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Paper Citation Record · LEDGER

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1611.01368.

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

pith.paper-citation-record.v1
1611.01368 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:11:03.422340Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T18:29:34.584726Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 40fddf0c-1d3b-4233-a08a-1c739c0dec07 · inbound

Visualizing and Understanding the Effectiveness of BERT cites this paper.

Visualizing and Understanding the Effectiveness of BERT Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-14T13:11:03.422340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:11:03.422340Z digest=sha256:9950dc3cf4b7b08fd69d3edcab40ac4946c5597027b17b1860f48bffdc82277a

Observation 33fbdb41-c036-47c8-825a-58a9a711881f · inbound

Higher-order Comparisons of Sentence Encoder Representations cites this paper.

Higher-order Comparisons of Sentence Encoder Representations Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-14T05:58:40.926250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:58:40.926250Z digest=sha256:e92f7e3e8107faef872ec43f580a8602b826bf34203e7d14118b8b412aa8566a

Observation b752a128-8f75-4ca0-bd64-141082367bba · inbound

Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific Text cites this paper.

Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific Text Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T19:02:02.673453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T19:02:02.673453Z digest=sha256:70988d422f19d4aff4cc9c5eafe02eb317e178b0dabaf5f63009b34265cf465e

Observation d60762e6-5ff3-4c9b-b8ab-e7f62c359fb6 · inbound

Aligning Brain Activity with Advanced Transformer Models: Exploring the Role of Punctuation in Semantic Processing cites this paper.

Aligning Brain Activity with Advanced Transformer Models: Exploring the Role of Punctuation in Semantic Processing Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T21:10:09.426393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:10:09.426393Z digest=sha256:90470c4f816c9d5c9c9aeed21013505ca2a055bf74fa008400c4d001bbb23fd2

Observation ce3d5129-20e8-4be7-b6b0-6c1c254e7809 · inbound

Language Models Largely Exhibit Human-like Constituent Ordering Preferences cites this paper.

Language Models Largely Exhibit Human-like Constituent Ordering Preferences Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Reference 2016

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T18:29:34.590588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-08T18:29:34.408672Z digest=sha256:10cc6cbe7656af6d09fa3bb23f3219b5d18f171c87643f3fbfce1774dc69db4c