Search Results for author: Chengqing Zong

Found 19 papers, 6 papers with code

Entity-level Cross-modal Learning Improves Multi-modal Machine Translation

no code implementations Findings (EMNLP) 2021 Xin Huang, Jiajun Zhang, Chengqing Zong

Inspired by the findings of (CITATION) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation.

Machine Translation Representation Learning +1

Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding

1 code implementation Findings (ACL) 2022 Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong

More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity.

Language Modelling

A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization

no code implementations EMNLP 2020 Jinghui Yan, Yining Wang, Lu Xiang, Yu Zhou, Chengqing Zong

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing.

Medical Procedure

How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models?

no code implementations LREC 2022 Xiaohan Zhang, Shaonan Wang, Chengqing Zong

Based on these results, we suggest a block-wise cross-validation training method and an adequate data size for increasing the performance of linear encoding models.

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation

1 code implementation18 Oct 2022 Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang, Wei Luo, Zhongqiang Huang, Chengqing Zong

Existing zero-shot methods fail to align the two modalities of speech and text into a shared semantic space, resulting in much worse performance compared to the supervised ST methods.

Automatic Speech Recognition Machine Translation +3

Instance-aware Prompt Learning for Language Understanding and Generation

1 code implementation18 Jan 2022 Feihu Jin, Jinliang Lu, Jiajun Zhang, Chengqing Zong

Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token.

Few-Shot Learning

Learning to Select the Next Reasonable Mention for Entity Linking

no code implementations8 Dec 2021 Jian Sun, Yu Zhou, Chengqing Zong

To address the problem, we propose a novel model, called DyMen, to dynamically adjust the subsequent linking target based on the previously linked entities via reinforcement learning, enabling the model to select a link target that can fully use previously linked information.

Entity Linking Knowledge Graphs +1

Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models

no code implementations NeurIPS Workshop AI4Scien 2021 Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong

However, most of the existing studies have focused on discriminating which one in two stimuli corresponds to the given brain image, which is far from directly generating text from neural activities.

Text Generation

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).

Distributed Representations of Emotion Categories in Emotion Space

no code implementations ACL 2021 Xiangyu Wang, Chengqing Zong

Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories.

Emotion Classification

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 +2

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

Dual Attention Network for Cross-lingual Entity Alignment

no code implementations COLING 2020 Jian Sun, Yu Zhou, Chengqing Zong

The hierarchical attention adaptively aggregates the low-hierarchy and the high-hierarchy information, which is beneficial to balance the neighborhood information of counterpart entities and distinguish non-counterpart entities with similar structures.

Entity Alignment Graph Attention +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

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