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Paper Citation Record · LEDGER

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2506.04389.

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

pith.paper-citation-record.v1
2506.04389 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:47:35.923159Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

18 of 18 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0c9cb463-1458-4649-8ae4-377d7beaaa90 · outbound

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

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T10:47:34.533917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:34.533917Z digest=sha256:bdd1de4ef75ee3ef5cad174c044d740b69f6469ef0a7e51db00a4a3f077904a4

Observation 40b89bd0-92ad-4ece-84cb-e9edbd05e08c · outbound

This paper cites 4171–4186, Minneapolis, Minnesota, (June 2019).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 4171–4186, Minneapolis, Minnesota, (June 2019)

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.947345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:34.583732Z digest=sha256:39581b64811c6490525428c46e7e8d3e27aea92d8d5f018802bc714c1f632fe3

Observation c00c27e9-8e12-4ff8-8ad5-257cf5d7dbf8 · outbound

This paper cites Language-agnostic BERT Sentence Embedding.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Language-agnostic BERT Sentence Embedding

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T10:47:34.673853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:34.673853Z digest=sha256:45a972bfc0186b01cfcaaeee5d7a0e9e72c66fb9e95fdb87b64641d2d82ea905

Observation 58e31b3e-e53f-4495-b026-f521b20802d3 · outbound

This paper cites 3904–3913, Hong Kong, China, (November 2019).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 3904–3913, Hong Kong, China, (November 2019)

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.771287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:34.768080Z digest=sha256:60e35d93211bfaf3839523291b9557c39d8969561e6e58e53244a5d168cc92f4

Observation 3a29507b-4700-43ff-aeae-1fdc6ebecfd0 · outbound

This paper cites 305–313, Online, (August 2021).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 305–313, Online, (August 2021)

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.664668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:34.839368Z digest=sha256:c972554ab7305b4c03829d131b08a53cd5f66c1b2a23ad2b646d3e378935142d

Observation 298ae760-f59d-4b86-8f0c-ab844f00d864 · outbound

This paper cites Smith, ‘Don’t stop pretraining: Adapt language models to domains and tasks’, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Smith, ‘Don’t stop pretraining: Adapt language models to domains and tasks’, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.560190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:34.934053Z digest=sha256:f8a7e5c6ae6a53b0a7bbfa0796685d8009f3da080cc7c9d675d8c4216548aedc

Observation 63cda3e2-fada-48ef-adef-022b27e98993 · outbound

This paper cites 5392– 5404, Florence, Italy, (July 2019).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 5392– 5404, Florence, Italy, (July 2019)

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.396201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.027146Z digest=sha256:b8047904e61c71648bbf51996757d1913ae7a34c43d3773cb54988eb3befdbd2

Observation b1273d74-f56d-43f1-8f07-afb531e9a6a7 · outbound

This paper cites Kingma and Jimmy Ba.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Kingma and Jimmy Ba

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T10:47:35.129600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:35.129600Z digest=sha256:6fe564b5c2c0ed1897b930c3960c973a9dc364d69a81bf783fca8721bd76cfc2

Observation 3f799583-6f5c-4361-b750-8093d23d32be · outbound

This paper cites 1209–1218, Online, (November 2020).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 1209–1218, Online, (November 2020)

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.262867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.224848Z digest=sha256:bceadc7a0b2a958ccd64247640774517e59c37e49df3421b714ed6cf88284ba6

Observation 20cc8b28-6fb4-433a-b2b9-329869b97c51 · outbound

This paper cites 172–182, Online, (November 2020).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 172–182, Online, (November 2020)

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:37.056109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.267500Z digest=sha256:181ce5b7c6665b205f9613c1e36733651071ef148fd8de53951de668bb8338f6

Observation 1520993d-00b0-464e-a882-d50aa67788be · outbound

This paper cites Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T10:47:35.331537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:35.331537Z digest=sha256:b1efe917569ff521d11db6ac6df66a67454ec83e6c796e4b45e96acd68f5dae8

Observation b5d7feb1-ada7-4d9b-9427-c7316880c9f1 · outbound

This paper cites Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T10:47:35.431879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:35.431879Z digest=sha256:a2580164457a3c050184479e015da93f31f142206ec48e19906f5b24459d2031

Observation d5a1b3d5-3918-4b34-9875-70873cfd0a9e · outbound

This paper cites Torres, Arantza Pozo, and Raquel Justo, ‘Topic classifier for customer service dialog systems’, pp.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Torres, Arantza Pozo, and Raquel Justo, ‘Topic classifier for customer service dialog systems’, pp

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:36.871104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.499347Z digest=sha256:8d365ba0b13b70a038f20401cd1d4fed0ec51bab44cc975a96f2bda90892b451

Observation d99c693b-7eb4-49e7-873d-ce0b681525f0 · outbound

This paper cites Generating representative samples for few-shot classification, 2022.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Generating representative samples for few-shot classification, 2022

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:36.702091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.603268Z digest=sha256:7e647a86db3483d76f013e3a963a0ccd55826f06bd211183da8f7599ba075743

Observation 3193ade5-d253-46e4-a533-9a61ff3ba9a3 · outbound

This paper cites 4618–4625.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 4618–4625

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:36.548546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.669355Z digest=sha256:2e10938c79c6d05df30e1823532f2511bb4373670dc2b411f68a9b5b1f059711

Observation 6f59e94e-a436-4cf3-baa1-ce24798c4e3f · outbound

This paper cites an unresolved cited work.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:47:36.362901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.760841Z digest=sha256:362c936bc05a773904f22eac53faaf7515170584f75c24bd43d6e24b531adad7

Observation af60d8b8-f59e-4da3-96ab-2c140ba47118 · outbound

This paper cites Lam, ‘Effectiveness of pre-training for few-shot intent classification’, in Findings of the Association for Computational Linguistics: EMNLP 2021, pp.

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care Lam, ‘Effectiveness of pre-training for few-shot intent classification’, in Findings of the Association for Computational Linguistics: EMNLP 2021, pp

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:36.252507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.826981Z digest=sha256:8bdb60059bb91c58160fbecfd05b084e123f96b8a1999f34ff5cf234f3edafea

Observation b7c7ff51-c4a6-42db-9ed4-217e47ff3d47 · outbound

This paper cites 5064– 5082, Online, (November 2020).

Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care 5064– 5082, Online, (November 2020)

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:47:36.089392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:47:35.923159Z digest=sha256:ffe07506a32a2f105eece1fe5fc80c56366384e57fb219b98e848480660271b8

Pith citing papers

No inbound Pith citation observations are available.