most citedDiversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder

9 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2021

A Conditional Generative Matching Model for Multi-lingual Reply Suggestion

Budhaditya Deb, Guoqing Zheng, Milad Shokouhi +1

We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and se…

cs.CL2021

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

Yusen Zhang, Ansong Ni, Tao Yu +6

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…

cs.CL2021

Language Scaling for Universal Suggested Replies Model

Qianlan Ying, Payal Bajaj, Budhaditya Deb +7

We consider the problem of scaling automated suggested replies for Outlook email system to multiple languages. Faced with increased compute requirements and low resources for langu…

cs.CL2021

A Dataset and Baselines for Multilingual Reply Suggestion

Mozhi Zhang, Wei Wang, Budhaditya Deb +3

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion da…

cs.CL20199 cited

Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder

Budhaditya Deb, Peter Bailey, Milad Shokouhi

We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based info…