Datasets:

amazon
/

poiss

---
license: apache-2.0
pretty_name: "POISS: Point-of-Interest Search Suite"
task_categories:
- text-retrieval
language:
- en
- es
- fr
- pt
- de
- it
- nl
size_categories:
- 100K<n<1M
tags:
- point-of-interest
- poi-search
- geospatial
- reranking
- multilingual
- graded-relevance
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: test
    path: data/test-*.parquet
---
# POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search

POISS is the first large-scale dataset for Point-of-Interest (POI) search. Given a natural-language query and
the coordinate it was issued from, a system ranks POIs by relevance. Relevance here is
genuinely two-sided: "coffee shops near me" depends on where the user is, while "hotel caribe en
mérida yucatán" names a specific place hundreds of kilometres away. Models have to weigh
**semantics** and **geography** together.

The dataset contains **309,386 queries in 7 languages across 14 locales**, grounded in the
[Overture Maps Places](https://docs.overturemaps.org/guides/places/) corpus of more than 72
million POIs. Each query comes with a **ranked list of candidate POIs (~301) with graded
relevance labels**, so the same data supports both retrieval over the full corpus and reranking
of a candidate pool.

Fine-tuned baselines: [`amazon/poiss-bge-m3-retriever`](https://huggingface.co/amazon/poiss-bge-m3-retriever)
and [`amazon/poiss-bge-m3-reranker`](https://huggingface.co/amazon/poiss-bge-m3-reranker).

## About

The datasets and models in this repository originate from our paper accepted at the **Main Conference** of **The 2026 Conference on Empirical Methods in Natural Language Processing**.

For more details, please check out the paper [POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search](POISS_paper.pdf).

✨ **Come visit our poster session in Budapest!** We look forward to meeting you and discussing our work.

## What you can build with it

- **Geo-aware retrieval at scale.** Retrieve from a 72M-POI index using the query text and the
  user's coordinate. Recall@{10,100,1000} and NDCG@{5,20} against graded labels.
- **Reranking with graded relevance.** Up to 20 ranked positives per query let you study
  fine-grained ordering, not just relevant/non-relevant.
- **Multilingual and cross-lingual behaviour.** English, Spanish, French, Portuguese, German,
  Italian and Dutch, each with enough test queries to report per-language numbers. Every row
  carries `language`, `locale` and `query_country`, so the breakdowns are a `group_by` away.
- **Intent analysis.** The `intent_group` column splits queries into name-anchored (`Search`,
  `Detail`) and open-ended (`Recommend`, `Things-to-do`) intents, which behave very differently:
  open-ended queries stay substantially harder for every system we measured.
- **Geography versus semantics.** Every query carries its issuing coordinate and every candidate
  its location, so distance can be modelled explicitly, combined with lexical or dense scores,
  or studied as a feature.
- **Training data for retrievers and rerankers.** The ranked, labeled candidate list per query
  is designed to be sampled from: positives, graded positives, judged negatives and an unjudged
  tail are all identified, so you can pick the positive/negative scheme your setup needs.

## Dataset at a glance

| | train | test |
|---|---|---|
| Queries | 267,368 | 42,018 |
| Candidate query–POI pairs | 80,495,030 | 12,655,117 |
| Candidates per query (avg) | 301.1 | 301.2 |
| Graded relevant POIs per query (avg) | 14.15 | 13.78 |

Across both splits the dataset references 20,960,591 distinct Overture POIs. Queries average
3.26 tokens. These counts are measured on the files in this repository; Table 1 of the paper
reports the candidate pools before de-duplication, so its per-query averages are
marginally higher.

## Row schema

| Field | Type | Description |
|---|---|---|
| `query_id` | string | uuid4 |
| `split` | string | `train` or `test` |
| `query_text` | string | the query |
| `user_geolocation` | struct{`lat`,`lng`: float64} | coordinate the query was issued from |
| `language` | string | ISO 639-1 code, e.g. `it` |
| `locale` | string | language–country locale, e.g. `it_IT`, `es_US` |
| `query_country` | string | ISO 3166-1 alpha-2 country, e.g. `IT` |
| `intent_group` | string | `Search`, `Detail`, `Recommend` or `Things-to-do` |
| `candidates` | list[string] | ordered Overture GERS identifiers |
| `candidate_labels` | list[string] | parallel to `candidates`, one label each |
| `relevant_candidates` | list[string] | the graded-relevant subset (≤ 20), in ranked order |
| `consolidation_removed` | list[string] | POIs identified as cross-source duplicates |
| `label_counts` | struct of 5 int64 | count per label |

### Candidate ordering and labels

`candidate_labels[i]` describes `candidates[i]`, and the labels form contiguous blocks in this
order:

| Order | Label | Meaning | avg/query (train) |
|---|---|---|---|
| 1 | `relevant` | graded positives, ranked | 14.15 |
| 2 | `semantic_positive` | judged relevant, beyond the graded top-20 | 6.45 |
| 3 | `semantic_negative_google` | reference-engine candidate judged non-relevant | 2.34 |
| 4 | `semantic_negative_poi_search_system` | retrieved candidate judged non-relevant | 11.14 |
| 5 | `unjudged_poi_search_system` | retrieve-and-rerank tail | 266.98 |

Because the order is fixed, a rank-based slice of `candidates` is also a label-based one:
`relevant_candidates` is the head of the list, and `label_counts` gives the block boundaries in
constant time.

## Loading

```python
from datasets import load_dataset
poiss = load_dataset("amazon/poiss")
row = poiss["test"][0]
print(row["query_text"], row["user_geolocation"], row["locale"], row["intent_group"])
print(row["candidates"][:5], row["candidate_labels"][:5])
# per-language or per-intent slices
italian = poiss["test"].filter(lambda r: r["language"] == "it")
open_ended = poiss["test"].filter(lambda r: r["intent_group"] == "Recommend")
```

## Recovering POI content

POISS ships Overture identifiers; POI names, addresses, coordinates and categories come from
Overture itself. `scripts/join_overture.py` does the join against a current release:

```bash
python scripts/join_overture.py --split test --release 2026-08-19.0 --out test_pois.parquet
```

Output columns: `poiss_candidate_id`, `overture_id`, `names`, `addresses`, `categories`,
`confidence`, `latitude`, `longitude`, `quality_score`. Requires `pyarrow` and `tqdm`.

POISS was built on Overture release `2026-03-18`, and Overture reassigns some GERS identifiers
when it re-conflates the corpus. `data/overture_id_map.parquet` translates 1,853,120 such
identifiers to their current equivalent, matched through the provider records the two releases
share. With the map applied, 88.5% of candidate identifiers and about 90% of the graded
positives resolve against release `2026-08-19`; the remainder are POIs Overture has since
retired, and the script lists them in its report. The map is refreshed as new Overture releases
appear, and `--id-map` accepts your own.

## Quality score

`data/poi_quality_score.parquet` maps `overture_id` to a `score` in [0, 5]: an LLM-derived
quality signal used during labeling and read by the cross-encoder as its `Score` feature. It is
not an Overture field, so it ships with the dataset.

```python
import pyarrow.parquet as pq
table = pq.read_table("data/poi_quality_score.parquet")
scores = dict(zip(table.column("overture_id").to_pylist(), table.column("score").to_pylist()))
```

The file covers the POIs for which upstream rating information was available — 30.7% of
query–candidate pairs, and 43.7% of the graded positives. Models were trained and evaluated
with the field absent where no score exists.

## Tasks and reference results

**Full-corpus retrieval.** The search space is the entire Overture corpus; systems are scored
against the graded labels. Test split, percentages:

| System | R@10 | R@100 | R@1k | N@5 | N@20 |
|---|---|---|---|---|---|
| Distance only | 0.6 | 2.4 | 10.2 | 0.5 | 0.7 |
| BM25 | 17.8 | 29.9 | 42.8 | 16.0 | 18.0 |
| BM25 + distance | 18.4 | 31.2 | 42.8 | 17.1 | 19.1 |
| BGE-M3 zero-shot | 15.2 | 26.4 | 40.0 | 13.7 | 15.3 |
| **BGE-M3 fine-tuned** | **45.2** | **83.2** | **95.6** | 58.3 | 60.3 |
| Qwen3-Embedding-0.6B zero-shot | 22.2 | 35.3 | 49.7 | 22.6 | 24.1 |
| Qwen3-Embedding-0.6B fine-tuned | 45.0 | 82.5 | 94.7 | **58.7** | **60.7** |

Fine-tuned per-language R@100 spans 79.8 (NL) to 86.1 (DE); per-intent R@100 is 89.1 on
`Search` against 77.3 on `Recommend`. Both breakdowns can be reproduced from the `language` and
`intent_group` columns.

The test split is 19.5% US English, 15.0% UK English, 14.4% Canadian English, 8.9% Brazilian
Portuguese, 8.6% French, 8.2% Spanish (Spain), 7.6% Mexican Spanish, 7.1% Italian, 6.9% German,
with the remainder spread over Dutch, Irish English, Canadian French, Austrian German and US
Spanish. By intent it is 49.8% `Search`, 47.8% `Recommend`, 1.9% `Things-to-do`, 0.5% `Detail`.

**Candidate reranking.** A system reorders a per-query pool. The numbers below use the pool from
the reference setup — the fine-tuned retriever's top-1000 over the full corpus — which can be
rebuilt with the released retriever. Reranking the `candidates` column is also a valid task, in
an easier setting where every graded positive is present by construction.

| System | P@5 | P@20 | MRR | N@5 | N@20 |
|---|---|---|---|---|---|
| BGE-M3 retriever (bi-encoder order) | 57.6 | 35.5 | 85.5 | 61.5 | 62.6 |
| BGE-Reranker-v2-m3 zero-shot | 24.6 | 14.3 | 50.0 | 32.1 | 34.8 |
| **BGE-Reranker-v2-m3 fine-tuned** | **63.0** | **38.3** | **89.7** | **66.8** | **66.6** |
| Qwen3-Reranker-0.6B zero-shot | 38.3 | 21.3 | 68.3 | 43.8 | 44.6 |
| Qwen3-Reranker-0.6B fine-tuned | 61.8 | 37.4 | 88.8 | 65.7 | 65.7 |

For reference, the reranker baseline was trained with positives = `relevant` and negatives drawn
from `semantic_negative_poi_search_system` + `unjudged_poi_search_system`, padded to 100 per
query. Other sampling schemes, or training on a rank-based subset of the full list, are equally
available.

## License and attribution

POISS is released under the **Apache License 2.0**, covering the queries, the relevance labels
and their ordering, the identifier map, the quality scores and the scripts.
As a condition of use, you must cite our associated paper whenever you use this dataset to train a model, run evaluations, or publish research.

POI content is not redistributed here. When you join against Overture you are bound by
Overture's terms: the places theme is published under CDLA-Permissive-2.0 and Apache-2.0
depending on the source provider and contains no OpenStreetMap data. Attribute Overture Maps
Foundation and the per-source licenses accordingly; each Overture record carries its own
`sources[].license`. See [Overture licensing](https://docs.overturemaps.org/guides/places/).

The released baselines carry the license of the model they were fine-tuned from:
[`poiss-bge-m3-retriever`](https://huggingface.co/amazon/poiss-bge-m3-retriever) is MIT, from
`BAAI/bge-m3`; [`poiss-bge-m3-reranker`](https://huggingface.co/amazon/poiss-bge-m3-reranker) is
Apache-2.0, from `BAAI/bge-reranker-v2-m3`.

## Citation

```bibtex
@inproceedings{maritan-etal-2026-poiss,
    title = "{POISS}: A Large-Scale Multilingual Dataset for Point-of-Interest Search",
    author = "Maritan, Nicola  and
      Moschitti, Alessandro  and
      Borazio, Federico  and
      Zhou, Xiaokun  and
      Bai, Zhengwei",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    note = "To appear",
}
```

	---
	license: apache-2.0
	pretty_name: "POISS: Point-of-Interest Search Suite"
	task_categories:
	- text-retrieval
	language:
	- en
	- es
	- fr
	- pt
	- de
	- it
	- nl
	size_categories:
	- 100K<n<1M
	tags:
	- point-of-interest
	- poi-search
	- geospatial
	- reranking
	- multilingual
	- graded-relevance
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*.parquet
	- split: test
	path: data/test-*.parquet
	---

	# POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search

	POISS is the first large-scale dataset for Point-of-Interest (POI) search. Given a natural-language query and
	the coordinate it was issued from, a system ranks POIs by relevance. Relevance here is
	genuinely two-sided: "coffee shops near me" depends on where the user is, while "hotel caribe en
	mérida yucatán" names a specific place hundreds of kilometres away. Models have to weigh
	semantics and geography together.

	The dataset contains 309,386 queries in 7 languages across 14 locales, grounded in the
	[Overture Maps Places](https://docs.overturemaps.org/guides/places/) corpus of more than 72
	million POIs. Each query comes with a **ranked list of candidate POIs (~301) with graded
	relevance labels**, so the same data supports both retrieval over the full corpus and reranking
	of a candidate pool.

	Fine-tuned baselines: [`amazon/poiss-bge-m3-retriever`](https://huggingface.co/amazon/poiss-bge-m3-retriever)
	and [`amazon/poiss-bge-m3-reranker`](https://huggingface.co/amazon/poiss-bge-m3-reranker).

	## About

	The datasets and models in this repository originate from our paper accepted at the Main Conference of The 2026 Conference on Empirical Methods in Natural Language Processing.

	For more details, please check out the paper [POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search](POISS_paper.pdf).

	✨ Come visit our poster session in Budapest! We look forward to meeting you and discussing our work.

	## What you can build with it

	- Geo-aware retrieval at scale. Retrieve from a 72M-POI index using the query text and the
	user's coordinate. Recall@{10,100,1000} and NDCG@{5,20} against graded labels.
	- Reranking with graded relevance. Up to 20 ranked positives per query let you study
	fine-grained ordering, not just relevant/non-relevant.
	- Multilingual and cross-lingual behaviour. English, Spanish, French, Portuguese, German,
	Italian and Dutch, each with enough test queries to report per-language numbers. Every row
	carries `language`, `locale` and `query_country`, so the breakdowns are a `group_by` away.
	- Intent analysis. The `intent_group` column splits queries into name-anchored (`Search`,
	`Detail`) and open-ended (`Recommend`, `Things-to-do`) intents, which behave very differently:
	open-ended queries stay substantially harder for every system we measured.
	- Geography versus semantics. Every query carries its issuing coordinate and every candidate
	its location, so distance can be modelled explicitly, combined with lexical or dense scores,
	or studied as a feature.
	- Training data for retrievers and rerankers. The ranked, labeled candidate list per query
	is designed to be sampled from: positives, graded positives, judged negatives and an unjudged
	tail are all identified, so you can pick the positive/negative scheme your setup needs.

	## Dataset at a glance

	\| \| train \| test \|
	\|---\|---\|---\|
	\| Queries \| 267,368 \| 42,018 \|
	\| Candidate query–POI pairs \| 80,495,030 \| 12,655,117 \|
	\| Candidates per query (avg) \| 301.1 \| 301.2 \|
	\| Graded relevant POIs per query (avg) \| 14.15 \| 13.78 \|

	Across both splits the dataset references 20,960,591 distinct Overture POIs. Queries average
	3.26 tokens. These counts are measured on the files in this repository; Table 1 of the paper
	reports the candidate pools before de-duplication, so its per-query averages are
	marginally higher.

	## Row schema

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `query_id` \| string \| uuid4 \|
	\| `split` \| string \| `train` or `test` \|
	\| `query_text` \| string \| the query \|
	\| `user_geolocation` \| struct{`lat`,`lng`: float64} \| coordinate the query was issued from \|
	\| `language` \| string \| ISO 639-1 code, e.g. `it` \|
	\| `locale` \| string \| language–country locale, e.g. `it_IT`, `es_US` \|
	\| `query_country` \| string \| ISO 3166-1 alpha-2 country, e.g. `IT` \|
	\| `intent_group` \| string \| `Search`, `Detail`, `Recommend` or `Things-to-do` \|
	\| `candidates` \| list[string] \| ordered Overture GERS identifiers \|
	\| `candidate_labels` \| list[string] \| parallel to `candidates`, one label each \|
	\| `relevant_candidates` \| list[string] \| the graded-relevant subset (≤ 20), in ranked order \|
	\| `consolidation_removed` \| list[string] \| POIs identified as cross-source duplicates \|
	\| `label_counts` \| struct of 5 int64 \| count per label \|

	### Candidate ordering and labels

	`candidate_labels[i]` describes `candidates[i]`, and the labels form contiguous blocks in this
	order:

	\| Order \| Label \| Meaning \| avg/query (train) \|
	\|---\|---\|---\|---\|
	\| 1 \| `relevant` \| graded positives, ranked \| 14.15 \|
	\| 2 \| `semantic_positive` \| judged relevant, beyond the graded top-20 \| 6.45 \|
	\| 3 \| `semantic_negative_google` \| reference-engine candidate judged non-relevant \| 2.34 \|
	\| 4 \| `semantic_negative_poi_search_system` \| retrieved candidate judged non-relevant \| 11.14 \|
	\| 5 \| `unjudged_poi_search_system` \| retrieve-and-rerank tail \| 266.98 \|

	Because the order is fixed, a rank-based slice of `candidates` is also a label-based one:
	`relevant_candidates` is the head of the list, and `label_counts` gives the block boundaries in
	constant time.

	## Loading

	```python
	from datasets import load_dataset

	poiss = load_dataset("amazon/poiss")
	row = poiss["test"][0]
	print(row["query_text"], row["user_geolocation"], row["locale"], row["intent_group"])
	print(row["candidates"][:5], row["candidate_labels"][:5])

	# per-language or per-intent slices
	italian = poiss["test"].filter(lambda r: r["language"] == "it")
	open_ended = poiss["test"].filter(lambda r: r["intent_group"] == "Recommend")
	```

	## Recovering POI content

	POISS ships Overture identifiers; POI names, addresses, coordinates and categories come from
	Overture itself. `scripts/join_overture.py` does the join against a current release:

	```bash
	python scripts/join_overture.py --split test --release 2026-08-19.0 --out test_pois.parquet
	```

	Output columns: `poiss_candidate_id`, `overture_id`, `names`, `addresses`, `categories`,
	`confidence`, `latitude`, `longitude`, `quality_score`. Requires `pyarrow` and `tqdm`.

	POISS was built on Overture release `2026-03-18`, and Overture reassigns some GERS identifiers
	when it re-conflates the corpus. `data/overture_id_map.parquet` translates 1,853,120 such
	identifiers to their current equivalent, matched through the provider records the two releases
	share. With the map applied, 88.5% of candidate identifiers and about 90% of the graded
	positives resolve against release `2026-08-19`; the remainder are POIs Overture has since
	retired, and the script lists them in its report. The map is refreshed as new Overture releases
	appear, and `--id-map` accepts your own.

	## Quality score

	`data/poi_quality_score.parquet` maps `overture_id` to a `score` in [0, 5]: an LLM-derived
	quality signal used during labeling and read by the cross-encoder as its `Score` feature. It is
	not an Overture field, so it ships with the dataset.

	```python
	import pyarrow.parquet as pq
	table = pq.read_table("data/poi_quality_score.parquet")
	scores = dict(zip(table.column("overture_id").to_pylist(), table.column("score").to_pylist()))
	```

	The file covers the POIs for which upstream rating information was available — 30.7% of
	query–candidate pairs, and 43.7% of the graded positives. Models were trained and evaluated
	with the field absent where no score exists.

	## Tasks and reference results

	Full-corpus retrieval. The search space is the entire Overture corpus; systems are scored
	against the graded labels. Test split, percentages:

	\| System \| R@10 \| R@100 \| R@1k \| N@5 \| N@20 \|
	\|---\|---\|---\|---\|---\|---\|
	\| Distance only \| 0.6 \| 2.4 \| 10.2 \| 0.5 \| 0.7 \|
	\| BM25 \| 17.8 \| 29.9 \| 42.8 \| 16.0 \| 18.0 \|
	\| BM25 + distance \| 18.4 \| 31.2 \| 42.8 \| 17.1 \| 19.1 \|
	\| BGE-M3 zero-shot \| 15.2 \| 26.4 \| 40.0 \| 13.7 \| 15.3 \|
	\| BGE-M3 fine-tuned \| 45.2 \| 83.2 \| 95.6 \| 58.3 \| 60.3 \|
	\| Qwen3-Embedding-0.6B zero-shot \| 22.2 \| 35.3 \| 49.7 \| 22.6 \| 24.1 \|
	\| Qwen3-Embedding-0.6B fine-tuned \| 45.0 \| 82.5 \| 94.7 \| 58.7 \| 60.7 \|

	Fine-tuned per-language R@100 spans 79.8 (NL) to 86.1 (DE); per-intent R@100 is 89.1 on
	`Search` against 77.3 on `Recommend`. Both breakdowns can be reproduced from the `language` and
	`intent_group` columns.

	The test split is 19.5% US English, 15.0% UK English, 14.4% Canadian English, 8.9% Brazilian
	Portuguese, 8.6% French, 8.2% Spanish (Spain), 7.6% Mexican Spanish, 7.1% Italian, 6.9% German,
	with the remainder spread over Dutch, Irish English, Canadian French, Austrian German and US
	Spanish. By intent it is 49.8% `Search`, 47.8% `Recommend`, 1.9% `Things-to-do`, 0.5% `Detail`.

	Candidate reranking. A system reorders a per-query pool. The numbers below use the pool from
	the reference setup — the fine-tuned retriever's top-1000 over the full corpus — which can be
	rebuilt with the released retriever. Reranking the `candidates` column is also a valid task, in
	an easier setting where every graded positive is present by construction.

	\| System \| P@5 \| P@20 \| MRR \| N@5 \| N@20 \|
	\|---\|---\|---\|---\|---\|---\|
	\| BGE-M3 retriever (bi-encoder order) \| 57.6 \| 35.5 \| 85.5 \| 61.5 \| 62.6 \|
	\| BGE-Reranker-v2-m3 zero-shot \| 24.6 \| 14.3 \| 50.0 \| 32.1 \| 34.8 \|
	\| BGE-Reranker-v2-m3 fine-tuned \| 63.0 \| 38.3 \| 89.7 \| 66.8 \| 66.6 \|
	\| Qwen3-Reranker-0.6B zero-shot \| 38.3 \| 21.3 \| 68.3 \| 43.8 \| 44.6 \|
	\| Qwen3-Reranker-0.6B fine-tuned \| 61.8 \| 37.4 \| 88.8 \| 65.7 \| 65.7 \|

	For reference, the reranker baseline was trained with positives = `relevant` and negatives drawn
	from `semantic_negative_poi_search_system` + `unjudged_poi_search_system`, padded to 100 per
	query. Other sampling schemes, or training on a rank-based subset of the full list, are equally
	available.

	## License and attribution

	POISS is released under the Apache License 2.0, covering the queries, the relevance labels
	and their ordering, the identifier map, the quality scores and the scripts.
	As a condition of use, you must cite our associated paper whenever you use this dataset to train a model, run evaluations, or publish research.

	POI content is not redistributed here. When you join against Overture you are bound by
	Overture's terms: the places theme is published under CDLA-Permissive-2.0 and Apache-2.0
	depending on the source provider and contains no OpenStreetMap data. Attribute Overture Maps
	Foundation and the per-source licenses accordingly; each Overture record carries its own
	`sources[].license`. See [Overture licensing](https://docs.overturemaps.org/guides/places/).

	The released baselines carry the license of the model they were fine-tuned from:
	[`poiss-bge-m3-retriever`](https://huggingface.co/amazon/poiss-bge-m3-retriever) is MIT, from
	`BAAI/bge-m3`; [`poiss-bge-m3-reranker`](https://huggingface.co/amazon/poiss-bge-m3-reranker) is
	Apache-2.0, from `BAAI/bge-reranker-v2-m3`.

	## Citation

	```bibtex
	@inproceedings{maritan-etal-2026-poiss,
	title = "{POISS}: A Large-Scale Multilingual Dataset for Point-of-Interest Search",
	author = "Maritan, Nicola and
	Moschitti, Alessandro and
	Borazio, Federico and
	Zhou, Xiaokun and
	Bai, Zhengwei",
	booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
	month = oct,
	year = "2026",
	address = "Budapest, Hungary",
	publisher = "Association for Computational Linguistics",
	note = "To appear",
	}
	```