@inproceedings{fytas-etal-2021-makes,
title = "What Makes a Scientific Paper be Accepted for Publication?",
author = "Fytas, Panagiotis and
Rizos, Georgios and
Specia, Lucia",
editor = "Feder, Amir and
Keith, Katherine and
Manzoor, Emaad and
Pryzant, Reid and
Sridhar, Dhanya and
Wood-Doughty, Zach and
Eisenstein, Jacob and
Grimmer, Justin and
Reichart, Roi and
Roberts, Molly and
Shalit, Uri and
Stewart, Brandon and
Veitch, Victor and
Yang, Diyi",
booktitle = "Proceedings of the First Workshop on Causal Inference and NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.cinlp-1.4",
doi = "10.18653/v1/2021.cinlp-1.4",
pages = "44--60",
abstract = "Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process. We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peer-review dataset. Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons. Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper{'}s main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.",
}
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<abstract>Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process. We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peer-review dataset. Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons. Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper’s main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.</abstract>
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%0 Conference Proceedings
%T What Makes a Scientific Paper be Accepted for Publication?
%A Fytas, Panagiotis
%A Rizos, Georgios
%A Specia, Lucia
%Y Feder, Amir
%Y Keith, Katherine
%Y Manzoor, Emaad
%Y Pryzant, Reid
%Y Sridhar, Dhanya
%Y Wood-Doughty, Zach
%Y Eisenstein, Jacob
%Y Grimmer, Justin
%Y Reichart, Roi
%Y Roberts, Molly
%Y Shalit, Uri
%Y Stewart, Brandon
%Y Veitch, Victor
%Y Yang, Diyi
%S Proceedings of the First Workshop on Causal Inference and NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F fytas-etal-2021-makes
%X Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process. We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peer-review dataset. Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons. Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper’s main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.
%R 10.18653/v1/2021.cinlp-1.4
%U https://aclanthology.org/2021.cinlp-1.4
%U https://doi.org/10.18653/v1/2021.cinlp-1.4
%P 44-60
Markdown (Informal)
[What Makes a Scientific Paper be Accepted for Publication?](https://aclanthology.org/2021.cinlp-1.4) (Fytas et al., CINLP 2021)
ACL