14,682 research outputs found
Unsupervised Semantic Parsing of Video Collections
Human communication typically has an underlying structure. This is reflected
in the fact that in many user generated videos, a starting point, ending, and
certain objective steps between these two can be identified. In this paper, we
propose a method for parsing a video into such semantic steps in an
unsupervised way. The proposed method is capable of providing a semantic
"storyline" of the video composed of its objective steps. We accomplish this
using both visual and language cues in a joint generative model. The proposed
method can also provide a textual description for each of the identified
semantic steps and video segments. We evaluate this method on a large number of
complex YouTube videos and show results of unprecedented quality for this
intricate and impactful problem
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
Semantic parsing is challenging due to the structure gap and the semantic gap
between utterances and logical forms. In this paper, we propose an unsupervised
semantic parsing method - Synchronous Semantic Decoding (SSD), which can
simultaneously resolve the semantic gap and the structure gap by jointly
leveraging paraphrasing and grammar constrained decoding. Specifically, we
reformulate semantic parsing as a constrained paraphrasing problem: given an
utterance, our model synchronously generates its canonical utterance and
meaning representation. During synchronous decoding: the utterance paraphrasing
is constrained by the structure of the logical form, therefore the canonical
utterance can be paraphrased controlledly; the semantic decoding is guided by
the semantics of the canonical utterance, therefore its logical form can be
generated unsupervisedly. Experimental results show that SSD is a promising
approach and can achieve competitive unsupervised semantic parsing performance
on multiple datasets.Comment: Accepted by ACL 202
Unsupervised Person Image Generation with Semantic Parsing Transformation
In this paper, we address unsupervised pose-guided person image generation,
which is known challenging due to non-rigid deformation. Unlike previous
methods learning a rock-hard direct mapping between human bodies, we propose a
new pathway to decompose the hard mapping into two more accessible subtasks,
namely, semantic parsing transformation and appearance generation. Firstly, a
semantic generative network is proposed to transform between semantic parsing
maps, in order to simplify the non-rigid deformation learning. Secondly, an
appearance generative network learns to synthesize semantic-aware textures.
Thirdly, we demonstrate that training our framework in an end-to-end manner
further refines the semantic maps and final results accordingly. Our method is
generalizable to other semantic-aware person image generation tasks, eg,
clothing texture transfer and controlled image manipulation. Experimental
results demonstrate the superiority of our method on DeepFashion and
Market-1501 datasets, especially in keeping the clothing attributes and better
body shapes.Comment: Accepted to CVPR 2019 (Oral). Our project is available at
https://github.com/SijieSong/person_generation_sp
Rule-Based Semantic Tagging. An Application Undergoing Dictionary Glosses
The project presented in this article aims to formalize criteria and
procedures in order to extract semantic information from parsed dictionary
glosses. The actual purpose of the project is the generation of a semantic
network (nearly an ontology) issued from a monolingual Italian dictionary,
through unsupervised procedures. Since the project involves rule-based Parsing,
Semantic Tagging and Word Sense Disambiguation techniques, its outcomes may
find an interest also beyond this immediate intent. The cooperation of both
syntactic and semantic features in meaning construction are investigated, and
procedures which allows a translation of syntactic dependencies in semantic
relations are discussed. The procedures that rise from this project can be
applied also to other text types than dictionary glosses, as they convert the
output of a parsing process into a semantic representation. In addition some
mechanism are sketched that may lead to a kind of procedural semantics, through
which multiple paraphrases of an given expression can be generated. Which means
that these techniques may find an application also in 'query expansion'
strategies, interesting Information Retrieval, Search Engines and Question
Answering Systems.Comment: 12 pages, 2 Table
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
One daunting problem for semantic parsing is the scarcity of annotation.
Aiming to reduce nontrivial human labor, we propose a two-stage semantic
parsing framework, where the first stage utilizes an unsupervised paraphrase
model to convert an unlabeled natural language utterance into the canonical
utterance. The downstream naive semantic parser accepts the intermediate output
and returns the target logical form. Furthermore, the entire training process
is split into two phases: pre-training and cycle learning. Three tailored
self-supervised tasks are introduced throughout training to activate the
unsupervised paraphrase model. Experimental results on benchmarks Overnight and
GeoGranno demonstrate that our framework is effective and compatible with
supervised training.Comment: accepted by ACL 2020 Long, 12 pages, 5 figure
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Semantic parsing is the task of transducing natural language (NL) utterances
into formal meaning representations (MRs), commonly represented as tree
structures. Annotating NL utterances with their corresponding MRs is expensive
and time-consuming, and thus the limited availability of labeled data often
becomes the bottleneck of data-driven, supervised models. We introduce
StructVAE, a variational auto-encoding model for semisupervised semantic
parsing, which learns both from limited amounts of parallel data, and
readily-available unlabeled NL utterances. StructVAE models latent MRs not
observed in the unlabeled data as tree-structured latent variables. Experiments
on semantic parsing on the ATIS domain and Python code generation show that
with extra unlabeled data, StructVAE outperforms strong supervised models.Comment: ACL 201
Unsupervised Semantic Action Discovery from Video Collections
Human communication takes many forms, including speech, text and
instructional videos. It typically has an underlying structure, with a starting
point, ending, and certain objective steps between them. In this paper, we
consider instructional videos where there are tens of millions of them on the
Internet.
We propose a method for parsing a video into such semantic steps in an
unsupervised way. Our method is capable of providing a semantic "storyline" of
the video composed of its objective steps. We accomplish this using both visual
and language cues in a joint generative model. Our method can also provide a
textual description for each of the identified semantic steps and video
segments. We evaluate our method on a large number of complex YouTube videos
and show that our method discovers semantically correct instructions for a
variety of tasks.Comment: First version of this paper arXiv:1506.08438 appeared in ICCV 2015.
This extended version has more details on the learning algorithm and
hierarchical clustering with full derivation, additional analysis on the
robustness to the subtitle noise, and a novel application on robotic
Joint learning of ontology and semantic parser from text
Semantic parsing methods are used for capturing and representing semantic
meaning of text. Meaning representation capturing all the concepts in the text
may not always be available or may not be sufficiently complete. Ontologies
provide a structured and reasoning-capable way to model the content of a
collection of texts. In this work, we present a novel approach to joint
learning of ontology and semantic parser from text. The method is based on
semi-automatic induction of a context-free grammar from semantically annotated
text. The grammar parses the text into semantic trees. Both, the grammar and
the semantic trees are used to learn the ontology on several levels -- classes,
instances, taxonomic and non-taxonomic relations. The approach was evaluated on
the first sentences of Wikipedia pages describing people
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
We introduce deep inside-outside recursive autoencoders (DIORA), a
fully-unsupervised method for discovering syntax that simultaneously learns
representations for constituents within the induced tree. Our approach predicts
each word in an input sentence conditioned on the rest of the sentence and uses
inside-outside dynamic programming to consider all possible binary trees over
the sentence. At test time the CKY algorithm extracts the highest scoring
parse. DIORA achieves a new state-of-the-art F1 in unsupervised binary
constituency parsing (unlabeled) in two benchmark datasets, WSJ and MultiNLI.Comment: 14 pages, 8 figures, 8 tables. NAACL 201
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
- …