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Found 14 papers, 7 papers with code
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding
Experiments show that a state-of-the-art BERT-based model suffers performance loss under this drift.
Unsupervised Multi-View Post-OCR Error Correction With Language Models
We investigate post-OCR correction in a setting where we have access to different OCR views of the same document.
Improving Wikipedia Verifiability with AI
Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort.
The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise.
LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state-of-the-art that does mention detection and entity disambiguation jointly.
Ranked #9 on
Entity Linking
on AIDA-CoNLL
(using extra training data)
OPIEC: An Open Information Extraction Corpus
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
A Relational Tucker Decomposition for Multi-Relational Link Prediction
We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs.
On Evaluating Embedding Models for Knowledge Base Completion
In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion.
Learning Distributional Token Representations from Visual Features
In summary, we show that it is possible to achieve a \textit{text representation} only from pixels.
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain.
