Search Results for author: Leonardo F. R. Ribeiro

Found 13 papers, 10 papers with code

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, 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, Mahima 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, 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

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Text Generation

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

1 code implementation13 Apr 2022 Leonardo F. R. Ribeiro, Mengwen Liu, Iryna Gurevych, Markus Dreyer, Mohit Bansal

Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.

Abstractive Text Summarization

UKP-SQUARE: An Online Platform for Question Answering Research

1 code implementation ACL 2022 Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Max Eichler, Gregor Geigle, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Gül Şahin, Iryna Gurevych

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).

Information Retrieval Question Answering

Structural Adapters in Pretrained Language Models for AMR-to-text Generation

1 code implementation EMNLP 2021 Leonardo F. R. Ribeiro, Yue Zhang, Iryna Gurevych

Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.

AMR-to-Text Generation Data-to-Text Generation +1

Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers

1 code implementation EMNLP (DeeLIO) 2020 Anne Lauscher, Olga Majewska, Leonardo F. R. Ribeiro, Iryna Gurevych, Nikolai Rozanov, Goran Glavaš

Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models.

Common Sense Reasoning

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

1 code implementation29 Jan 2020 Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent, Iryna Gurevych

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations.

Graph-to-Sequence KG-to-Text Generation +1

Enhancing AMR-to-Text Generation with Dual Graph Representations

1 code implementation IJCNLP 2019 Leonardo F. R. Ribeiro, Claire Gardent, Iryna Gurevych

Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.

AMR-to-Text Generation Data-to-Text Generation +1

struc2vec: Learning Node Representations from Structural Identity

5 code implementations11 Apr 2017 Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

Graph Embedding Network Embedding +1

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