activity
20152022
most citedGPT-3-driven pedagogical agents for training children's curious question-asking skills

122 citations · 553 across the 26 of their papers we have counts for

collaborators

26 papers

cs.CL2022★ 122 cited

GPT-3-driven pedagogical agents for training children's curious question-asking skills

Rania Abdelghani, Yen-Hsiang Wang, Xingdi Yuan +4

In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cue…

eess.AS2021★ 2 cited

Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis

Mu Yang, Shaojin Ding, Tianlong Chen +2

This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially…

cs.CL2021★ 1 cited

Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Rui Meng, Khushboo Thaker, Lei Zhang +4

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured…

cs.LG2021★ 11 cited

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations

Chaofan Chen, Kangcheng Lin, Cynthia Rudin +3

Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support to…

cs.LG2021★ 2 cited

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning

Tong Wang, Jingyi Yang, Yunyi Li +1

We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE predict…

cs.LG2020★ 4 cited

Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness

Tong Wang, Maytal Saar-Tsechansky

We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature…