| Challenge: | Knowledge Bases (KBs) are widely used for representing information in a structured format. |
| Approach: | They propose a method to measure Demand-Weighted Completeness by defining an entity by its classes and using usage data to predict relation distributions. |
| Outcome: | The proposed method can be used to estimate completeness of knowledge bases based on how they are used and can quantify usage and completeness changes over time. |
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Knowledge Base Completion Meets Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge bases are tedious and require a large amount of labor to build. |
| Approach: | They propose a method that allows for transfer of knowledge from one collection of facts to another without entity or relation matching. |
| Outcome: | The proposed method is the most impactful on small datasets, showing a 6% increase in rank and 65% decrease in rank over the previous best method. |
Fact Discovery from Knowledge Base via Facet Decomposition (N19-1)
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| Challenge: | Recent years have witnessed the emergence and growth of many large-scale knowledge bases (KBs) however, there are some issues unsettled towards enriching the KBs. |
| Approach: | They propose a framework that decomposes the discovery problem into several facet components and an auto-encoder component to estimate some facets of the fact. |
| Outcome: | The proposed framework achieves promising results on a benchmark dataset. |
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| 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. |
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols (2020.emnlp-main)
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| Challenge: | Existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. |
| Approach: | They propose a method that integrates entities, relations and time into a uniform space . they propose improved evaluation protocols for link and time prediction . |
| Outcome: | The proposed method exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations yielding state-of-the-art results. |
Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)
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| Challenge: | Existing knowledge graph completion models only evaluated candidate triples from content information. |
| Approach: | They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation. |
| Outcome: | The proposed model improves on two representative datasets and improves performance. |
Quantifying Similarity between Relations with Fact Distribution (P19-1)
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| Challenge: | a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks. |
| Approach: | They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs . |
| Outcome: | The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes. |
Automatic Construction of Enterprise Knowledge Base (2021.emnlp-demo)
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| Challenge: | Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia. |
| Approach: | They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques. |
| Outcome: | The proposed system is currently serving as part of a Microsoft 365 service. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Modeling and Prediction of Online Product Review Helpfulness: A Survey (P18-1)
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| Challenge: | review helpfulness modeling is a task that studies the mechanisms that affect review helpfuliness and attempts to accurately predict it. |
| Approach: | This paper provides an overview of the most relevant work in helpfulness prediction . it discusses the insights gained from said work and provides guidelines for future research . |
| Outcome: | This paper summarizes the most relevant work in helpfulness prediction and understanding in the past decade . it outlines the insights gained from the results and provides guidelines for future research . |