Search Results for author: Weiran Xu

Found 31 papers, 12 papers with code

Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots

no code implementations EMNLP 2020 Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, Weiran Xu

Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.

Slot Filling

Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization

no code implementations Findings (EMNLP) 2021 Lulu Zhao, Weihao Zeng, Weiran Xu, Jun Guo

Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process.

Abstractive Dialogue Summarization Contrastive Learning

A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

1 code implementation Findings (EMNLP) 2021 Yuejie Lei, Fujia Zheng, Yuanmeng Yan, Keqing He, Weiran Xu

Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding.

Abstractive Dialogue Summarization Abstractive Text Summarization +1

Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization

no code implementations EMNLP 2021 Zhiyuan Zeng, Jiaze Chen, Weiran Xu, Lei LI

Based on the artificial dataset, we train an evaluation model that can not only make accurate and robust factual consistency discrimination but is also capable of making interpretable factual errors tracing by backpropagated gradient distribution on token embeddings.

Abstractive Text Summarization Data Augmentation

Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

1 code implementation EMNLP 2021 Yuanmeng Yan, Rumei Li, Sirui Wang, Hongzhi Zhang, Zan Daoguang, Fuzheng Zhang, Wei Wu, Weiran Xu

The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB).

Question Answering Relation Extraction

A Robust Contrastive Alignment Method For Multi-Domain Text Classification

no code implementations26 Apr 2022 Xuefeng Li, Hao Lei, LiWen Wang, Guanting Dong, Jinzheng Zhao, Jiachi Liu, Weiran Xu, Chunyun Zhang

In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning.

Classification Contrastive Learning +1

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

1 code implementation9 Apr 2022 Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, Yanan Wu

The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings.

Domain Adaptation

InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER

no code implementations8 Mar 2022 LiWen Wang, Rumei Li, Yang Yan, Yuanmeng Yan, Sirui Wang, Wei Wu, Weiran Xu

Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.

Entity Typing Few-Shot Learning +6

TODSum: Task-Oriented Dialogue Summarization with State Tracking

no code implementations25 Oct 2021 Lulu Zhao, Fujia Zheng, Keqing He, Weihao Zeng, Yuejie Lei, Huixing Jiang, Wei Wu, Weiran Xu, Jun Guo, Fanyu Meng

Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.

Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack

1 code implementation NAACL 2021 LiWen Wang, Yuanmeng Yan, Keqing He, Yanan Wu, Weiran Xu

In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.

Adversarial Attack Representation Learning

Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge

no code implementations1 Jan 2021 Lulu Zhao, Zeyuan Yang, Weiran Xu, Sheng Gao, Jun Guo

In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge.

Abstractive Dialogue Summarization

Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words

no code implementations COLING 2020 Lulu Zhao, Weiran Xu, Jun Guo

A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue.

Abstractive Dialogue Summarization Graph Attention

Finding Salient Context based on Semantic Matching for Relevance Ranking

no code implementations3 Sep 2019 Yuanyuan Qi, Jiayue Zhang, Weiran Xu, Jun Guo

In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval.

Information Retrieval Semantic Similarity +1

Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network

no code implementations NAACL 2018 Chenliang Li, Weiran Xu, Si Li, Sheng Gao

Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.

Abstractive Text Summarization

DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

no code implementations ACL 2018 Pengda Qin, Weiran Xu, William Yang Wang

Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.

Relation Classification

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

2 code implementations ACL 2018 Pengda Qin, Weiran Xu, William Yang Wang

The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

reinforcement-learning Relation Extraction

Neural Regularized Domain Adaptation for Chinese Word Segmentation

no code implementations WS 2017 Zuyi Bao, Si Li, Weiran Xu, Sheng Gao

For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature.

Chinese Word Segmentation Domain Adaptation +1

Bitext Name Tagging for Cross-lingual Entity Annotation Projection

no code implementations COLING 2016 Dongxu Zhang, Boliang Zhang, Xiaoman Pan, Xiaocheng Feng, Heng Ji, Weiran Xu

Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.

named-entity-recognition NER +1

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