76 research outputs found
Online Deception Detection Refueled by Real World Data Collection
The lack of large realistic datasets presents a bottleneck in online
deception detection studies. In this paper, we apply a data collection method
based on social network analysis to quickly identify high-quality deceptive and
truthful online reviews from Amazon. The dataset contains more than 10,000
deceptive reviews and is diverse in product domains and reviewers. Using this
dataset, we explore effective general features for online deception detection
that perform well across domains. We demonstrate that with generalized features
- advertising speak and writing complexity scores - deception detection
performance can be further improved by adding additional deceptive reviews from
assorted domains in training. Finally, reviewer level evaluation gives an
interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing
(RANLP) 201
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
We argue that semantic meanings of a sentence or clause can not be
interpreted independently from the rest of a paragraph, or independently from
all discourse relations and the overall paragraph-level discourse structure.
With the goal of improving implicit discourse relation classification, we
introduce a paragraph-level neural networks that model inter-dependencies
between discourse units as well as discourse relation continuity and patterns,
and predict a sequence of discourse relations in a paragraph. Experimental
results show that our model outperforms the previous state-of-the-art systems
on the benchmark corpus of PDTB.Comment: Accepted by NAACL 201
X-VoE: Measuring eXplanatory Violation of Expectation in Physical Events
Intuitive physics is pivotal for human understanding of the physical world,
enabling prediction and interpretation of events even in infancy. Nonetheless,
replicating this level of intuitive physics in artificial intelligence (AI)
remains a formidable challenge. This study introduces X-VoE, a comprehensive
benchmark dataset, to assess AI agents' grasp of intuitive physics. Built on
the developmental psychology-rooted Violation of Expectation (VoE) paradigm,
X-VoE establishes a higher bar for the explanatory capacities of intuitive
physics models. Each VoE scenario within X-VoE encompasses three distinct
settings, probing models' comprehension of events and their underlying
explanations. Beyond model evaluation, we present an explanation-based learning
system that captures physics dynamics and infers occluded object states solely
from visual sequences, without explicit occlusion labels. Experimental outcomes
highlight our model's alignment with human commonsense when tested against
X-VoE. A remarkable feature is our model's ability to visually expound VoE
events by reconstructing concealed scenes. Concluding, we discuss the findings'
implications and outline future research directions. Through X-VoE, we catalyze
the advancement of AI endowed with human-like intuitive physics capabilities.Comment: 19 pages, 16 figures, selected for an Oral presentation at ICCV 2023.
Project link: https://pku.ai/publication/intuitive2023iccv
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning
Relational graph neural networks have garnered particular attention to encode
graph context in knowledge graphs (KGs). Although they achieved competitive
performance on small KGs, how to efficiently and effectively utilize graph
context for large KGs remains an open problem. To this end, we propose the
Relation-based Embedding Propagation (REP) method. It is a post-processing
technique to adapt pre-trained KG embeddings with graph context. As relations
in KGs are directional, we model the incoming head context and the outgoing
tail context separately. Accordingly, we design relational context functions
with no external parameters. Besides, we use averaging to aggregate context
information, making REP more computation-efficient. We theoretically prove that
such designs can avoid information distortion during propagation. Extensive
experiments also demonstrate that REP has significant scalability while
improving or maintaining prediction quality. Notably, it averagely brings about
10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and
takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.Comment: Accepted by IJCAI 202
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