46 citations · 259 across the 35 of their papers we have counts for
36 papers
CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization
Min Xiao, Junnan Zhu, Haitao Lin +2
Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear. Existing multimodal summarization approaches focus on designing t…
Multi-Teacher Knowledge Distillation For Text Image Machine Translation
Cong Ma, Yaping Zhang, Mei Tu +3
Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentenc…
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation
Cong Ma, Yaping Zhang, Mei Tu +3
Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and o…
Learning to Select the Next Reasonable Mention for Entity Linking
Jian Sun, Yu Zhou, Chengqing Zong
Entity linking aims to establish a link between entity mentions in a document and the corresponding entities in knowledge graphs (KGs). Previous work has shown the effectiveness of…
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Haitao Lin, Liqun Ma, Junnan Zhu +4
Dialogue summarization has drawn much attention recently. Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowin…
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
Haitao Lin, Lu Xiang, Yu Zhou +2
Spoken Language Understanding (SLU) is one essential step in building a dialogue system. Due to the expensive cost of obtaining the labeled data, SLU suffers from the data scarcity…
Bridging the Modality Gap for Speech-to-Text Translation
Yuchen Liu, Junnan Zhu, Jiajun Zhang +1
End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structur…
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Xiaomian Kang, Yang Zhao, Jiajun Zhang +1
Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglec…
Neural Machine Translation: Challenges, Progress and Future
Jiajun Zhang, Chengqing Zong
Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping…
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
Yuchen Liu, Jiajun Zhang, Hao Xiong +5
Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional p…
NCLS: Neural Cross-Lingual Summarization
Junnan Zhu, Qian Wang, Yining Wang +4
Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. Existing methods simply divide this tas…
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Weikang Wang, Jiajun Zhang, Qian Li +2
Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we f…
Understanding Memory Modules on Learning Simple Algorithms
Kexin Wang, Yu Zhou, Shaonan Wang +2
Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms. However, we still have little understanding o…
Sequence Generation: From Both Sides to the Middle
Long Zhou, Jiajun Zhang, Chengqing Zong +1
The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually…
Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Weikang Wang, Jiajun Zhang, Qian Li +3
Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands a…
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
He Bai, Yu Zhou, Jiajun Zhang +1
Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the pre…
Synchronous Bidirectional Neural Machine Translation
Long Zhou, Jiajun Zhang, Chengqing Zong
Existing approaches to neural machine translation (NMT) generate the target language sequence token by token from left to right. However, this kind of unidirectional decoding frame…
End-to-End Speech Translation with Knowledge Distillation
Yuchen Liu, Hao Xiong, Zhongjun He +4
End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years. Compared to…
Synchronous Bidirectional Inference for Neural Sequence Generation
Jiajun Zhang, Long Zhou, Yang Zhao +1
In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result…
Language-Independent Representor for Neural Machine Translation
Long Zhou, Yuchen Liu, Jiajun Zhang +2
Current Neural Machine Translation (NMT) employs a language-specific encoder to represent the source sentence and adopts a language-specific decoder to generate target translation.…
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
He Bai, Yu Zhou, Jiajun Zhang +3
To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. Trans…
Phrase Table as Recommendation Memory for Neural Machine Translation
Yang Zhao, Yining Wang, Jiajun Zhang +1
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent b…
Learning Multimodal Word Representation via Dynamic Fusion Methods
Shaonan Wang, Jiajun Zhang, Chengqing Zong
Multimodal models have been proven to outperform text-based models on learning semantic word representations. Almost all previous multimodal models typically treat the representati…
Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics
Shaonan Wang, Jiajun Zhang, Nan Lin +1
Multimodal models have been proven to outperform text-based approaches on learning semantic representations. However, it still remains unclear what properties are encoded in multim…