150 research outputs found
One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language
processing applications. To further expand KGs' coverage, previous studies on
knowledge graph completion usually require a large number of training instances
for each relation. However, we observe that long-tail relations are actually
more common in KGs and those newly added relations often do not have many known
triples for training. In this work, we aim at predicting new facts under a
challenging setting where only one training instance is available. We propose a
one-shot relational learning framework, which utilizes the knowledge extracted
by embedding models and learns a matching metric by considering both the
learned embeddings and one-hop graph structures. Empirically, our model yields
considerable performance improvements over existing embedding models, and also
eliminates the need of re-training the embedding models when dealing with newly
added relations.Comment: EMNLP 201
Do Multi-hop Readers Dream of Reasoning Chains?
General Question Answering (QA) systems over texts require the multi-hop
reasoning capability, i.e. the ability to reason with information collected
from multiple passages to derive the answer. In this paper we conduct a
systematic analysis to assess such an ability of various existing models
proposed for multi-hop QA tasks. Specifically, our analysis investigates that
whether providing the full reasoning chain of multiple passages, instead of
just one final passage where the answer appears, could improve the performance
of the existing QA models. Surprisingly, when using the additional evidence
passages, the improvements of all the existing multi-hop reading approaches are
rather limited, with the highest error reduction of 5.8% on F1 (corresponding
to 1.3% absolute improvement) from the BERT model.
To better understand whether the reasoning chains could indeed help find
correct answers, we further develop a co-matching-based method that leads to
13.1% error reduction with passage chains when applied to two of our base
readers (including BERT). Our results demonstrate the existence of the
potential improvement using explicit multi-hop reasoning and the necessity to
develop models with better reasoning abilities.Comment: Accepted by MRQA Workshop 201
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal
Recent deep learning methods have achieved promising results in image shadow
removal. However, their restored images still suffer from unsatisfactory
boundary artifacts, due to the lack of degradation prior embedding and the
deficiency in modeling capacity. Our work addresses these issues by proposing a
unified diffusion framework that integrates both the image and degradation
priors for highly effective shadow removal. In detail, we first propose a
shadow degradation model, which inspires us to build a novel unrolling
diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's
capacity in shadow removal via progressively refining the desired output with
both degradation prior and diffusive generative prior, which by nature can
serve as a new strong baseline for image restoration. Furthermore,
ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary
task of the diffusion generator, which leads to more accurate and robust
shadow-free image generation. We conduct extensive experiments on three popular
public datasets, including ISTD, ISTD+, and SRD, to validate our method's
effectiveness. Compared to the state-of-the-art methods, our model achieves a
significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB
over SRD dataset
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