44,984 research outputs found
A Logical Approach To Deciding Semantic Subtyping
International audienceWe consider a type algebra equipped with recursive, product, function, intersection, union, and complement types together with type variables and implicit universal quantification over them. We consider the subtyping relation recently defined by Castagna and Xu over such type expressions and show how this relation can be decided in EXPTIME, answering an open question. The novelty, originality and strength of our solution reside in introducing a logical modeling for the semantic subtyping framework. We model semantic subtyping in a tree logic and use a satisfiability-testing algorithm in order to decide subtyping. We report on practical experiments made with a full implementation of the system. This provides a powerful polymorphic type system aiming at maintaining full static type-safety of functional programs that manipulate trees, even with higher-order functions, which is particularly useful in the context of XML
A Context-aware Attention Network for Interactive Question Answering
Neural network based sequence-to-sequence models in an encoder-decoder
framework have been successfully applied to solve Question Answering (QA)
problems, predicting answers from statements and questions. However, almost all
previous models have failed to consider detailed context information and
unknown states under which systems do not have enough information to answer
given questions. These scenarios with incomplete or ambiguous information are
very common in the setting of Interactive Question Answering (IQA). To address
this challenge, we develop a novel model, employing context-dependent
word-level attention for more accurate statement representations and
question-guided sentence-level attention for better context modeling. We also
generate unique IQA datasets to test our model, which will be made publicly
available. Employing these attention mechanisms, our model accurately
understands when it can output an answer or when it requires generating a
supplementary question for additional input depending on different contexts.
When available, user's feedback is encoded and directly applied to update
sentence-level attention to infer an answer. Extensive experiments on QA and
IQA datasets quantitatively demonstrate the effectiveness of our model with
significant improvement over state-of-the-art conventional QA models.Comment: 9 page
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
Cross-modal alignment is essential for vision-language pre-training (VLP)
models to learn the correct corresponding information across different
modalities. For this purpose, inspired by the success of masked language
modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling
tasks have been proposed for VLP to further promote cross-modal interactions.
The core idea of previous masked modeling tasks is to focus on reconstructing
the masked tokens based on visible context for learning local-to-local
alignment. However, most of them pay little attention to the global semantic
features generated for the masked data, resulting in the limited cross-modal
alignment ability of global representations. Therefore, in this paper, we
propose a novel Semantic Completion Learning (SCL) task, complementary to
existing masked modeling tasks, to facilitate global-to-local alignment.
Specifically, the SCL task complements the missing semantics of masked data by
capturing the corresponding information from the other modality, promoting
learning more representative global features which have a great impact on the
performance of downstream tasks. Moreover, we present a flexible vision
encoder, which enables our model to perform image-text and video-text
multimodal tasks simultaneously. Experimental results show that our proposed
method obtains state-of-the-art performance on various vision-language
benchmarks, such as visual question answering, image-text retrieval, and
video-text retrieval
- …