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.
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.
Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems.
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.
Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses.
Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse.
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.