Search Results for author:
Found 13 papers, 5 papers with code
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions.
Language Model Pre-Training with Sparse Latent Typing
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #6 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Visual Understanding and Narration: A Deeper Understanding and Explanation of Visual Scenes
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 open-ended questions, such as 'what happens, or might have happened, here?'
ScoutBot: A Dialogue System for Collaborative Navigation
ScoutBot is a dialogue interface to physical and simulated robots that supports collaborative exploration of environments.
A Pipeline for Creative Visual Storytelling
We have piloted this design for a sequence of images in an annotation task.
Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue.
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
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.
Zero-Shot Transfer Learning for Event Extraction
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.
Automated Phrase Mining from Massive Text Corpora
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Ranked #11 on
Relation Extraction
on NYT11-HRL
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.
