Papers by Samuel Broscheit
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction (2020.acl-main)
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| Challenge: | Existing methods for predicting knowledge graphs rely on the rich structure of the knowledge graph. |
| Approach: | They propose an evaluation protocol and a methodology for creating the open link prediction benchmark OlpBench. |
| Outcome: | The proposed model predicts test facts by completing questions in open link prediction task. |
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding (2022.acl-long)
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| Challenge: | Covariate drift can occur when there is a drift between training and testing regarding what users request or how they request it. |
| Approach: | They propose a method that exploits natural variations in data to create a covariate drift in spoken language understanding datasets. |
| Outcome: | The proposed method improves robustness against covariate drift in spoken language understanding (SLU) it shows that a state-of-the-art model suffers performance loss under this drift. |
LibKGE - A knowledge graph embedding library for reproducible research (2020.emnlp-demos)
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| Challenge: | Knowledge graph embedding models are trained to predict false triples and high scores for true triples. |
| Approach: | LibKGE is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. |
| Outcome: | LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. |
Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
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| Challenge: | Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information. |
| Approach: | They propose to use a pretrained language model to reconcile different OCR views in unsupervised way so that their combination contains fewer errors than each individual view. |
| Outcome: | The proposed model can reconcile multiple OCR views so that their combined version contains fewer errors than the best OCR view. |