Papers by Weijian Xie

4 papers
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)

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Challenge: Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples.
Approach: They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term .
Outcome: The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets.
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction (2020.emnlp-main)

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Challenge: Existing relation extraction methods require centralizing training data from different medical platforms while holding the privacy-sensitive data puts patients' privacy at risk.
Approach: They propose a federated relation extraction model that trains a central model without sharing or exchange of private local data.
Outcome: The proposed model trains a central model without uploading local parameters, and it performs well on three publicly available datasets.
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

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Challenge: Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach: They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome: The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

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Challenge: Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach: They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome: The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

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