNN-based approach for KGC.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations