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Claim this profileWorks135 from public data
- 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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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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