Search Results for author: Tingwen Liu

Found 37 papers, 22 papers with code

Maximal Clique Based Non-Autoregressive Open Information Extraction

no code implementations EMNLP 2021 Bowen Yu, Yucheng Wang, Tingwen Liu, Hongsong Zhu, Limin Sun, Bin Wang

However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.

Open Information Extraction Sentence

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

3 code implementations ACL 2022 Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu

Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.

Language Modelling Sentence

CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process

no code implementations23 Jan 2024 XiaoDong Li, Jiawei Sheng, Jiangxia Cao, Wenyuan Zhang, Quangang Li, Tingwen Liu

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.


Adaptive Data Augmentation for Aspect Sentiment Quad Prediction

1 code implementation12 Jan 2024 Wenyuan Zhang, Xinghua Zhang, Shiyao Cui, Kun Huang, Xuebin Wang, Tingwen Liu

Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis.

Aspect-Based Sentiment Analysis Data Augmentation +2

FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

1 code implementation30 Nov 2023 Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu

The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content.

Fairness Instruction Following +1

Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction

no code implementations4 Aug 2023 Shiyao Cui, Xin Cong, Jiawei Sheng, Xuebin Wang, Tingwen Liu, Jinqiao Shi

In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.

Wider and Deeper LLM Networks are Fairer LLM Evaluators

1 code implementation3 Aug 2023 Xinghua Zhang, Bowen Yu, Haiyang Yu, Yangyu Lv, Tingwen Liu, Fei Huang, Hongbo Xu, Yongbin Li

Each perspective corresponds to the role of a specific LLM neuron in the first layer.

Universal Information Extraction with Meta-Pretrained Self-Retrieval

no code implementations18 Jun 2023 Xin Cong. Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

Inspired by the fact that large amount of knowledge are stored in the pretrained language models~(PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE.


Contrastive Cross-Domain Sequential Recommendation

1 code implementation8 Apr 2023 Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, Bin Wang

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.

Sequential Recommendation

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

no code implementations5 Apr 2023 Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng, Quangang Li, Tingwen Liu, Jinqiao Shi

For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.

named-entity-recognition Named Entity Recognition +3

Event Causality Extraction with Event Argument Correlations

1 code implementation COLING 2022 Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi

Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.

Event Causality Identification

Towards Generalized Open Information Extraction

no code implementations29 Nov 2022 Bowen Yu, Zhenyu Zhang, Jingyang Li, Haiyang Yu, Tingwen Liu, Jian Sun, Yongbin Li, Bin Wang

Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.

Open Information Extraction

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

no code implementations14 Jul 2022 Zhenyu Zhang, Bowen Yu, Haiyang Yu, Tingwen Liu, Cheng Fu, Jingyang Li, Chengguang Tang, Jian Sun, Yongbin Li

In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.

Language Modelling

Relation-Guided Few-Shot Relational Triple Extraction

1 code implementation SIGIR 2022 Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang

To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.

Relation RTE +1

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

1 code implementation31 Mar 2022 Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, Bin Wang

As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.

Recommendation Systems

Document-Level Event Extraction via Human-Like Reading Process

no code implementations7 Feb 2022 Shiyao Cui, Xin Cong, Bowen Yu, Tingwen Liu, Yucheng Wang, Jinqiao Shi

Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.

Document-level Event Extraction Event Extraction

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

1 code implementation EMNLP 2021 Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Mengge Xue, Hongbo Xu

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.

Denoising named-entity-recognition +2

Deep Structural Point Process for Learning Temporal Interaction Networks

1 code implementation8 Jul 2021 Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang

A temporal interaction network consists of a series of chronological interactions between users and items.

Bipartite Graph Embedding via Mutual Information Maximization

1 code implementation10 Dec 2020 Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang

However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved.

Graph Embedding Link Prediction

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

no code implementations3 Dec 2020 Shiyao Cui, Bowen Yu, Xin Cong, Tingwen Liu, Quangang Li, Jinqiao Shi

A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.

Event Detection Graph Attention +1

Document-level Relation Extraction with Dual-tier Heterogeneous Graph

no code implementations COLING 2020 Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo

Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.

Decision Making Document-level Relation Extraction +2

Porous Lattice Transformer Encoder for Chinese NER

no code implementations COLING 2020 Xue Mengge, Bowen Yu, Tingwen Liu, Yue Zhang, Erli Meng, Bin Wang

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information.


TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

1 code implementation COLING 2020 Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, Limin Sun

To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.

Relation Relation Extraction

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.

Knowledge Graph Completion Link Prediction

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

1 code implementation23 Jun 2020 Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang

We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.

Classification Clustering +2

Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention

1 code implementation ACL 2020 Yanzeng Li, Bowen Yu, Mengge Xue, Tingwen Liu

Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese.

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

1 code implementation10 Sep 2019 Bowen Yu, Zhen-Yu Zhang, Xiaobo Shu, Yubin Wang, Tingwen Liu, Bin Wang, Sujian Li

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.

Relation Extraction

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