52 citations · 65 across the 7 of their papers we have counts for
8 papers
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion
Wei-Jen Ko, Yating Wu, Cutter Dalton +3
Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, makin…
Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections
Wei-Jen Ko, Cutter Dalton, Mark Simmons +3
While there has been substantial progress in text comprehension through simple factoid question answering, more holistic comprehension of a discourse still presents a major challen…
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala +6
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are lin…
Generating Dialogue Responses from a Semantic Latent Space
Wei-Jen Ko, Avik Ray, Yilin Shen +1
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. However, a good respo…
Inquisitive Question Generation for High Level Text Comprehension
Wei-Jen Ko, Te-Yuan Chen, Yiyan Huang +2
Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill…
Assessing Discourse Relations in Language Generation from GPT-2
Wei-Jen Ko, Junyi Jessy Li
Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models. GPT-2, in particular, is suited for generation tasks given its left-to-righ…
Domain Agnostic Real-Valued Specificity Prediction
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. While this information is useful for many downstream…
Learning Deep Latent Spaces for Multi-Label Classification
Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko +1
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 i…