246 research outputs found
Crystal structure of 3-(2-dimethylaminoethyl)-2,3-dihydro-2-thioxoquinazolin-4(1H)-one, C12H15N3OS
Abstract
C12H15N3OS, monoclinic, P21/c (no. 14), a = 7.9840(18) Å, b = 11.331(3) Å, c = 14.428(3) Å, β = 105.702(4)°, V = 1256.5(5) Å3, Z = 4, R
gt(F) = 0.0639, wR
ref(F
2) = 0.1293, T = 296K
An Empirical Study on the Language Modal in Visual Question Answering
Generalization beyond in-domain experience to out-of-distribution data is of
paramount significance in the AI domain. Of late, state-of-the-art Visual
Question Answering (VQA) models have shown impressive performance on in-domain
data, partially due to the language priors bias which, however, hinders the
generalization ability in practice. This paper attempts to provide new insights
into the influence of language modality on VQA performance from an empirical
study perspective. To achieve this, we conducted a series of experiments on six
models. The results of these experiments revealed that, 1) apart from prior
bias caused by question types, there is a notable influence of postfix-related
bias in inducing biases, and 2) training VQA models with word-sequence-related
variant questions demonstrated improved performance on the out-of-distribution
benchmark, and the LXMERT even achieved a 10-point gain without adopting any
debiasing methods. We delved into the underlying reasons behind these
experimental results and put forward some simple proposals to reduce the
models' dependency on language priors. The experimental results demonstrated
the effectiveness of our proposed method in improving performance on the
out-of-distribution benchmark, VQA-CPv2. We hope this study can inspire novel
insights for future research on designing bias-reduction approaches.Comment: Accepted by IJCAI202
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently
- …