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Found 8 papers, 1 papers with code
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders
The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences *without access to labeled training data*.
How do people talk about images? A study on open-domain conversations with images.
This paper explores how humans conduct conversations with images by investigating an open-domain image conversation dataset, ImageChat.
LED: A Dataset for Life Event Extraction from Dialogs
Our proposed life event dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.
Breaking Down Multilingual Machine Translation
While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning.
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders
Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases.
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called "one hop".
MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
