Search Results for author: Wei-Jen Ko

Found 9 papers, 7 papers with code

Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion

1 code implementation12 Oct 2022 Wei-Jen Ko, Yating Wu, Cutter Dalton, Dananjay Srinivas, Greg Durrett, Junyi Jessy Li

Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme.

Dependency Parsing Question Answering

Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections

1 code implementation1 Nov 2021 Wei-Jen Ko, Cutter Dalton, Mark Simmons, Eliza Fisher, Greg Durrett, Junyi Jessy Li

While there has been substantial progress in text comprehension through simple factoid question answering, more holistic comprehension of a discourse still presents a major challenge (Dunietz et al., 2020).

Question Answering Reading Comprehension

Generating Dialogue Responses from a Semantic Latent Space

no code implementations EMNLP 2020 Wei-Jen Ko, Avik Ray, Yilin Shen, Hongxia Jin

Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary.

Dialogue Generation

Inquisitive Question Generation for High Level Text Comprehension

1 code implementation EMNLP 2020 Wei-Jen Ko, Te-Yuan Chen, Yiyan Huang, Greg Durrett, Junyi Jessy Li

Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems.

Question Generation Question-Generation +2

Domain Agnostic Real-Valued Specificity Prediction

1 code implementation13 Nov 2018 Wei-Jen Ko, Greg Durrett, Junyi Jessy Li

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse.

Dialogue Generation Informativeness +2

Learning Deep Latent Spaces for Multi-Label Classification

1 code implementation3 Jul 2017 Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance.

Classification General Classification +1

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