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- Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph63
A novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which is constructed under the guidance of the automatically selected pseudo triggers, which achieves competitive results.
- Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations62
This work proposes a novel method to integrate source-side syntax implicitly for NMT by using the intermediate hidden representations of a well-trained end-to-end dependency parser, referred to as syntax-aware word representations (SAWRs).
- TemplateGEC: Improving Grammatical Error Correction with Detection Template34
A novel method, TemplateGEC, is proposed, which capitalizes on the capabilities of both Seq2Edit and Sequ2Seq frameworks in error detection and correction respectively, and reveals the potential of the method in performing human-in-the-loop GEC.
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- Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering26
KEDKG is introduced, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers and surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.
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- Multi-objective Large Language Model Alignment with Hierarchical Experts19
This paper introduces \textit{HoE}(Hierarchical Mixture-of-Experts), a \textit{lightweight}, \textit{parameter-efficient}, and \textit{plug-and-play} approach that eliminates the need for model training, while enabling LLMs to adapt across the entire Pareto frontier and accommodate diverse user preferences.
- Overview of the NLPCC 2019 Shared Task: Cross-Domain Dependency Parsing19
An overview of the NLPCC 2019 shared task on cross-domain dependency parsing, including (1) the data annotation process, (2) task settings, methods, results, and analysis of submitted systems and the authors' recent work (Li+19), and discussions on related works and future directions.
- Supervised Treebank Conversion: Data and Approaches18
This work proposes two simple yet effective conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser that are superior to the widely used multi-task learning framework in multi-treebank exploitation and leads to significantly higher parsing accuracy.
- SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT16
This paper describes participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL), which includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR.
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- NEWS 2018 Whitepaper13
The objective of the shared task is to promote machine transliteration research by providing a common benchmarking platform for the community to evaluate the state-of-the-art technologies.
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- Dependency Parsing with Partial Annotations: An Empirical Comparison12
The results show that LLG par is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.
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- Towards Demonstration-Aware Large Language Models for Machine Translation7
A novel approach that integrates demonstration-aware training and inference strategies within the framework of tuning-based LTMs, hereby referred to as demonstration-aware LTMs, which mitigates the negative impacts traditionally associated with demonstrations and secures substantial improvements in translation accuracy, particularly in domain-specific and document-level translation tasks.
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- Chinese Zero Pronoun Resolution5
This article proposes a chain-to-chain approach to improve the performance of ZP resolution in three aspects: consecutive ZPs are clustered into coreferential chains, each working as one independent anaphor as a whole; additional features from ZP and common NP coreFerential chains are employed to better represent anaphors and their antecedent candidates, respectively.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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