1 code implementation • 12 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.
1 code implementation • 1 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).
1 code implementation • ACL 2021 • Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.
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.
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.
no code implementations • INLG (ACL) 2020 • Wei-Jen Ko, Junyi Jessy Li
Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models.
1 code implementation • NAACL 2019 • Wei-Jen Ko, Greg Durrett, Junyi Jessy Li
Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses.
1 code implementation • 13 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.
1 code implementation • 3 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.