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Found 15 papers, 4 papers with code
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages.
Exploiting Rich Syntax for Better Knowledge Base Question Answering
Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees. In this paper, we propose an approach to learn syntax-based representations for KBQA.
Improving AMR Parsing with Sequence-to-Sequence Pre-training
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Ranked #15 on
AMR Parsing
on LDC2017T10
(using extra training data)
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
Learning When to Attend for Neural Machine Translation
In the past few years, attention mechanisms have become an indispensable component of end-to-end neural machine translation models.
Modeling Source Syntax for Neural Machine Translation
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.
