Search Results for author: Junnan Zhu

Found 11 papers, 4 papers with code

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization

1 code implementation EMNLP 2021 Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).

Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch

3 code implementations ICLR 2021 Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, Hongsheng Li

In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs.

Multimodal Sentence Summarization via Multimodal Selective Encoding

no code implementations COLING 2020 Haoran Li, Junnan Zhu, Jiajun Zhang, Xiaodong He, Chengqing Zong

Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.

Sentence Summarization

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity

no code implementations COLING 2020 Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.

Machine Translation Multi-Task Learning +1

Bridging the Modality Gap for Speech-to-Text Translation

no code implementations28 Oct 2020 Yuchen Liu, Junnan Zhu, Jiajun Zhang, Chengqing Zong

End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way.

Speech-to-Text Translation Translation

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization

no code implementations ACL 2020 Junnan Zhu, Yu Zhou, Jiajun Zhang, Cheng-qing Zong

Cross-lingual summarization aims at summarizing a document in one language (e. g., Chinese) into another language (e. g., English).

Translation

NCLS: Neural Cross-Lingual Summarization

1 code implementation IJCNLP 2019 Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Cheng-qing Zong

Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.

Machine Translation Multi-Task Learning +1

Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video

no code implementations EMNLP 2017 Haoran Li, Junnan Zhu, Cong Ma, Jiajun Zhang, Cheng-qing Zong

In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.

Automatic Speech Recognition Document Summarization +1

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