Papers by Florian Boudin
CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions (2024.lrec-main)
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| Challenge: | CASIMIR dataset contains multiple revisions of 15,646 scientific articles . authors question the relevance of current evaluation methods for text revision . |
| Approach: | They propose a textual resource on the revision step of the writing process of scientific articles. |
| Outcome: | The proposed dataset contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. |
Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness (2021.naacl-main)
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| Challenge: | Neural keyphrase generation models can output absent keyphrases, which are keyphrase that do not appear in the source text. |
| Approach: | They propose a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. |
| Outcome: | The proposed model shows that only 20% of the words that make up keyphrases actually serve as document expansion, but this small fraction behind much of the gains observed in retrieval effectiveness. |
ACL-rlg: A Dataset for Reading List Generation (2025.coling-main)
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| Challenge: | Existing tools for searching the literature return an overwhelming number of results, making familiarization process daunting and inefficient. |
| Approach: | They propose to use ACL-rlg as the largest open expert-annotated reading list dataset to help researchers navigate key literature. |
| Outcome: | The proposed dataset outperforms existing search engines and indexing methods and shows signs of data contamination. |
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data (2022.lrec-1)
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| Challenge: | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and can be used for further NLP applications. |
| Approach: | They propose to fine-tune pre-trained BERT models for automatic Term Extraction (ATE) using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. |
| Outcome: | The proposed models can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE. |
Towards Reliable Paper Contributions Annotation in the ACL Rolling Review (2026.findings-acl)
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| Challenge: | Identifying the types of contributions an article makes can help readers grasp its significance. |
| Approach: | They propose to use a typology to categorize articles by their contributions to improve review quality and fairness. |
| Outcome: | The ACL Rolling Review (ARR) introduced a typology requiring authors to specify their contributions to improve review quality and fairness. |
Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models (2024.acl-srw)
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| Challenge: | Pre-trained language models (PLMs) are used to produce examples sentences targeting L2 learners. |
| Approach: | They propose to use pre-trained language models to produce diverse examples of Japanese sentences that are aligned with learners’ proficiency levels. |
| Outcome: | The proposed method is adaptable to other languages with minor adjustments. |
An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers (2020.lrec-1)
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| Challenge: | Formulaic expressions are used by authors of scientific papers because they convey specific communicative functions in the rhetorical structure of papers. |
| Approach: | They created a manually annotated dataset to detect formulaic expressions in sentences using a seed list of labelled formulaic words. |
| Outcome: | The proposed dataset can detect communicative functions in sentences using a seed list of labelled expressions from scholarly papers in the ACL Anthology. |
Identifying Reliable Evaluation Metrics for Scientific Text Revision (2025.acl-long)
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| Challenge: | Effective revision is a critical step in scientific writing, ensuring clarity, coherence, and adherence to academic standards. |
| Approach: | They propose to use ROUGE and BERTScore to assess revision quality . they also examine LLM-as-a-judge approaches to assess instruction-following revisions . |
| Outcome: | The proposed method improves the accuracy of revision tasks with and without a gold reference. |
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)
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| Challenge: | predicting the final label alone is insufficient and offers limited interpretability. |
| Approach: | They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification. |
| Outcome: | The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning. |
Keyphrase Generation for Scientific Document Retrieval (2020.acl-main)
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| Challenge: | Sequence-to-sequence models have been used to generate keyphrases, but it is unclear whether they are reliable enough for document retrieval. |
| Approach: | They propose a framework for extrinsic evaluation that allows for a better understanding of the limitations of keyphrase generation models. |
| Outcome: | The proposed models improve retrieval performance by supplementing documents with keyphrases that are not present in the source text and generalizing models across domains. |
Unsupervised Keyphrase Extraction with Multipartite Graphs (N18-2)
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| Challenge: | Recent years have witnessed a resurgence of interest in automatic keyphrase extraction. |
| Approach: | They propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. |
| Outcome: | The proposed model improves on three widely used datasets. |
Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts (2024.findings-emnlp)
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| Challenge: | Existing methods for keyphrase generation are limited to resource-rich languages. |
| Approach: | They propose to extract silver-standard keyphrases from citation contexts to create synthetic labeled data for domain adaptation. |
| Outcome: | The proposed method produces significant and consistent improvements over baselines across three domains. |