Papers by Frank Mtumbuka
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains (2024.eacl-long)
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| Challenge: | Entity typing is the task of assigning semantic types to entities mentioned in text. |
| Approach: | They propose to pre-train an entity encoder such that embeddings of coreferring entities are more similar to each other. |
| Outcome: | The proposed method improves state-of-the-art on fine-grained entity typing and entity extraction. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open information extraction (OIE) is the task of extracting facts from natural language text. |
| Approach: | They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%. |
| Outcome: | The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets. |
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction (2020.emnlp-main)
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| Challenge: | Open information extraction (OIE) is a method for extracting facts from text in structured format . alternative formulations allow for longer tuples, but most work focuses on binary predicates only. |
| Approach: | They propose to extract facts from natural language text and represent them as structured triples . they compare different neural network architectures and training approaches . |
| Outcome: | The proposed approach improves the currently best models on the OIE16 benchmark by 0.421 F1 score and 0.420 AUC-PR . |
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence (2022.findings-naacl)
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| Challenge: | Recent evidence shows that large-size pre-trained language models do not satisfy the logical negation property (LNP) However, their reliability is being challenged due to faulty behaviours and incomprehension on number-related representations. |
| Approach: | They propose a new intermediate training task to directly learn meaning text correspondence instead of relying on the distributional hypothesis. |
| Outcome: | The proposed approach outperforms previous models on 7 GLUE tasks and outperformed previous models. |