Papers by Sanja Štajner
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs (C18-1)
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| Challenge: | Existing methods to assess text complexity only at lexical and syntactic levels have not been attempted. |
| Approach: | They propose to automatically estimate conceptual complexity using graph-based measures on a large knowledge base. |
| Outcome: | The proposed measures achieve high discriminative power even in a default setup. |
A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification (L18-1)
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| Challenge: | Xu et al., 2016) show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification. |
| Approach: | They evaluate neural sequence-to-sequence models for text simplification on Wikipedia and Newsela datasets. |
| Outcome: | The proposed model can generalize across corpora and overcome challenges when tested on Wikipedia and Newsela datasets. |
Data-Driven Text Simplification (C18-3)
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| Challenge: | Automatic text simplification is the process of transforming a complex text into an equivalent version which would be easier to read or understand by automatic natural language processors. |
| Approach: | This tutorial provides an overview of automatic text simplification, which is the process of transforming a complex text into an equivalent version. |
| Outcome: | The aim of this paper is to provide a comprehensive overview of past and current research on automatic text simplification. |
CATS: A Tool for Customized Alignment of Text Simplification Corpora (L18-1)
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| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts (P19-1)
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| Challenge: | Existing models for assessing conceptual complexity of texts are lacking . conceptual complexity accounts for background knowledge necessary to understand mentioned concepts . |
| Approach: | They propose an unsupervised approach for assessing conceptual complexity of texts based on spreading activation using DBpedia knowledge graph as a proxy to long-term memory. |
| Outcome: | The proposed model outperforms current state of the art in assessing conceptual complexity of texts. |