RWKV: Reinventing RNNs for the Transformer Era

5 code implementations • 22 May 2023 • Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Jiaju Lin, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Bolun Wang, Johan S. Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

Computational Efficiency Natural Language Inference

UniMorph 3.0: Universal Morphology

no code implementations • LREC 2020 • Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ern{\v{s}}treits, Yuval Pinter, Cass Jacobs, ra L., Ryan Cotterell, Mans Hulden, David Yarowsky

The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.

Technical notes: Syntax-aware Representation Learning With Pointer Networks

no code implementations • 17 Mar 2019 • Matteo Grella

This is a work-in-progress report, which aims to share preliminary results of a novel sequence-to-sequence schema for dependency parsing that relies on a combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in which the final softmax function has been replaced with the logistic regression.

Dependency Parsing regression +1

Non-Projective Dependency Parsing via Latent Heads Representation (LHR)

1 code implementation • 6 Feb 2018 • Matteo Grella, Simone Cangialosi

In this paper, we introduce a novel approach based on a bidirectional recurrent autoencoder to perform globally optimized non-projective dependency parsing via semi-supervised learning.

Dependency Parsing

Notes About a More Aware Dependency Parser

no code implementations • 20 Jul 2015 • Matteo Grella

In this paper I explain the reasons that led me to research and conceive a novel technology for dependency parsing, mixing together the strengths of data-driven transition-based and constraint-based approaches.

Dependency Parsing