Paper Citation Record · LEDGER

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity

As of 9 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2507.15864.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.15864 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:01:28.785953Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy17
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e8110e3-63f9-480b-b234-b60fcbe6143a · outbound

This paper cites Template-based named entity recognition using BART.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Template-based named entity recognition using BART

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:31.683268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fffacbb7-2506-43dc-9f15-00b8e2d99470 · outbound

This paper cites Good examples make a faster learner: Sim- ple demonstration-based learning for low-resource ner.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Good examples make a faster learner: Sim- ple demonstration-based learning for low-resource ner

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:30.874778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 47933b35-c329-4c6f-8bac-e1a69b118de9 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:01:27.980962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e02c1bdd-15f9-41cd-b6c8-5867938717e7 · outbound

This paper cites GPT Understands, Too.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity GPT Understands, Too

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:01:28.027974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a4659be-6f57-4555-b5b3-6c12a6b4333e · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:30.351865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1eebe8a-e775-45bd-9517-0831ba2a619b · outbound

This paper cites [Ma et al., 2022b] Ruotian Ma, Xin Zhou, Tao Gui, Yid- ing Tan, Linyang Li, Qi Zhang, and Xuanjing Huang.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity [Ma et al., 2022b] Ruotian Ma, Xin Zhou, Tao Gui, Yid- ing Tan, Linyang Li, Qi Zhang, and Xuanjing Huang

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:30.007052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b8db49a7-0b9a-457d-952a-5a7b4be9b195 · outbound

This paper cites [Reimers and Gurevych, 2019] Nils Reimers and Iryna Gurevych.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity [Reimers and Gurevych, 2019] Nils Reimers and Iryna Gurevych

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.859904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 463f28ab-d4da-4811-9a48-bb769663e861 · outbound

This paper cites Introduction to the conll-2003 shared task: Language-independent named entity recognition.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Introduction to the conll-2003 shared task: Language-independent named entity recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.726365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f7e2b58-f5d0-4eaf-8b67-cd40b5fb6150 · outbound

This paper cites [Wang et al., 2022] Shuohang Wang, Yichong Xu, Yuwei Fang, Yang Liu, Siqi Sun, Ruochen Xu, Chenguang Zhu, and Michael Zeng.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity [Wang et al., 2022] Shuohang Wang, Yichong Xu, Yuwei Fang, Yang Liu, Siqi Sun, Ruochen Xu, Chenguang Zhu, and Michael Zeng

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.358411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea4c2b3c-e8ec-4af2-88f4-c15c8982a742 · outbound

This paper cites [Wu et al., 2020] Tien-Hsuan Wu, Ben Kao, Anne S.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity [Wu et al., 2020] Tien-Hsuan Wu, Ben Kao, Anne S

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.210212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d0f47a82-afb2-4cf2-8c19-4e4197c2a995 · outbound

This paper cites Sim- ple and effective few-shot named entity recognition with structured nearest neighbor learning.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Sim- ple and effective few-shot named entity recognition with structured nearest neighbor learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.088698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7564ec80-2f89-4c3d-9654-8702f7db054d · outbound

This paper cites Data augmentation for low-resource named entity recognition using backtranslation.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Data augmentation for low-resource named entity recognition using backtranslation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:28.965006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1f48c9db-d511-4718-83a3-af3883cc6e7d · outbound

This paper cites Prototypical networks for few-shot learning.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Prototypical networks for few-shot learning

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.594741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9168ecc8-eade-41d2-ab82-107dd7f27ad0 · outbound

This paper cites T-NER: An all-round python library for transformer-based named entity recognition.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity T-NER: An all-round python library for transformer-based named entity recognition

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:29.459500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72568441-2b94-4cc7-b7e5-470852f6df0f · outbound

This paper cites Making pre-trained language models better few- shot learners.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Making pre-trained language models better few- shot learners

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:31.133010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 71bb85f8-32b4-43e2-9179-2324034b45fe · outbound

This paper cites Few-shot classification in named entity recognition task.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Few-shot classification in named entity recognition task

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:31.415630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 900b5ecd-2354-41d2-b498-3555b4321aca · outbound

This paper cites [Dai and Adel, 2020] Xiang Dai and Heike Adel.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity [Dai and Adel, 2020] Xiang Dai and Heike Adel

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:31.542743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f4f469eb-1f43-4721-b040-330db337f4d0 · outbound

This paper cites A dataset of german legal doc- uments for named entity recognition.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity A dataset of german legal doc- uments for named entity recognition

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:30.586241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b06f1a59-8ffb-4099-9ff0-0e9b48a27ecc · outbound

This paper cites Label semantics for few shot named entity recognition.

Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity Label semantics for few shot named entity recognition

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:01:30.184838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.