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20162021
most citedGenerative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability

13 citations · 20 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CL2021

SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering

Xiaopeng Lu, Kyusong Lee, Tiancheng Zhao

Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and is often difficult to re…

cs.CL20204 cited

SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

Tiancheng Zhao, Xiaopeng Lu, Kyusong Lee

We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many n…

cs.CL20203 cited

Talk to Papers: Bringing Neural Question Answering to Academic Search

Tianchang Zhao, Kyusong Lee

We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable…

cs.CL2018

Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

Tiancheng Zhao, Kyusong Lee, Maxine Eskenazi

The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable acti…

cs.CL201713 cited

Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability

Tiancheng Zhao, Allen Lu, Kyusong Lee +1

Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper p…