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- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions3,965
A thorough overview of hallucination detection methods and benchmarks is presented and the promising research directions on LLM hallucinations are highlighted, including hallucination in large vision-language models and understanding of knowledge boundaries in LLM hallucinations.
- LTP: A Chinese Language Technology Platform503
LTP (Language Technology Platform) is an integrated Chinese processing platform which includes a suite of high performance natural language processing modules and relevant corpora that achieved good results in some relevant evaluations, such as CoNLL and SemEval.
- Cross-lingual Dependency Parsing Based on Distributed Representations201
This paper provides two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space and bridges the lexical feature gap by using distributed feature representations and their composition.
- Application-driven statistical paraphrase generation137
A novel method for statistical paraphrase generation (SPG), which can achieve various applications based on a uniform statistical model, and naturally combine multiple resources to enhance the PG performance.
- Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing124
This paper proposes Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018).
- Effective Deep Memory Networks for Distant Supervised Relation Extraction122
This paper introduces a novel neural approach for distant supervised RE with special focus on attention mechanisms, which includes two major attention-based memory components, which are capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair.
- Target-Dependent Sentiment Classification with Long Short Term Memory.122
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
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- Constructing and Embedding Abstract Event Causality Networks from Text Snippets93
Experimental results demonstrate that the abstract causality network is effective for discovering high-level causality rules behind specific causal events, and the embedding models perform better than state-of-the-art link prediction techniques in predicting events.
- A Representation Learning Framework for Multi-Source Transfer Parsing93
This work presents a novel representation learning framework that allows multi-source transfer parsing using full lexical features straightforwardly and significantly outperforms the state-of-the-art transfer system proposed most recently.
- Character-Level Chinese Dependency Parsing76
This paper presents novel adaptations of two major shift-reduce dependency parsing algorithms to character-level parsing, and demonstrates improved performances over word-based parsing methods on the Chinese Treebank.
- Double-image encryption based on the affine transform and the gyrator transform75
Some numerical simulations have validated the feasibility of the proposed image encryption scheme and the parameters in the affine transform and the gyrator transform are regarded as the key for the encryption algorithm.
- Topic-to-Essay Generation with Neural Networks72
A multi-topic aware long short-term memory network that learns the weight of each topic and is sequentially updated during the decoding process and has the ability to generate essays that are not only coherent but also closely related to the input topics.
- Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)65
This work develops a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension’s representation.
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- The Role of "Condition"55
A semi-supervised Multi-Input Multi-Output sequence labeling model that learns complex dependencies between the sequence tags from multiple signals and generates output sequences for fact and condition tuples and has a self-training module of multiple strategies to leverage the massive scientific data for better performance when manual annotation is limited.
- Using Imageability and Topic Chaining to Locate Metaphors in Linguistic Corpora53
A novel approach to metaphor identification which is based on three intersecting methods: imageability, topic chaining, and semantic clustering is described, which hypothesis is that metaphors are likely to use highly imageable words that do not generally have a topical or semantic association with the surrounding context.
- Transition-Based Disfluency Detection using LSTMs51
This model incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information, which can capture non-local chunk-level features and is free for noise in syntax.
- Generalizing Syntactic Structures for Product Attribute Candidate Extraction51
A novel approach is proposed by generalizing syntactic structures of the product attributes with two strategies: intuitive heuristics and syntactic structure similarity, which shows that the proposed approach is effective.
- Leveraging multiple mt engines for paraphrase generation46
A method that leverages multiple machine translation (MT) engines for paraphrase generation (PG) using a multi-pivot approach and two kinds of techniques, namely the selection-based and decoding-based techniques, to produce a best paraphrase T for S using the candidates acquired in the first stage.
- Chinese Zero Pronoun Resolution with Deep Memory Network41
A deep memory network is built that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents and takes advantage of semantic information by using these continuous distributed representations.
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- ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning37
This work presents an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR), and proposes the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner.
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- Learning Document Representation for Deceptive Opinion Spam Detection33
A neural network is presented to learn document-level representation that not only learns to represent each sentence but also represent the whole document of the review and shows the better performance in deceptive spam detection.
- Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction32
A novel target-specific abstract-guided news document representation model that uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.
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- Generating Chinese named entity data from parallel corpora27
A general framework to generate large-scale NER training data from parallel corpora using an English-Chinese parallel corpus is presented and can collect high-quality labeled data and can help improve Chinese NER.
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- Dialogue Discourse-Aware Graph Convolutional Networks for Abstractive Meeting Summarization.24
A Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization is developed by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance.
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- Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning22
This work systematically analyzes the impact of 48 datasets from 5 major categories of pretraining data of LLMs and measures their impacts on LLMs using benchmarks about nine major categories of model capabilities to provide insights into the organization of data to support more efficient pretraining of LLMs.
- Distilling Knowledge for Search-based Structured Prediction22
Experimental results show that distillation can effectively improve the single model’s performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively and it outperforms the greedy structured prediction models in previous literatures.
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- A syntactic path-based hybrid neural network for negation scope detection20
A syntactic path-based hybrid neural network architecture, a novel approach to identify the scope of negation in a sentence, that combines a bidirectional long short-term memory (Bi-LSTM) network and a convolutional neural network (CNN).
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- Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling16
A composite kernel is presented through combining the hybrid convolution tree kernel method with a feature-based method extended by the polynomial kernel, which achieves better performance than each of the individual methods and outperforms the best reported system on the CoNLL-2005 corpus.
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- Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text15
A new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences and improves F1 score relatively by 4.2% on BioNLP2013 and by 6.2%" on a new bio-text dataset for tuple extraction.
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- Knowledge-Aware Conversational Semantic Parsing over Web Tables14
A knowledge-aware semantic parser to improve parsing performance by integrating various types of knowledge, including grammar knowledge, expert knowledge, and external resource knowledge, which is based on a decomposable model.
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- A Multi-View–Based Collective Entity Linking Method13
This article proposes a multi-view–based collective entity linking algorithm, which combines several views of entities into an objective function for entity linking and can acquire higher accuracy than many state-of-the-art entity linking methods.
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- Encoding syntactic representations with a neural network for sentiment collocation extraction10
The experimental results show that the proposed method can accurately capture the latent semantic features hidden behind the syntactic paths that neither the common feature-based methods nor the syntactical-path-based method can handle, and that it significantly outperforms numerous baselines and previous methods.
- Triple based Background Knowledge Ranking for Document Enrichment10
This study proposes a document enrichment framework which automatically extracts “argument1,predicate,argument2” triple from any text corpus as background knowledge, so that to ensure the compatibility with any resource and improve the enriching accuracy.
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- Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words7
Experimental results show that the proposed simple yet effective approach to helping models learn rare biomedical words during tuning with prompt can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.
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- Encoding Distributional Semantics into Triple-Based Knowledge Ranking for Document Enrichment7
Evaluated as a ranking problem, this model significantly outperforms multiple strong baselines and conducts a task-based evaluation by incorporating these triples as additional features into document classification and enhances the performance by 3.02%.
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- TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling5
This paper proposes a Token-level Dynamic Graph-Interactive Network (TD-GIN) for joint multiple intent detection and slot filling, where it model the interaction between multiple intents and each token slot in a unified graph architecture.
- Table-to-Text Generation via Row-Aware Hierarchical Encoder5
Three extractive evaluation metrics and human evaluation show that the proposed neural model has the ability to select salient records and the generated game summary is more accurate.
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- Improving the Downstream Performance of Mixture-of-Experts Transformers via Weak Vanilla Transformers4
This work introduces the concept of transfer capability distillation, positing that although vanilla models have weaker performance, they are effective teachers of transfer capability, and develops a specific distillation method and conducts experiments using the BERT architecture, showing significant improvements in the downstream performance of MoE models.
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- BUEES: a bottom-up event extraction system3
This paper presents BUEES, a bottom-up event extraction system, which extracts events from the web in a completely unsupervised way, and compares it to a state-of-the-art Chineseevent extraction system.
- A Semantics Oriented Grammar for Chinese Treebanking3
This paper introduces a semantics oriented grammar for Chinese, designed to provide basic supports for tasks such as automatic semantic parsing and sentence generation, and is the first Chinese grammar representation capable of direct transformation into logical forms.
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- Natural Logic at the Core: Dynamic Rewards for Entailment Tree Generation2
This work proposes a method that integrates natural logic principles into reinforcement learning, enabling dynamic reward computation to guide entailment tree generation, ensuring logical consistency across reasoning steps while improving inter-pretability and generalization.
- Dialogue Context Modelling for Action Item Detection: Solution for ICASSP 2023 Mug Challenge Track 52
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- Generating Triples Based on Dependency Parsing for Contradiction Detection2
The dp-triple extraction method is proposed to extract more contradiction pairs than present shallow semantic extraction methods like open IE or SRL and it is demonstrated the joint method is effective in detecting contradiction.
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- Multiple TreeBanks Integration for Chinese Phrase Structure Grammar Parsing Using Bagging2
Bagging is proposed to enhance the baseline performance of PSG parsing in CIPS-Bakeoff2012 Task3.0 and outperforms the best closed system by nearly 4% which uses a single model.
- Think straight or think again? Continual joint learning of deduction, abduction and induction1
This paper designs a three-level learning procedure for deduction, abduction and induction that can achieve comparable performance with GPT-3.5 in human evaluation and introduces induction as a fact retriever to support and guide the above dual learning.
- PRISM: A Benchmark for Unveiling Cross-modal Knowledge Inconsistency in Large Vision-Language Models1
- Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns1
A gradient-based method is employed, to dissect the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns, finding that LLMs selectively activate task-specific attention heads during SFT and activation patterns for complex tasks are combinations of basic task patterns.
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- Topical Key Concept Extraction from Folksonomy1
This paper proposes a novel framework to extract topical key concepts from folksonomy and a novel link weight function which combines the local structure information and global semantic similarity is proposed in importance score propagation.
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- SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation–
The proposed SL-BiLEM (Structured Learnable Behavior-in-the-Loop Epidemic Model), leveraging physical constraints as regularization for robust extrapolation, is established as an interpretable tool for public health decision-makers seeking accurate prediction and principled intervention planning.
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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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