acl-org / acl-org/acl-anthology

Ingestion Request: {INLG 2026}

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ingestion
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Description

### General information about this request

- [x] I confirm that I have read the [Information for Submitters](https://aclanthology.org/info/contrib/).
- [x] I am submitting a request for a **new venue** that does not exist in the ACL Anthology yet.

### Venue Identifier

INLG

### ACL SIG(s) sponsoring or endorsing the whole venue

SIGGEN

### Volume Title

Proceedings of the 19th International Natural Language Generation Conference

### Venue Name (only if you are submitting a new venue)

International Natural Language Generation Conference

### Venue Website (only if you are submitting a new venue)

https://2026.inlgmeeting.org

### Date of Publication

2026-10-12

### Location

Utrecht, the Netherlands

### Submission Platform(s)

- [x] OpenReview (proceedings compiled with [aclpub2](https://github.com/rycolab/aclpub2/))
- [ ] Softconf/START (export the proceedings in ACLPUB format; no need to convert with aclpub2)
- [ ] Other (explain)

### ORCID iDs

- [x] We will instruct authors to supply ORCID information in their profiles in the submission platform ([instructions](https://aclanthology.org/info/author-pages/)). (Not necessary for historical volumes.)

### Delivery Method

- [x] Private Github repo (preferred)
- [x] wget-able web link (HTTP, FTP)
- [ ] Proprietary file sharing service (Google Drive or Dropbox)

### Supporting Information

_No response_

Contributor guide

No contributing guide indexed for this repository

Research direction

This is an ingestion request with full venue metadata (INLG, title, dates, location, website, and submission platform), but no target files or tests are named. Start by reading the repo’s contributor documentation for ingestion/venue onboarding to locate where venue records are maintained and how OpenReview+aclpub2 inputs are processed. Then follow the same flow used by recent venue entries and create the INLG 2026 record with the supplied values. Done means the INLG 2026 venue appears in the expected anthology metadata/state without unresolved ingest gaps.

Written by the indexing model from the issue text.

Assessment

Domain
content
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
55/100

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