77 research outputs found
Improving Natural Language Interaction with Robots Using Advice
Over the last few years, there has been growing interest in learning models
for physically grounded language understanding tasks, such as the popular
blocks world domain. These works typically view this problem as a single-step
process, in which a human operator gives an instruction and an automated agent
is evaluated on its ability to execute it. In this paper we take the first step
towards increasing the bandwidth of this interaction, and suggest a protocol
for including advice, high-level observations about the task, which can help
constrain the agent's prediction. We evaluate our approach on the blocks world
task, and show that even simple advice can help lead to significant performance
improvements. To help reduce the effort involved in supplying the advice, we
also explore model self-generated advice which can still improve results.Comment: Accepted as a short paper at NAACL 2019 (8 pages
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is
important to Natural Language Understanding. This paper introduces the task of
identifying if a desire expressed by a subject in a given short piece of text
was fulfilled. We propose various unstructured and structured models that
capture fulfillment cues such as the subject's emotional state and actions. Our
experiments with two different datasets demonstrate the importance of
understanding the narrative and discourse structure to address this task
An Interactive Framework for Profiling News Media Sources
The recent rise of social media has led to the spread of large amounts of
fake and biased news, content published with the intent to sway beliefs. While
detecting and profiling the sources that spread this news is important to
maintain a healthy society, it is challenging for automated systems.
In this paper, we propose an interactive framework for news media profiling.
It combines the strengths of graph based news media profiling models,
Pre-trained Large Language Models, and human insight to characterize the social
context on social media. Experimental results show that with as little as 5
human interactions, our framework can rapidly detect fake and biased news
media, even in the most challenging settings of emerging news events, where
test data is unseen
Interactively Learning Social Media Representations Improves News Source Factuality Detection
The rise of social media has enabled the widespread propagation of fake news,
text that is published with an intent to spread misinformation and sway
beliefs. Rapidly detecting fake news, especially as new events arise, is
important to prevent misinformation.
While prior works have tackled this problem using supervised learning
systems, automatedly modeling the complexities of the social media landscape
that enables the spread of fake news is challenging. On the contrary, having
humans fact check all news is not scalable. Thus, in this paper, we propose to
approach this problem interactively, where humans can interact to help an
automated system learn a better social media representation quality. On real
world events, our experiments show performance improvements in detecting
factuality of news sources, even after few human interactions.Comment: Accepted at Findings of IJCNLP-AACL 202
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