Paper Citation Record · LEDGER

Few-shot Text Classification with Distributional Signatures

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.06039.

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

pith.paper-citation-record.v1
1908.06039 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:02:45.223628Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

37 of 37 outbound references displayed

verified exact2
verified fuzzy16
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f70e038b-16b1-4fc6-a6a8-de005a8d9086 · outbound

This paper cites How to train your MAML.

Few-shot Text Classification with Distributional Signatures How to train your MAML

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.015217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53f2c082-81d7-4801-b981-8deeeb0814ea · outbound

This paper cites sci” and “rec,.

Few-shot Text Classification with Distributional Signatures sci” and “rec,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.740905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fa57f72c-29d7-4555-ad9d-c808857d160a · outbound

This paper cites Induction Networks for Few-Shot Text Classification.

Few-shot Text Classification with Distributional Signatures Induction Networks for Few-Shot Text Classification

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:02:45.503094Z

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Observation f813502f-cd5a-4829-ba8b-a3430f483737 · outbound

This paper cites Fewrel: A large-scale supervised few-shot relation classiﬁcation dataset with state-of-the-art evaluation.

Few-shot Text Classification with Distributional Signatures Fewrel: A large-scale supervised few-shot relation classiﬁcation dataset with state-of-the-art evaluation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.803639Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 754fd586-0aca-4691-af8a-22635cb62594 · outbound

This paper cites Attentive task-agnostic meta-learning for few-shot text classiﬁcation,.

Few-shot Text Classification with Distributional Signatures Attentive task-agnostic meta-learning for few-shot text classiﬁcation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.787802Z

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Observation cb0aebc5-3550-40c2-9f8c-1b7893fcd77c · outbound

This paper cites FastText.zip: Compressing text classification models.

Few-shot Text Classification with Distributional Signatures FastText.zip: Compressing text classification models

Reference 11

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Source-reported events for the cited work

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Observation 96228312-9fa8-4b27-be74-cb6863506fb4 · outbound

This paper cites Convolutional Neural Networks for Sentence Classification.

Few-shot Text Classification with Distributional Signatures Convolutional Neural Networks for Sentence Classification

Reference 12

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Source-reported events for the cited work

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Observation ca00762a-93c6-4738-bc6f-bed5c7319b93 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Few-shot Text Classification with Distributional Signatures On First-Order Meta-Learning Algorithms

Reference 16

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Observation 0def0135-b5a4-409d-948d-0bf7afabcb42 · outbound

This paper cites Linguistically-Informed Self-Attention for Semantic Role Labeling.

Few-shot Text Classification with Distributional Signatures Linguistically-Informed Self-Attention for Semantic Role Labeling

Reference 18

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Source-reported events for the cited work

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Observation ccb3fc4b-8ab1-4640-9a0f-57331ca05699 · outbound

This paper cites Meta-Transfer Learning for Few-Shot Learning.

Few-shot Text Classification with Distributional Signatures Meta-Transfer Learning for Few-Shot Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:02:45.355737Z

Source-reported events for the cited work

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Observation d8a909b7-0a6a-41ab-b094-aaa467f43bd6 · outbound

This paper cites Learning to Transfer.

Few-shot Text Classification with Distributional Signatures Learning to Transfer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.138344Z

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Observation 714e98e4-1ded-4f7a-a20d-0757e4798d57 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Few-shot Text Classification with Distributional Signatures HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.143740Z

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Unavailable: canonical work link unavailable.

Observation 6ef8a7f3-fbf5-4d61-9ec7-5f22a895e071 · outbound

This paper cites Diverse Few-Shot Text Classification with Multiple Metrics.

Few-shot Text Classification with Distributional Signatures Diverse Few-Shot Text Classification with Multiple Metrics

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.149050Z

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Observation 8ef98c4a-220b-4056-a99c-78b44ffecf85 · outbound

This paper cites When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification.

Few-shot Text Classification with Distributional Signatures When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T13:02:45.283146Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39fd053c-d9e3-4d1e-b06e-12d5d6333ff7 · outbound

This paper cites an unresolved cited work.

Few-shot Text Classification with Distributional Signatures Unresolved cited work

Reference 24

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Observation c575caa4-782e-437b-a153-776fd642142c · outbound

This paper cites doi: 10.18653/v1/D17-1004.

Few-shot Text Classification with Distributional Signatures doi: 10.18653/v1/D17-1004

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.163283Z

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Observation c072defc-111c-4bbc-a0bd-3f0b8a79e51f · outbound

This paper cites Implementation details may be found in Appendix A.11.

Few-shot Text Classification with Distributional Signatures Implementation details may be found in Appendix A.11

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.726197Z

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Observation 27c3b549-333b-469c-a0e5-7b29d597d3b6 · outbound

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Few-shot Text Classification with Distributional Signatures Unresolved cited work

Reference 28

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Observation e6293f03-1280-4d9d-a427-860dedc50602 · outbound

This paper cites original.

Few-shot Text Classification with Distributional Signatures original

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.678561Z

Source-reported events for the cited work

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Observation bfdf9753-2eac-489c-9905-9aec9cf1bb42 · outbound

This paper cites 77.1 on 5- way 5-shot, 60.1 vs.

Few-shot Text Classification with Distributional Signatures 77.1 on 5- way 5-shot, 60.1 vs

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.647758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 920fcc53-1de0-41e9-b4df-d1e728530779 · outbound

This paper cites Since examples in HuffPost Headlines are 30 times shorter, the cosine similarities are higher in this corpora.

Few-shot Text Classification with Distributional Signatures Since examples in HuffPost Headlines are 30 times shorter, the cosine similarities are higher in this corpora

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.631575Z

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Observation 62cb522c-58e0-4fb6-899b-443d60e7c156 · outbound

This paper cites an unresolved cited work.

Few-shot Text Classification with Distributional Signatures Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:02:45.616831Z

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Observation 9ea6c436-5352-4f60-9c53-6b5daae3cb93 · outbound

This paper cites For pretraining, we used Hugging Face’s language model ﬁnetuning code with default hyperparameters and BERT’s pretrained base-uncased model.

Few-shot Text Classification with Distributional Signatures For pretraining, we used Hugging Face’s language model ﬁnetuning code with default hyperparameters and BERT’s pretrained base-uncased model

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.584424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b53d5c09-c24c-40a6-a5aa-432b033f4731 · outbound

This paper cites During the MAML inner loop (adaptation stage), we perform ten updates with step size 10−3.

Few-shot Text Classification with Distributional Signatures During the MAML inner loop (adaptation stage), we perform ten updates with step size 10−3

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.568540Z

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Observation bd83b235-b393-4218-80d0-267f3364d203 · outbound

This paper cites MAML MAML meta-learns an initialization such that the model can quickly adapt to new tasks after a few gradient steps.

Few-shot Text Classification with Distributional Signatures MAML MAML meta-learns an initialization such that the model can quickly adapt to new tasks after a few gradient steps

Reference 300

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.600095Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ce46be7-46a6-47de-bc41-9e1e6651ff08 · outbound

This paper cites s(·): weighted average of word embeddings with weights given by s(·).

Few-shot Text Classification with Distributional Signatures s(·): weighted average of word embeddings with weights given by s(·)

Reference 500

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.662950Z

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Observation d69675b8-6854-467b-8bae-3b130ed3aa0c · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Few-shot Text Classification with Distributional Signatures Dropout: a simple way to prevent neural networks from overﬁtting

Reference 1972

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.771623Z

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Observation d530542f-a9de-4c99-88f9-195c27e376a1 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Few-shot Text Classification with Distributional Signatures Universal Language Model Fine-tuning for Text Classification

Reference 1997

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Observation c2ece059-5b04-4d75-9e0a-d5bed2cdc0dd · outbound

This paper cites A Structured Self-attentive Sentence Embedding.

Few-shot Text Classification with Distributional Signatures A Structured Self-attentive Sentence Embedding

Reference 2004

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Observation 87d62db8-3ee0-47f5-9284-64008e4be97f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Few-shot Text Classification with Distributional Signatures BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.028146Z

Source-reported events for the cited work

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Observation 63cb06d5-2670-48c0-8031-9cc1d1f267eb · outbound

This paper cites • P-MAML combines pre-training with MAML.

Few-shot Text Classification with Distributional Signatures • P-MAML combines pre-training with MAML

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.696355Z

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Observation 59acb955-df17-45f7-8b30-eb47acc8d51b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Few-shot Text Classification with Distributional Signatures Adam: A Method for Stochastic Optimization

Reference 2014

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Observation a32fe16f-b0ba-4ca7-810b-658c345193ff · outbound

This paper cites Multi-task Sequence to Sequence Learning.

Few-shot Text Classification with Distributional Signatures Multi-task Sequence to Sequence Learning

Reference 2015

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Source-reported events for the cited work

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Observation 8166e896-34c7-48fc-9e17-1046d4b60e60 · outbound

This paper cites Deriving machine attention from human rationales.

Few-shot Text Classification with Distributional Signatures Deriving machine attention from human rationales

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.848713Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 67133b2e-07b3-40a8-b92a-34d4304bc80d · outbound

This paper cites Few-Shot Learning with Graph Neural Networks.

Few-shot Text Classification with Distributional Signatures Few-Shot Learning with Graph Neural Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T13:02:45.034739Z

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Observation 8b69b671-9b7e-4043-a0df-d8965ddf07d3 · outbound

This paper cites Multi-source domain adaptation with mixture of experts.

Few-shot Text Classification with Distributional Signatures Multi-source domain adaptation with mixture of experts

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.818657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 426de6bd-ca77-4b2d-a7dd-30cfe2b157f6 · outbound

This paper cites Meta-learning for low- resource neural machine translation.

Few-shot Text Classification with Distributional Signatures Meta-learning for low- resource neural machine translation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:02:45.832923Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

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