Search Results for author: Tahira Naseem

Found 14 papers, 3 papers with code

Structural Guidance for Transformer Language Models

1 code implementation ACL 2021 Peng Qian, Tahira Naseem, Roger Levy, Ramón Fernandez Astudillo

Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data.

Language Modelling

AMR Parsing with Action-Pointer Transformer

no code implementations NAACL 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Radu Florian

In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.

Ranked #3 on AMR Parsing on LDC2020T02 (using extra training data)

AMR Parsing

Bootstrapping Multilingual AMR with Contextual Word Alignments

no code implementations EACL 2021 Janaki Sheth, Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.

Multilingual Word Embeddings Word Alignment

Pushing the Limits of AMR Parsing with Self-Learning

no code implementations Findings of the Association for Computational Linguistics 2020 Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.

 Ranked #1 on AMR Parsing on LDC2014T12 (F1 Full metric)

AMR Parsing Machine Translation +3

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

no code implementations ACL 2019 Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.

AMR Parsing

Cannot find the paper you are looking for? You can Submit a new open access paper.