Flaglab/esnlir-human-majority

ESNLIR — human majority labels vs connector labels

1,971 Spanish premise–hypothesis pairs carrying two labels: the one derived automatically from the discourse connector, and the one a majority of human annotators assigned after the connector was removed. They agree on 972 pairs and disagree on 999.

An Analysis of the Performance of Large Language Models in Spanish NLI Datasets with Causal Relationships Nicolás Pérez, Johan R. Portela, Ruben Manrique — Universidad de los Andes, Bogotá (IBERAMIA 2026, to appear)

Code: Pacolas/NLI-via-LLM · Collection: ESNLIR-LLM

Why this exists

ESNLIR, like SciNLI and MSciNLI, builds NLI pairs by distant supervision: take two adjacent sentences joined by a discourse connector, delete the connector, and let it stand as the gold label. The method scales to 400k pairs without annotators. It also assumes the connector is recoverable from the sentences alone.

This file measures how often that assumption holds. It is the evidence behind the paper's Cohen's κ = 0.32 — and the part that matters most, the 999 pairs where the two label sources diverge, exists nowhere else. Flaglab/esnlir-human-validated publishes only the 972 survivors; this dataset is a strict superset of it with identical schema.

Agreement

	pairs	share
human majority matches connector label	972	49.3%
human majority differs	999	50.7%

Cohen's κ = 0.325 (chance agreement 0.249), conventionally fair — far from the near-identity that treating connectors as ground truth would require.

Where the disagreement is

Rows are the connector-derived label, columns the human majority. The diagonal is agreement.

connector ↓ / human →	contrasting	entailment	neutral	reasoning	retained
contrasting	184	78	126	108	37.1%
entailment	20	219	102	159	43.8%
neutral	29	44	362	48	74.9%
reasoning	26	143	116	207	42.1%

Three things are visible here, and none of them are noise:

Contrasting connectors are the least trustworthy (37.1% retained). Sin embargo and no obstante frequently join sentences whose contrast is rhetorical rather than semantic; strip the connector and readers see two unrelated statements (126 → neutral) or a causal chain (108 → reasoning).

The reasoning/entailment boundary cuts both ways. 143 reasoning-connector pairs read as entailment to humans, and 159 entailment-connector pairs read as reasoning. This is the same confusion every model in the paper exhibits — it is a property of the distinction, not of the classifier.

Neutral is the only stable class (74.9%). Absence of a connector is easier to confirm than the presence of a specific relation.

The sample was balanced; the human labels are not

	contrasting	entailment	neutral	reasoning
connector label	496	500	483	492
human majority	259	484	706	522

Pairs were drawn near-uniformly by connector label, yet annotators returned a neutral-heavy distribution. The guidelines instructed them to judge strictly what is written and to choose neutral when a pair was ambiguous, so neutral absorbs exactly the cases where the deleted connector carried information the sentences do not.

Genres

genre	annotated	retained	rate		genre	annotated	retained	rate
theses	905	427	47.2%		legal	125	71	56.8%
books	412	217	52.7%		clinical	77	51	66.2%
comments	153	67	43.8%		talks	76	34	44.7%
news	149	71	47.7%		articles	74	34	45.9%

24 domains, 32 source datasets. Clinical and legal text — the most formulaic registers — retain best; web comments retain worst.

Fields

Identical to Flaglab/esnlir-human-validated, so the two join directly.

field	meaning
connector_type	label derived from the discourse connector (distant supervision)
connection_type	human majority label — differs from connector_type on 999 rows
connector	the connector that was removed, e.g. sin embargo,
sentence_1, sentence_2	premise and hypothesis, connector stripped
genre, domain, dataset	provenance
extraction_strategy, distance, *_paragraph, *_position	how the pair was extracted
id	source-article id — not unique per pair; join on (sentence_1, sentence_2)

Annotation

2,136 pairs were sampled across domains and labelled by 27 Spanish-speaking university students, who produced 13,626 annotations — a mean of 6.4 independent labels per pair. Adjudication was by majority vote.

165 pairs (7.7%) reached no majority and are not in this file, which is why it holds 1,971 rather than 2,136. Their absence means the agreement rate here is measured only over pairs where annotators could converge, and is therefore an optimistic estimate of connector reliability.

Usage

from datasets import load_dataset

ds = load_dataset("Flaglab/esnlir-human-majority", split="train")

disagree = ds.filter(lambda r: r["connector_type"] != r["connection_type"])
print(len(disagree), "pairs where the connector misleads")   # 999

# the pairs a 'sin embargo' reader would get wrong
for r in disagree.filter(lambda r: r["connector_type"] == "contrasting").select(range(3)):
    print(f"{r['connector']!r}  connector={r['connector_type']}  human={r['connection_type']}")
    print("   ", r["sentence_1"][:70], "/", r["sentence_2"][:70])

Training on the human labels instead of the connector labels, or using the 999 divergent pairs as a hard evaluation slice, are both straightforward from here.

A caution

Do not read model scores on the 972 agreeing pairs as evidence that annotation improves models. That subset is defined by agreement, so every classifier scores higher on it — the fine-tuned XLM-RoBERTa baseline gains 4.8 points under the same filter without any change to the model. The gain measures label reliability, not model quality. This dataset is published precisely so the selection effect is inspectable rather than implicit.

Related

Flaglab/esnlir-human-validated	the 972 agreeing pairs — a subset of this file
Flaglab/esnlir-llm-predictions	per-pair model outputs on these pairs
Flaglab/esnlir-test	the 80,213-pair test split these were sampled from

Citation

@InProceedings{perez2026llmspanishnlicausal,
  author    = {P{\'e}rez, Nicol{\'a}s and Portela, Johan R. and Manrique, Ruben},
  title     = {An Analysis of the Performance of Large Language Models in Spanish
               NLI Datasets with Causal Relationships},
  booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2026},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  note      = {To appear},
}

The pairs come from ESNLIR, released here under CC BY 4.0 — attribution to the source corpus is a condition of that licence:

@InProceedings{portela2025esnlirspanishmultigenredataset,
  author    = {Portela, Johan R. and P{\'e}rez-Ter{\'a}n, Nicol{\'a}s and Manrique, Rub{\'e}n},
  editor    = {Florez, Hector and Peluffo-Ordo{\~{n}}ez, Diego},
  title     = {{ESNLIR}: Expanding Spanish {NLI} Benchmarks with Multi-genre and Causal Annotation},
  booktitle = {Applied Informatics},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {345--361},
  isbn      = {978-3-032-07175-0},
  doi       = {10.1007/978-3-032-07175-0_23},
}