RetroGAN: A Cyclic Post-Specialization System for Improving
Out-of-Knowledge and Rare Word Representations
- URL: http://arxiv.org/abs/2108.12941v1
- Date: Mon, 30 Aug 2021 00:34:23 GMT
- Title: RetroGAN: A Cyclic Post-Specialization System for Improving
Out-of-Knowledge and Rare Word Representations
- Authors: Pedro Colon-Hernandez, Yida Xin, Henry Lieberman, Catherine Havasi,
Cynthia Breazeal, and Peter Chin
- Abstract summary: RetroGAN learns a one-to-one mapping between concepts and their retrofitted counterparts.
It applies that mapping to handle concepts that do not appear in the original Knowledge Base.
We test our system on three word-similarity benchmarks and a downstream sentence simplification task.
- Score: 9.260444813514948
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Retrofitting is a technique used to move word vectors closer together or
further apart in their space to reflect their relationships in a Knowledge Base
(KB). However, retrofitting only works on concepts that are present in that KB.
RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a
one-to-one mapping between concepts and their retrofitted counterparts. It
applies that mapping (post-specializes) to handle concepts that do not appear
in the original KB in a manner similar to how some natural language systems
handle out-of-vocabulary entries. We test our system on three word-similarity
benchmarks and a downstream sentence simplification task and achieve the state
of the art (CARD-660). Altogether, our results demonstrate our system's
effectiveness for out-of-knowledge and rare word generalization.
Related papers
- Efficient Induction of Language Models Via Probabilistic Concept
Formation [13.632454840363916]
We present a novel approach to the acquisition of language models from corpora.
The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts.
We explore three new extensions to Cobweb -- the Word, Leaf, and Path variants.
arXiv Detail & Related papers (2022-12-22T18:16:58Z) - DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for
Open-world Detection [118.36746273425354]
This paper presents a paralleled visual-concept pre-training method for open-world detection by resorting to knowledge enrichment from a designed concept dictionary.
By enriching the concepts with their descriptions, we explicitly build the relationships among various concepts to facilitate the open-domain learning.
The proposed framework demonstrates strong zero-shot detection performances, e.g., on the LVIS dataset, our DetCLIP-T outperforms GLIP-T by 9.9% mAP and obtains a 13.5% improvement on rare categories.
arXiv Detail & Related papers (2022-09-20T02:01:01Z) - Short-Term Word-Learning in a Dynamically Changing Environment [63.025297637716534]
We show how to supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly.
We demonstrate significant improvements in the detection rate of new words with only a minor increase in false alarms.
arXiv Detail & Related papers (2022-03-29T10:05:39Z) - Toward a Visual Concept Vocabulary for GAN Latent Space [74.12447538049537]
This paper introduces a new method for building open-ended vocabularies of primitive visual concepts represented in a GAN's latent space.
Our approach is built from three components: automatic identification of perceptually salient directions based on their layer selectivity; human annotation of these directions with free-form, compositional natural language descriptions.
Experiments show that concepts learned with our approach are reliable and composable -- generalizing across classes, contexts, and observers.
arXiv Detail & Related papers (2021-10-08T17:58:19Z) - Instant One-Shot Word-Learning for Context-Specific Neural
Sequence-to-Sequence Speech Recognition [62.997667081978825]
We present an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly.
In this paper we demonstrate that through this mechanism our system is able to recognize more than 85% of newly added words that it previously failed to recognize.
arXiv Detail & Related papers (2021-07-05T21:08:34Z) - Keyphrase Extraction with Dynamic Graph Convolutional Networks and
Diversified Inference [50.768682650658384]
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
Recent Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks.
In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously.
arXiv Detail & Related papers (2020-10-24T08:11:23Z) - WSRNet: Joint Spotting and Recognition of Handwritten Words [38.212002652391]
The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq branch that is further augmented with an Autoencoding module.
We show how to further process these representations with binarization and a retraining scheme to provide compact and highly efficient descriptors.
arXiv Detail & Related papers (2020-08-17T06:22:05Z) - Enhancing Word Embeddings with Knowledge Extracted from Lexical
Resources [3.7814216736076434]
We use traditional word embeddings and apply specialization methods to better capture semantic relations between words.
In our approach, we leverage external knowledge from rich lexical resources such as BabelNet.
arXiv Detail & Related papers (2020-05-20T13:45:49Z) - Common-Knowledge Concept Recognition for SEVA [15.124939896007472]
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA)
The problem is formulated as a token classification task similar to named entity extraction.
We construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts.
arXiv Detail & Related papers (2020-03-26T00:30:36Z) - Techniques for Vocabulary Expansion in Hybrid Speech Recognition Systems [54.49880724137688]
The problem of out of vocabulary words (OOV) is typical for any speech recognition system.
One of the popular approach to cover OOVs is to use subword units rather then words.
In this paper we explore different existing methods of this solution on both graph construction and search method levels.
arXiv Detail & Related papers (2020-03-19T21:24:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.