Datasets:

taln-ls2n
/

ARRContributions

acl_id string	title string	abstract string	conference_name string	conference_track string	year int64	url string	contribution_types list	openreview_id string	openreview_cycle string	openreview_history list	article_content string
2024.findings-eacl.68	Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis	Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. For instance, a sentiment classifier may erroneously learn that the token “performances” is commonly associated with positive movie r...	eacl	findings	2,024	https://aclanthology.org/2024.findings-eacl.68.pdf	[ "Model analysis & interpretability", "NLP engineering experiment" ]	Pkt8doM0TV	October 2023	[]	[{"1 Introduction": ["Disclaimer: This paper contains examples that may be considered profane or offensive. These examples by no means reflect the authors' view toward any groups or entities.", "Pre-trained language models (PLMs) such as BERT Devlin et al. (2019) and its derivative models have shown impressive performa...
2024.eacl-long.73	No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models	Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English. We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction. We...	eacl	long	2,024	https://aclanthology.org/2024.eacl-long.73.pdf	[ "NLP engineering experiment", "Approaches to low-resource settings", "Approaches low compute settings-efficiency" ]	vchiWnuieL	October 2023	[]	[{"1 Introduction": ["Grammatical Error Correction (GEC) systems are a vital link between expert language use and clear communication, enhancing writing skills and language learning. However, GEC research has primarily focused on the English language with much less coverage for other languages, resulting in English-ori...
2024.eacl-long.13	Language Models as Inductive Reasoners	Inductive reasoning is a core component of human intelligence. In the past research of inductive reasoning within computer science, formal language is used as representations of knowledge (facts and rules, more specifically). However, formal language can cause systematic problems for inductive reasoning such as disabil...	eacl	long	2,024	https://aclanthology.org/2024.eacl-long.13.pdf	[ "NLP engineering experiment", "Data resources" ]	MbM-nT-YfN	October 2023	[]	[{"1 Introduction": ["Inductive reasoning is to reach to a hypothesis (usually a rule that explains an aspect of the law of nature) based on pieces of evidence (usually observed facts of the world), where the observations can not provide conclusive support to the hypothesis [19]. It is ampliative, which means that the ...
2024.eacl-long.178	Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition	"Few-shot named entity recognition (NER) detects named entities within text using only a few annotat(...TRUNCATED)	eacl	long	2,024	https://aclanthology.org/2024.eacl-long.178.pdf	[ "Approaches to low-resource settings", "Data analysis" ]	ep9cuBomIC	October 2023	[]	"[{\"1 Introduction\": [\"Few-shot named entity recognition (NER) refers to identifying and classify(...TRUNCATED)
2024.eacl-long.176	"Do Moral Judgment and Reasoning Capability of LLMs Change with Language? A Study using the Multilin(...TRUNCATED)	"This paper explores the moral judgment and moral reasoning abilities exhibited by Large Language Mo(...TRUNCATED)	eacl	long	2,024	https://aclanthology.org/2024.eacl-long.176.pdf	[ "Model analysis & interpretability" ]	jmysF33NjI	October 2023	[]	"[{\"1 Introduction\": [\"In a recent work, Tanmay et al. (2023) used the Defining Issues Test (DIT)(...TRUNCATED)
2024.acl-long.438	PokeMQA: Programmable knowledge editing for Multi-hop Question Answering	"Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehens(...TRUNCATED)	acl	long	2,024	https://aclanthology.org/2024.acl-long.438.pdf	[ "NLP engineering experiment", "Approaches to low-resource settings" ]	OQpPiCRTNdz	October 2023	[]	"[{\"1 Introduction\": [\"Multi-hop question answering (MQA) requires a sequence of interacted knowl(...TRUNCATED)
2024.acl-long.607	Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding	"We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Mod(...TRUNCATED)	acl	long	2,024	https://aclanthology.org/2024.acl-long.607.pdf	[ "NLP engineering experiment", "Approaches low compute settings-efficiency" ]	WueVcpFqKv	December 2023	[{"contribution_types":["NLP engineering experiment","Approaches low compute settings-efficiency"],"(...TRUNCATED)	"[{\"1 Introduction\": [\"Transformer-based Large Language Models (LLMs), such as GPT-3/4, PaLM, and(...TRUNCATED)
2024.findings-eacl.122	Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune?	"The ever-growing size of pretrained language models (PLM) presents a significant challenge for effi(...TRUNCATED)	eacl	findings	2,024	https://aclanthology.org/2024.findings-eacl.122.pdf	[ "Approaches low compute settings-efficiency" ]	-X-A7GO_bd	October 2023	[]	"[{\"1 Introduction\": [\"In recent years, the number of parameters used in language models has rise(...TRUNCATED)
2024.naacl-long.431	Naive Bayes-based Context Extension for Large Language Models	"Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventio(...TRUNCATED)	naacl	long	2,024	https://aclanthology.org/2024.naacl-long.431.pdf	[ "NLP engineering experiment" ]	j0pVr1fIKQR	December 2023	[{"contribution_types":["NLP engineering experiment","Approaches low compute settings-efficiency","T(...TRUNCATED)	"[{\"1 Introduction\": [\"Large Language Models (LLMs) have demonstrated remarkable capabilities in (...TRUNCATED)
2024.findings-eacl.127	Sequence Shortening for Context-Aware Machine Translation	"Context-aware Machine Translation aims to improve translations of sentences by incorporating surrou(...TRUNCATED)	eacl	findings	2,024	https://aclanthology.org/2024.findings-eacl.127.pdf	[ "NLP engineering experiment" ]	pL0PGOZ91S	October 2023	[]	"[{\"1 Introduction\": [\"Following the introduction of the Transformer model Vaswani et al. (2017),(...TRUNCATED)

End of preview. Expand in Data Studio

ARRContributions: A Dataset of Contribution Types from ARR Papers

About

ARRContributions is a dataset of more than 2000 articles extracted from ARR papers submitted to OpenReview that present contribution types information. Contributions types are required to be specified by the authors when making submission to ARR.

The ARR typology (Rogers et al., 2023) defines 11 contribution types that authors can select from to best characterize their work: (1) NLP engineering experiment (e.g., methods improving state-of-the-art results), (2) approaches for low-compute settings and efficiency, (3) approaches for low-resource settings, (4) data resources, (5) data analysis, (6) model analysis and interpretability, (7) reproduction studies, (8) position papers, (9) surveys, (10) theory, and (11) publicly available software and pre-trained models.

Content

The following data fields are available :

Feature	Type	Description
`acl_id`	`string`	Unique identifier of the paper in the ACL Anthology.
`title`	`string`	Title of the paper.
`abstract`	`string`	Abstract of the paper.
`conference_name`	`string`	Name of the conference (e.g., acl, emnlp, eacl).
`conference_track`	`string`	Track or submission category within the conference.
`year`	`int64`	Year of publication.
`url`	`string`	ACL Anthology link to the paper.
`contribution_types`	`list[string]`	List of contribution types selected according to the ARR typology (Rogers et al., 2023), e.g., data resources, model analysis, theory.
`openreview_id`	`string`	Unique OpenReview submission ID.
`openreview_cycle`	`string`	Review cycle or round associated with the OpenReview submission.
`openreview_history`	`list[object]`	List of previous submission records for the same paper when available. Each record includes: • `contribution_types` (`list[string]`): Contribution types selected in that cycle. • `contribution_types_has_changed` (`bool`): Whether the contribution types differ from the previous cycle. • `cycle` (`string`): The OpenReview cycle name. • `id` (`string`): The OpenReview submission ID.
`article_content`	`string`	Full text of the paper (extracted using nougat).

We split our dataset into training, validation, and test sets using an 80-10-10 ratio, ensuring label balance through multi-label stratification strategy. The test set was manually annotated by three independent annotators to establish an additional gold-standard labeling. We provide both the original test annotations from the dataset authors and the consensus annotations from the three annotators as separate splits.

Licence

Dataset: CC BY-NC 4.0
Original papers: CC BY 4.0 (retain attribution)

If you use this dataset:

You may use, share, and adapt the dataset for non-commercial research or educational purposes only.
Must attribute both the dataset creators and the original ACL Anthology authors for any content used.

Citation

@misc{,
      title={}, 
      author={},
      year={},
      eprint={},
      archivePrefix={},
      primaryClass={}
}

Downloads last month: 3