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Found 14 papers, 3 papers with code
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
Structural Guidance for Transformer Language Models
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.
AMR Parsing with Action-Pointer Transformer
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)
Bootstrapping Multilingual AMR with Contextual Word Alignments
We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Pushing the Limits of AMR Parsing with Self-Learning
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)
Transition-based Parsing with Stack-Transformers
Modeling the parser state is key to good performance in transition-based parsing.
Event Presence Prediction Helps Trigger Detection Across Languages
The task of event detection and classification is central to most information retrieval applications.
GPT-too: A language-model-first approach for AMR-to-text generation
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #7 on
AMR-to-Text Generation
on LDC2017T10
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
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.
Ranked #8 on
AMR Parsing
on LDC2017T10
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging.