Papers with LEA

4 papers
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification (2022.naacl-main)

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Challenge: Existing few-shot text classification methods often lack labeled data in real-world tasks.
Approach: They propose a meta-learning method that encodes how to attend for given tasks . they evaluate the method on five benchmark datasets and show it is competitive .
Outcome: The proposed method performs better on five benchmark datasets than previous methods on labeled data.
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)

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Challenge: Existing systems for coreference resolution are difficult because of their long coreferent chains.
Approach: They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system .
Outcome: The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task.
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets (2023.acl-long)

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Challenge: Anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets.
Approach: They propose an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements on seven public Darknet markets.
Outcome: The proposed approach can help law enforcement agencies make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.
Multilingual Coreference Resolution in Low-resource South Asian Languages (2024.lrec-main)

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Challenge: Existing coreference resolution models for South Asian languages are limited . a a sanity check for the prediction of translations is required to ensure accuracy of the model, authors say .
Approach: They evaluate an end-to-end coreference resolution model on a Hindi golden set . they use translation and word-alignment tools to translate a translated dataset into 31 languages .
Outcome: The proposed model scored 64 and 68 on a Hindi golden set.

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