Datasets:

Flaglab
/

esnlir-al-annotated-test

license: cc-by-4.0
language:
  - es
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
pretty_name: ESNLIR — human-annotated AL evaluation set (1,695 pairs)
size_categories:
  - 1K<n<10K
tags:
  - nli
  - spanish
  - causal
  - esnlir
  - active-learning
  - human-annotated
configs:
  - config_name: default
    data_files:
      - split: test
        path: test_full.jsonl

ESNLIR — human-annotated evaluation set

1,695 human-annotated premise–hypothesis pairs spanning 24 domains and 8 genres, built as the evaluation set for active-learning experiments on the ESNLIR corpus.

Active Learning for Spanish Natural Language Inference on a Heterogeneous Multi-Domain Corpus Diego Ortiz, Johan R. Portela, Ruben Manrique — Universidad de los Andes, Bogotá Advances in Artificial Intelligence — IBERAMIA 2026 (to appear)

Code: jd-rodriguezp1234/esnlir-active-learning · Corpus: Flaglab/ESNLIR-dataset

Labels

Four inference relations. Each instance is a pair of adjacent sentences from a single document: the premise precedes a discourse connector, the hypothesis follows it, and the connector is removed so the relation has to be recovered from the two sentences alone.

label	n	share
`neutral`	634	37.4%
`entailment`	379	22.4%
`reasoning`	370	21.8%
`contrasting`	312	18.4%

Classes are uneven by design — this is a natural sample, not a balanced one. Report macro F1 rather than accuracy.

reasoning (cause–effect, e.g. en consecuencia, por consiguiente) is ESNLIR's departure from the usual three-class schema, and empirically the hardest of the four.

Genres and domains

genre	n	genre	n
`theses`	614	`articles`	132
`books`	315	`legal`	112
`comments`	234	`clinical`	50
`news`	198	`talks`	40

24 distinct domains. The five largest: literature (17.2%), web comments (13.8%), Spanish public-domain news (8.0%), encyclopedia (7.8%), engineering theses (7.0%). The set deliberately spans formal registers (legal, clinical) and informal ones (web comments, tweets).

Fields

{"sentence_1": "se pueden retener 10 pasaportes alemanes al mismo tiempo",
 "sentence_2": "estos pasaportes adicionales son válidos por solo 6 años, ...",
 "connector": "sin embargo,", "connector_type": "contrasting", "connection_type": "contrasting",
 "extraction_strategy": "linking_phrase", "distance": 1.0,
 "sentence_1_paragraph": 1, "sentence_1_position": 55,
 "sentence_2_paragraph": 1, "sentence_2_position": 56,
 "id": "esarticles__eswiki__...", "dataset": "esarticles__eswiki",
 "genre": "articles", "domain": "encyclopedia"}

The gold label is connection_type, the human majority label. connector_type is the label derived automatically from the discourse connector, kept for reference. The two are identical on every row — see Two filters, not one below — so either column can be used.

Note id identifies the source article, not the pair, and is not unique. Join on (sentence_1, sentence_2).

Annotation

12 annotators (undergraduate CS students) worked over one month. Each instance was labeled independently by 3 annotators and the final label assigned by majority vote; instances without sufficient agreement were discarded — 151 removed, leaving the 1,695 released here.

Inter-annotator agreement was Cohen's κ = 0.641 (overlap-weighted mean), conventionally read as substantial. Agreement was lowest for reasoning, consistent with the entailment/reasoning boundary being the main source of ambiguity — the same distinction every model here finds hardest.

The written guidelines required annotators to judge strictly what is written, without mentally repairing or extending the sentences, and to choose neutral when a pair was ambiguous. No synthetic augmentation was used at any stage.

The retention filter

A pair was kept only if its human majority label matched the connector-derived label. The 151 discarded instances are exactly those where the two disagreed — which is why connection_type and connector_type are identical on all 1,695 remaining rows.

This makes the set a reliability-filtered slice, not a neutral sample: it holds only pairs where automatic and human labelling already coincide. Scores here are correspondingly higher than on the full ESNLIR test split (0.82 vs 0.68 macro F1 for the same XLM-RoBERTa checkpoint), and that gap measures label reliability rather than task difficulty — the same effect documented for ESNLIR's own human-validated subset. Do not read it as models being better at these instances.

The retention rate is notably high: 1,695 of 1,846 candidates, 91.8%. ESNLIR's own validation exercise retained only ~45% under the same rule, so these candidates — drawn by confidence stratification — were substantially easier to label consistently.

How candidates were chosen

Candidates were sampled by model confidence before annotation: a preliminary pass with the released ESNLIR XLM-RoBERTa checkpoint binned them into four confidence strata (high, medium, low, none), concentrating annotation effort on uncertain instances.

This has a consequence worth stating plainly. The set is enriched for instances that checkpoint finds hard, so that model's own score on it is biased downward. It still ranks highest among all systems evaluated, which is what makes the comparison robust to the selection effect.

Note this pulls in the opposite direction to the connector-agreement filter above: uncertainty sampling selects harder instances, agreement filtering keeps easier ones. The net effect is not a simple bias in one direction, which is another reason absolute numbers here are not comparable with numbers on the full ESNLIR test split. Relative comparisons between systems on this set are the intended use.

Reported results

Macro F1 on this set, from the active-learning paper:

encoder	method	labels used	macro F1
XLM-R	full supervision	1,000k	0.822
XLM-R	NegE (best, iter 5)	610k	0.796
XLM-R	LER (final, iter 7)	800k	0.789
Bertin	full supervision	1,000k	0.811
Bertin	NegE (best, iter 6)	710k	0.764
Bertin	LER (final, iter 7)	800k	0.754

Active learning reaches 95.5% of full supervision with 61% of the labels, and NegE and LER are indistinguishable — since LER acquires uniformly at random from a filtered pool, energy-based acquisition shows no measurable advantage over near-random selection.

Usage

from datasets import load_dataset

ds = load_dataset("Flaglab/esnlir-al-annotated-test", split="test")
print(ds[0]["sentence_1"], "|", ds[0]["connection_type"])


`Flaglab/ESNLIR-dataset`	the corpus this is drawn from
`Flaglab/esnlir-al-trajectories`	per-round AL metrics and pool indices
`Flaglab/ESNLIR-AL-XLM-RoBERTa-NegE`	best AL checkpoint

Citation

@InProceedings{ortiz2026activelearningspanishnli,
  author    = {Ortiz, Diego and Portela, Johan R. and Manrique, Ruben},
  title     = {Active Learning for Spanish Natural Language Inference
               on a Heterogeneous Multi-Domain Corpus},
  booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2026},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  note      = {To appear},
}

The corpus is ESNLIR (Portela, Pérez-Terán & Manrique, 2026), a separate project with its own code and paper.