Search Results for author: Liangjun Zang

Found 11 papers, 7 papers with code

Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs

no code implementations22 Mar 2024 Xiaobin Zhang, Liangjun Zang, Qianwen Liu, Shuchong Wei, Songlin Hu

With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge.

Event Relation Extraction Prompt Engineering +3

Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

1 code implementation ACL 2022 Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Zhongyuan Wang, Songlin Hu

Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary.

Data Augmentation Language Modelling +3

DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings

1 code implementation10 Dec 2021 Chaochen Gao, Xing Wu, Peng Wang, Jue Wang, Liangjun Zang, Zhongyuan Wang, Songlin Hu

To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE.

Contrastive Learning Knowledge Distillation +5

ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding

2 code implementations COLING 2022 Xing Wu, Chaochen Gao, Liangjun Zang, Jizhong Han, Zhongyuan Wang, Songlin Hu

Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.

Contrastive Learning Data Augmentation +5

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

1 code implementation25 May 2020 Dongjun Wei, Yaxin Liu, Fuqing Zhu, Liangjun Zang, Wei Zhou, Yijun Lu, Songlin Hu

In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods.

feature selection Word Embeddings

Data Augmentation for Copy-Mechanism in Dialogue State Tracking

no code implementations22 Feb 2020 Xiaohui Song, Liangjun Zang, Yipeng Su, Xing Wu, Jizhong Han, Songlin Hu

While several state-of-the-art approaches to dialogue state tracking (DST) have shown promising performances on several benchmarks, there is still a significant performance gap between seen slot values (i. e., values that occur in both training set and test set) and unseen ones (values that occur in training set but not in test set).

Data Augmentation Dialogue State Tracking

TransSent: Towards Generation of Structured Sentences with Discourse Marker

no code implementations5 Sep 2019 Xing Wu, Dongjun Wei, Liangjun Zang, Jizhong Han, Songlin Hu

Automatic and human evaluation results show that TransSent can generate structured sentences with high quality, and has certain scalability in different tasks.

Dialogue Generation Sentence

ESA: Entity Summarization with Attention

2 code implementations25 May 2019 Dongjun Wei, Yaxin Liu, Fuqing Zhu, Liangjun Zang, Wei Zhou, Jizhong Han, Songlin Hu

Entity summarization aims at creating brief but informative descriptions of entities from knowledge graphs.

Clustering Knowledge Graphs

Conditional BERT Contextual Augmentation

5 code implementations17 Dec 2018 Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, Songlin Hu

BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model.

Data Augmentation Language Modelling +1

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