# CASIMIR: un Corpus d'Articles Scientifiques Intégrant les Modifications et Révisions des auteurs

Léane Jourdan, Richard Dufour, Nicolas Hernandez, Florian Boudin

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To cite this version:

Léane Jourdan, Richard Dufour, Nicolas Hernandez, Florian Boudin. CASIMIR: un Corpus d'Articles Scientifiques Intégrant les Modifications et Révisions des auteurs. Atelier sur l'Analyse et la Recherche de Textes Scientifiques, CORIA-TALN 2023, Jun 2023, Paris, France. hal-04122594

HAL Id: hal-04122594

https://hal.science/hal-04122594

Submitted on 8 Jun 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# CASIMIR: un Corpus d'Articles Scientifiques
## Intégrant les Modifications et Révisions des auteurs

Léane Jourdan, Florian Boudin, Richard Dufour, Nicolas Hernandez

`{prénom.nom}@univ-nantes.fr`

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## Objectifs

* Nouveau corpus d'articles scientifiques avec révisions

* Un alignement des modifications entre les versions des articles

* Enrichit avec les métadonnées des articles et les relectures par les pairs

## Processus de collecte

1. Collecte des événements (ateliers, conférences, etc) sur Open Review

2. Collecte des articles : métadonnées, relectures et PDF disponibles des versions des articles

3. Filtrage des articles ayant une seule version (89,33% des articles conservés soit 97,46% des PDF)

4. Conversion des PDF vers XML (outil Grobid)

## Exemple de révisions

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### Abstract

Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used to understand reasoning from question to answer. We make our code and data available at https://github.com/anonymous.

<figure><img src="image_5.png" /><figcaption>Figure 1: The triple scoring process for a question answering task, and two methods that use the scores to extract relevant subgraphs for a question and candidate answer.</figcaption></figure>

### 1 Introduction

For models to be able to reason about situations that arise in everyday life, they must have access to contextually appropriate common sense information. This information is commonly stored as a large set of facts from which the model must identify a relevant subset. One approach to structuring these facts is as a knowledge graph. Here, nodes represent high-level concepts, and relationships are expressed via typed edges joining two nodes. Each edge type represents a different kind of conceptual relationship between concepts. The contextually-relevant subgraphs ('schema graphs') that are extracted from these graphs are often encoded using neural models, which are trained for tasks including question answering or natural language inference.

Prior work has focused on different ways to encode these facts, including by inputting them into a graph neural network (GNN) or into a transformer (Feng et al., 2020; Yasunaga et al., 2021). However, the question of how to identify useful information has been under-explored, particularly in work that uses GNN encoders. If contextually important information is not retrieved then performance could be dramatically reduced, a potential result of the use of overly simplistic retrieval methods.

In this paper we explore methods to extract high-quality subgraphs containing contextually relevant

information.<fnref n="1" /> We approach this as a ranking task across triples in a knowledge graph, and propose two methods that use the scores to extract a subgraph. The first is a weighted pathfinding approach which extends prior work (Lin et al., 2019), while the second builds a minimum spanning tree that includes the highest-ranked triples (figure 1). Both approaches ensure that all or most nodes in the subgraph are reachable from each other, which is important for two reasons. First, it means that the GNN can update node embeddings with information from most other nodes, which would not be possible if the graph were disconnected. Second, it allows paths of reasoning to be extracted from the subgraph, which are often used as explanations for model behaviour (Feng et al., 2020; Wang et al., 2020; Yasunaga et al., 2021).

There are also situations when specific concepts need to be included in order for a subgraph to be of high enough quality. For example, in question answering, a full explanation must include one

## Articles

les PDF des versions
des articles

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## Contenu

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## Metadonnées des articles

* dates

* auteurs

* mots-clés

* événement

* identifiants

## Relectures

* commentaires

* notes

* décisions

* dates

## Fichiers de correspondance

* entre les versions finales et antérieures des articles

* entre les relectures et les articles

## Description

Contient 730 évènements et 118 415 PDF pour 26 355 articles

Domaines : apprentissage auto, robotique, TAL, vision, etc.

<figure><img src="image_8.png" /><figcaption>Distribution des articles suivant leur nombre de relectures</figcaption></figure>

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## Et ensuite?

* Améliorer la conversion des PDF (choix de l'outil, traitement des figures, tables)

* Aligner les versions paragraphe à paragraphe et phrase à phrase puis extraire les révisions

* Annoter les documents selon une taxonomie de révisions à définir (ex: clarté, grammaire, style)

* Exploitation pour la mise en place d'outils d'aide à l'écriture

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<footnote n="1">¹We call these "relevant subgraphs" or "extracted subgraphs", noting that others use "schema graphs" (Lin et al., 2019).</footnote>