157 citations · 160 across the 6 of their papers we have counts for
6 papers
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems
Ting-Wei Wu, Fatemeh Sheikholeslami, Mohammad Kachuee +2
Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such syst…
Open World Classification with Adaptive Negative Samples
Ke Bai, Guoyin Wang, Jiwei Li +5
Open world classification is a task in natural language processing with key practical relevance and impact. Since the open or {\em unknown} category data only manifests in the infe…
Cluster-Guided Label Generation in Extreme Multi-Label Classification
Taehee Jung, Joo-Kyung Kim, Sungjin Lee +1
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treatin…
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +6
This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone do…
Learning Personalized Representations using Graph Convolutional Network
Hongyu Shen, Jinoh Oh, Shuai Zhao +3
Generating representations that precisely reflect customers' behavior is an important task for providing personalized skill routing experience in Alexa. Currently, Dynamic Routing…
Why We Need New Evaluation Metrics for NLG
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry +1
The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods:…