3 research outputs found
Learning to Read by Spelling: Towards Unsupervised Text Recognition
This work presents a method for visual text recognition without using any
paired supervisory data. We formulate the text recognition task as one of
aligning the conditional distribution of strings predicted from given text
images, with lexically valid strings sampled from target corpora. This enables
fully automated, and unsupervised learning from just line-level text-images,
and unpaired text-string samples, obviating the need for large aligned
datasets. We present detailed analysis for various aspects of the proposed
method, namely - (1) impact of the length of training sequences on convergence,
(2) relation between character frequencies and the order in which they are
learnt, (3) generalisation ability of our recognition network to inputs of
arbitrary lengths, and (4) impact of varying the text corpus on recognition
accuracy. Finally, we demonstrate excellent text recognition accuracy on both
synthetically generated text images, and scanned images of real printed books,
using no labelled training examples
Attacking decipherment problems optimally with low-order n-gram models
We introduce a method for solving substitution ciphers using low-order letter n-gram models. This method enforces global constraints using integer programming, and it guarantees that no decipherment key is overlooked. We carry out extensive empirical experiments showing how decipherment accuracy varies as a function of cipher length and n-gram order. We also make an empirical investigation of Shannon’s (1949) theory of uncertainty in decipherment.
Itzulpen automatiko gainbegiratu gabea
192 p.Modern machine translation relies on strong supervision in the form of parallel corpora. Such arequirement greatly departs from the way in which humans acquire language, and poses a major practicalproblem for low-resource language pairs. In this thesis, we develop a new paradigm that removes thedependency on parallel data altogether, relying on nothing but monolingual corpora to train unsupervisedmachine translation systems. For that purpose, our approach first aligns separately trained wordrepresentations in different languages based on their structural similarity, and uses them to initializeeither a neural or a statistical machine translation system, which is further trained through iterative backtranslation.While previous attempts at learning machine translation systems from monolingual corporahad strong limitations, our work¿along with other contemporaneous developments¿is the first to reportpositive results in standard, large-scale settings, establishing the foundations of unsupervised machinetranslation and opening exciting opportunities for future research