1,035 research outputs found
Symbolic inductive bias for visually grounded learning of spoken language
A widespread approach to processing spoken language is to first automatically
transcribe it into text. An alternative is to use an end-to-end approach:
recent works have proposed to learn semantic embeddings of spoken language from
images with spoken captions, without an intermediate transcription step. We
propose to use multitask learning to exploit existing transcribed speech within
the end-to-end setting. We describe a three-task architecture which combines
the objectives of matching spoken captions with corresponding images, speech
with text, and text with images. We show that the addition of the speech/text
task leads to substantial performance improvements on image retrieval when
compared to training the speech/image task in isolation. We conjecture that
this is due to a strong inductive bias transcribed speech provides to the
model, and offer supporting evidence for this.Comment: ACL 201
Efficient Methods for the Design and Training of Neural Networks
The field of artificial intelligence has seen significant advancements with the development of neural networks, which have numerous applications in computer vision, natural language processing, and speech processing. Despite these advancements, designing and training these networks still pose numerous challenges. This thesis aims to address two critical aspects of neural network development, design and training, within the context of computer vision tasks.
The thesis focuses on three main challenges in the development of neural networks. The first challenge is finding an efficient way to perform architecture search in an extremely large or even unlimited search space. To address this challenge, the thesis proposes a Neural Search-space Evolution (NSE) scheme that enables efficient and effective architecture search in large-scale search spaces. The second challenge is to improve the efficiency of self-supervised learning for model pretraining. To address this challenge, the thesis proposes a combinatorial patches approach that significantly improves the efficiency of self-supervised learning. The third challenge is to develop an efficient and versatile multitask model that can leverage the benefits of large-scale multitask training. To address this challenge, the thesis proposes a Unified model for Human-Centric Perceptions (UniHCP) as a simple and scalable solution for a human-centric perception system that unifies multiple human-centric tasks into a neat, efficient, and scalable model.
The results of this thesis demonstrate the effectiveness of the proposed methods in improving the practicality and performance of neural network design and training. The NSE scheme, combinatorial patches approach, and UniHCP have been tested on a broad range of datasets, tasks, and settings, yielding impressive results. These findings affirm the efficacy of the proposed methods in enhancing the efficiency of the design and training process of neural networks
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention
This paper introduces a novel aligner for Abstract Meaning Representation
(AMR) graphs that can scale cross-lingually, and is thus capable of aligning
units and spans in sentences of different languages. Our approach leverages
modern Transformer-based parsers, which inherently encode alignment information
in their cross-attention weights, allowing us to extract this information
during parsing. This eliminates the need for English-specific rules or the
Expectation Maximization (EM) algorithm that have been used in previous
approaches. In addition, we propose a guided supervised method using alignment
to further enhance the performance of our aligner. We achieve state-of-the-art
results in the benchmarks for AMR alignment and demonstrate our aligner's
ability to obtain them across multiple languages. Our code will be available at
\href{https://www.github.com/Babelscape/AMR-alignment}{github.com/Babelscape/AMR-alignment}.Comment: ACL 2023. Please cite authors correctly using both lastnames
("Mart\'inez Lorenzo", "Huguet Cabot"
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