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

4 citations · 10 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20221 cited

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation

Chen Wang, Yuchen Liu, Boxing Chen +4

End-to-end Speech Translation (ST) aims at translating the source language speech into target language text without generating the intermediate transcriptions. However, the trainin…

cs.CL20221 cited

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions

Haitao Lin, Junnan Zhu, Lu Xiang +3

Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing e…

cs.CL2021

Exploiting Curriculum Learning in Unsupervised Neural Machine Translation

Jinliang Lu, Jiajun Zhang

Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability. However, a…

cs.CL20214 cited

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…

cs.CL20212 cited

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…

cs.CL20212 cited

Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

Yangyifan Xu, Yijin Liu, Fandong Meng +3

Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training…