Search Results for author:
Found 14 papers, 8 papers with code
Stacked AMR Parsing with Silver Data
In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin.
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
Although DC-Match is a simple yet effective method for semantic matching, it highly depends on the external NER techniques to identify the keywords of sentences, which limits the performance of semantic matching for minor languages since satisfactory NER tools are usually hard to obtain.
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP).
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends
In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model.
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset
1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates.
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure.
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Dependency Parsing
Semantic Role Labeling (predicted predicates)
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.
Semantic Role Labeling with Heterogeneous Syntactic Knowledge
Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention.
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline.
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
Syntax-aware Neural Semantic Role Labeling
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
Dependency Parsing with Partial Annotations: An Empirical Comparison
This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA).
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
