48 research outputs found
Automatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two
physical objects that are typically found near each other in real life. In this
paper, we study how to automatically extract such relationship through a
sentence-level relation classifier and aggregating the scores of entity pairs
from a large corpus. Also, we release two benchmark datasets for evaluation and
future research.Comment: Accepted by ACL 2018. A preliminary version is presented on
AKBC@NIPS'1
ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation
The pre-trained conversational models still fail to capture the implicit
commonsense (CS) knowledge hidden in the dialogue interaction, even though they
were pre-trained with an enormous dataset. In order to build a dialogue agent
with CS capability, we firstly inject external knowledge into a pre-trained
conversational model to establish basic commonsense through efficient Adapter
tuning (Section 4). Secondly, we propose the ``two-way learning'' method to
enable the bidirectional relationship between CS knowledge and sentence pairs
so that the model can generate a sentence given the CS triplets, also generate
the underlying CS knowledge given a sentence (Section 5). Finally, we leverage
this integrated CS capability to improve open-domain dialogue response
generation so that the dialogue agent is capable of understanding the CS
knowledge hidden in dialogue history on top of inferring related other
knowledge to further guide response generation (Section 6). The experiment
results demonstrate that CS\_Adapter fusion helps DialoGPT to be able to
generate series of CS knowledge. And the DialoGPT+CS\_Adapter response model
adapted from CommonGen training can generate underlying CS triplets that fits
better to dialogue context.Comment: this is a long paper, the short version was accepted by SemDial 202
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1
Scene Graph Generation with External Knowledge and Image Reconstruction
Scene graph generation has received growing attention with the advancements
in image understanding tasks such as object detection, attributes and
relationship prediction,~\etc. However, existing datasets are biased in terms
of object and relationship labels, or often come with noisy and missing
annotations, which makes the development of a reliable scene graph prediction
model very challenging. In this paper, we propose a novel scene graph
generation algorithm with external knowledge and image reconstruction loss to
overcome these dataset issues. In particular, we extract commonsense knowledge
from the external knowledge base to refine object and phrase features for
improving generalizability in scene graph generation. To address the bias of
noisy object annotations, we introduce an auxiliary image reconstruction path
to regularize the scene graph generation network. Extensive experiments show
that our framework can generate better scene graphs, achieving the
state-of-the-art performance on two benchmark datasets: Visual Relationship
Detection and Visual Genome datasets.Comment: 10 pages, 5 figures, Accepted in CVPR 201
Visually Grounded Commonsense Knowledge Acquisition
Large-scale commonsense knowledge bases empower a broad range of AI
applications, where the automatic extraction of commonsense knowledge (CKE) is
a fundamental and challenging problem. CKE from text is known for suffering
from the inherent sparsity and reporting bias of commonsense in text. Visual
perception, on the other hand, contains rich commonsense knowledge about
real-world entities, e.g., (person, can_hold, bottle), which can serve as
promising sources for acquiring grounded commonsense knowledge. In this work,
we present CLEVER, which formulates CKE as a distantly supervised
multi-instance learning problem, where models learn to summarize commonsense
relations from a bag of images about an entity pair without any human
annotation on image instances. To address the problem, CLEVER leverages
vision-language pre-training models for deep understanding of each image in the
bag, and selects informative instances from the bag to summarize commonsense
entity relations via a novel contrastive attention mechanism. Comprehensive
experimental results in held-out and human evaluation show that CLEVER can
extract commonsense knowledge in promising quality, outperforming pre-trained
language model-based methods by 3.9 AUC and 6.4 mAUC points. The predicted
commonsense scores show strong correlation with human judgment with a 0.78
Spearman coefficient. Moreover, the extracted commonsense can also be grounded
into images with reasonable interpretability. The data and codes can be
obtained at https://github.com/thunlp/CLEVER.Comment: Accepted by AAAI 202