Paraphrase identification with deep learning: A review of datasets and methods
C Zhou, C Qiu, L Liang, DE Acuna - arXiv preprint arXiv:2212.06933, 2022 - arxiv.org
The rapid progress of Natural Language Processing (NLP) technologies has led to the
widespread availability and effectiveness of text generation tools such as ChatGPT and …
widespread availability and effectiveness of text generation tools such as ChatGPT and …
Neural amr: Sequence-to-sequence models for parsing and generation
Sequence-to-sequence models have shown strong performance across a broad range of
applications. However, their application to parsing and generating text usingAbstract …
applications. However, their application to parsing and generating text usingAbstract …
AMR parsing as sequence-to-graph transduction
We propose an attention-based model that treats AMR parsing as sequence-to-graph
transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic …
transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic …
An incremental parser for abstract meaning representation
Meaning Representation (AMR) is a semantic representation for natural language that
embeds annotations related to traditional tasks such as named entity recognition, semantic …
embeds annotations related to traditional tasks such as named entity recognition, semantic …
Broad-coverage semantic parsing as transduction
We unify different broad-coverage semantic parsing tasks under a transduction paradigm,
and propose an attention-based neural framework that incrementally builds a meaning …
and propose an attention-based neural framework that incrementally builds a meaning …
A transition-based directed acyclic graph parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable framework for
semantic representation, which builds on extensive typological work and supports rapid …
semantic representation, which builds on extensive typological work and supports rapid …
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 …
Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch …
AMR parsing using stack-LSTMs
M Ballesteros, Y Al-Onaizan - arXiv preprint arXiv:1707.07755, 2017 - arxiv.org
We present a transition-based AMR parser that directly generates AMR parses from plain
text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our …
text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our …
Text summarization using abstract meaning representation
With an ever increasing size of text present on the Internet, automatic summary generation
remains an important problem for natural language understanding. In this work we explore a …
remains an important problem for natural language understanding. In this work we explore a …
Better transition-based AMR parsing with a refined search space
This paper introduces a simple yet effective transition-based system for Abstract Meaning
Representation (AMR) parsing. We argue that a well-defined search space involved in a …
Representation (AMR) parsing. We argue that a well-defined search space involved in a …