A Large-Scale Comparison of Historical Text Normalization Systems

There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder--decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201

Few-Shot and Zero-Shot Learning for Historical Text Normalization

Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different multi-task learning architectures. This paper evaluates 63~multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages, using autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary tasks. We observe consistent, significant improvements across languages when training data for the target task is limited, but minimal or no improvements when training data is abundant. We also show that zero-shot learning outperforms the simple, but relatively strong, identity baseline.Comment: Accepted at DeepLo-201

Investigating cross-language speech retrieval for a spontaneous conversational speech collection

Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language translation. The CLEF 2005 Cross-Language Speech Retrieval (CL-SR) task provides a standard test collection to investigate these challenges. We show that we can improve retrieval performance: by careful selection of the term weighting scheme; by decomposing automated transcripts into phonetic substrings to help ameliorate transcription errors; and by combining automatic transcriptions with manually-assigned metadata. We further show that topic translation with online machine translation resources yields effective CL-SR

Miracle’s 2005 Approach to Cross-lingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Bilingual and Multilingual Information Retrieval. In the multilingual track, we have concentrated our work on the merging process of the results of monolingual runs to get the multilingual overall result, relying on available translations. In the bilingual and multilingual tracks, we have used available translation resources, and in some cases we have using a combining approach

Basque-to-Spanish and Spanish-to-Basque machine translation for the health domain

[EU]Master Amaierako Lan honek medikuntza domeinuko euskara eta gaztelera arteko itzulpen automatiko sistema bat garatzeko helburuarekin emandako lehenengo urratsak aurkezten ditu. Corpus elebidun nahikoaren faltan, hainbat esperimentu burutu dira Itzulpen Automatiko Neuronalean erabiltzen diren parametroak domeinuz kanpoko corpusean aztertzeko; medikuntza domeinuan izandako jokaera ebaluatzeko ordea, eskuz itzulitako corpusa erabili da medikuntza domeinuko corpusen presentzia handituz entrenatutako sistema desberdinak probatzeko. Lortutako emaitzek deskribatutako helbururako bidean lehenengo aurrerapausoa suposatzen dute.[EN]This project presents the initial steps towards the objective of developing a Machine Translation system for the health domain between Basque and Spanish. In the absence of a big enough bilingual corpus, several experiments have been carried out to test different Neural Machine Translation parameters on an out-of-domain corpus; while performance on the health domain has been evaluated with a manually translated corpus in different systems trained with increasing presence of health domain corpora. The results obtained represent a first step forward to the described objective