68 citations · 173 across the 16 of their papers we have counts for
17 papers
Language Model Pre-Training with Sparse Latent Typing
Liliang Ren, Zixuan Zhang, Han Wang +3
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only foc…
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
Manling Li, Sha Li, Zhenhailong Wang +5
Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event sc…
A Research Platform for Multi-Robot Dialogue with Humans
Matthew Marge, Stephen Nogar, Cory J. Hayes +4
This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which u…
Visual Understanding and Narration: A Deeper Understanding and Explanation of Visual Scenes
Stephanie M. Lukin, Claire Bonial, Clare R. Voss
We describe the task of Visual Understanding and Narration, in which a robot (or agent) generates text for the images that it collects when navigating its environment, by answering…
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
Matthew Marge, Claire Bonial, Stephanie Lukin +11
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automa…
A Pipeline for Creative Visual Storytelling
Stephanie M. Lukin, Reginald Hobbs, Clare R. Voss
Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross…
Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial +5
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. mult…
ScoutBot: A Dialogue System for Collaborative Navigation
Stephanie M. Lukin, Felix Gervits, Cory J. Hayes +7
ScoutBot is a dialogue interface to physical and simulated robots that supports collaborative exploration of environments. The demonstration will allow users to issue unconstrained…
Object and Text-guided Semantics for CNN-based Activity Recognition
Sungmin Eum, Christopher Reale, Heesung Kwon +2
Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods…
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
Claire Bonial, Matthew Marge, Ron artstein +11
We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data colle…
Zero-Shot Transfer Learning for Event Extraction
Lifu Huang, Heng Ji, Kyunghyun Cho +1
Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. I…
Automated Phrase Mining from Massive Text Corpora
Jingbo Shang, Jialu Liu, Meng Jiang +3
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as informati…
Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue
Matthew Marge, Claire Bonial, Brendan Byrne +4
Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today. We…
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Xiang Ren, Zeqiu Wu, Wenqi He +5
Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have rel…
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
Douglas Summers-Stay, Clare Voss, Taylor Cassidy
The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness. We describe a system called Displacer for performing KB queries exte…
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Xiang Ren, Wenqi He, Meng Qu +3
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, t…
Scalable Topical Phrase Mining from Text Corpora
Ahmed El-Kishky, Yanglei Song, Chi Wang +2
While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the proble…