19 research outputs found
Findings of the 2019 Conference on Machine Translation (WMT19)
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation
Transformer Models for Machine Translation and Streaming Automatic Speech Recognition
[ES] El procesamiento del lenguaje natural (NLP) es un conjunto de problemas
computacionales con aplicaciones de máxima relevancia, que junto con otras
tecnologías informáticas se ha beneficiado de la revolución que ha significado
el aprendizaje profundo. Esta tesis se centra en dos problemas fundamentales
para el NLP: la traducción automática (MT) y el reconocimiento automático
del habla o transcripción automática (ASR); así como en una arquitectura
neuronal profunda, el Transformer, que pondremos en práctica para mejorar
las soluciones de MT y ASR en algunas de sus aplicaciones.
El ASR y MT pueden servir para obtener textos multilingües de alta calidad a
un coste razonable para una diversidad de contenidos audiovisuales. Concre-
tamente, esta tesis aborda problemas como el de traducción de noticias o el de
subtitulación automática de televisión. El ASR y MT también se pueden com-
binar entre sí, generando automáticamente subtítulos traducidos, o con otras
soluciones de NLP: resumen de textos para producir resúmenes de discursos, o
síntesis del habla para crear doblajes automáticos. Estas aplicaciones quedan
fuera del alcance de esta tesis pero pueden aprovechar las contribuciones que
contiene, en la meduda que ayudan a mejorar el rendimiento de los sistemas
automáticos de los que dependen.
Esta tesis contiene una aplicación de la arquitectura Transformer al MT tal y
como fue concebida, mediante la que obtenemos resultados de primer nivel en
traducción de lenguas semejantes. En capítulos subsecuentes, esta tesis aborda
la adaptación del Transformer como modelo de lenguaje para sistemas híbri-
dos de ASR en vivo. Posteriormente, describe la aplicación de este tipus de
sistemas al caso de uso de subtitulación de televisión, participando en una com-
petición pública de RTVE donde obtenemos la primera posición con un marge
importante. También demostramos que la mejora se debe principalmenta a la
tecnología desarrollada y no tanto a la parte de los datos.[CA] El processament del llenguage natural (NLP) és un conjunt de problemes com-
putacionals amb aplicacions de màxima rellevància, que juntament amb al-
tres tecnologies informàtiques s'ha beneficiat de la revolució que ha significat
l'impacte de l'aprenentatge profund. Aquesta tesi se centra en dos problemes
fonamentals per al NLP: la traducció automàtica (MT) i el reconeixement
automàtic de la parla o transcripció automàtica (ASR); així com en una ar-
quitectura neuronal profunda, el Transformer, que posarem en pràctica per a
millorar les solucions de MT i ASR en algunes de les seues aplicacions.
l'ASR i MT poden servir per obtindre textos multilingües d'alta qualitat a un
cost raonable per a un gran ventall de continguts audiovisuals. Concretament,
aquesta tesi aborda problemes com el de traducció de notícies o el de subtitu-
lació automàtica de televisió. l'ASR i MT també es poden combinar entre ells,
generant automàticament subtítols traduïts, o amb altres solucions de NLP:
amb resum de textos per produir resums de discursos, o amb síntesi de la parla
per crear doblatges automàtics. Aquestes altres aplicacions es troben fora de
l'abast d'aquesta tesi però poden aprofitar les contribucions que conté, en la
mesura que ajuden a millorar els resultats dels sistemes automàtics dels quals
depenen.
Aquesta tesi conté una aplicació de l'arquitectura Transformer al MT tal com
va ser concebuda, mitjançant la qual obtenim resultats de primer nivell en
traducció de llengües semblants. En capítols subseqüents, aquesta tesi aborda
l'adaptació del Transformer com a model de llenguatge per a sistemes híbrids
d'ASR en viu. Posteriorment, descriu l'aplicació d'aquest tipus de sistemes al
cas d'ús de subtitulació de continguts televisius, participant en una competició
pública de RTVE on obtenim la primera posició amb un marge significant.
També demostrem que la millora es deu principalment a la tecnologia desen-
volupada i no tant a la part de les dades[EN] Natural language processing (NLP) is a set of fundamental computing prob-
lems with immense applicability, as language is the natural communication
vehicle for people. NLP, along with many other computer technologies, has
been revolutionized in recent years by the impact of deep learning. This thesis
is centered around two keystone problems for NLP: machine translation (MT)
and automatic speech recognition (ASR); and a common deep neural architec-
ture, the Transformer, that is leveraged to improve the technical solutions for
some MT and ASR applications.
ASR and MT can be utilized to produce cost-effective, high-quality multilin-
gual texts for a wide array of media. Particular applications pursued in this
thesis are that of news translation or that of automatic live captioning of tele-
vision broadcasts. ASR and MT can also be combined with each other, for
instance generating automatic translated subtitles from audio, or augmented
with other NLP solutions: text summarization to produce a summary of a
speech, or speech synthesis to create an automatic translated dubbing, for in-
stance. These other applications fall out of the scope of this thesis, but can
profit from the contributions that it contains, as they help to improve the
performance of the automatic systems on which they depend.
This thesis contains an application of the Transformer architecture to MT as it
was originally conceived, achieving state-of-the-art results in similar language
translation. In successive chapters, this thesis covers the adaptation of the
Transformer as a language model for streaming hybrid ASR systems. After-
wards, it describes how we applied the developed technology for a specific use
case in television captioning by participating in a competitive challenge and
achieving the first position by a large margin. We also show that the gains
came mostly from the improvement in technology capabilities over two years
including that of the Transformer language model adapted for streaming, and
the data component was minor.Baquero Arnal, P. (2023). Transformer Models for Machine Translation and Streaming Automatic Speech Recognition [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19368
NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval
This paper reports on a study of cross-lingual information retrieval (CLIR)
using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the
biggest contribution of this study is the finding that despite the mT5 model
being fine-tuned only on query-document pairs of the same language it proved to
be viable for CLIR tasks, where query-document pairs are in different
languages, even in the presence of suboptimal first-stage retrieval
performance. The results of the study show outstanding performance across all
tasks and languages, leading to a high number of winning positions. Finally,
this study provides valuable insights into the use of mT5 in CLIR tasks and
highlights its potential as a viable solution. For reproduction refer to
https://github.com/unicamp-dl/NeuCLIR22-mT
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice
Classifiers in natural language processing (NLP) often have a large number of output classes. For example, neural language models (LMs) and machine translation (MT) models both predict tokens from a vocabulary of thousands. The Softmax output layer of these models typically receives as input a dense feature representation, which has much lower dimensionality than the output. In theory, the result is some words may be impossible to be predicted via argmax, irrespective of input features, and empirically, there is evidence this happens in small language models (Demeter et al., 2020). In this paper we ask whether it can happen in practical large language models and translation models. To do so, we develop algorithms to detect such unargmaxable tokens in public models. We find that 13 out of 150 models do indeed have such tokens; however, they are very infrequent and unlikely to impact model quality. We release our algorithms and code to the public
Why don't people use character-level machine translation?
We present a literature and empirical survey that critically assesses the
state of the art in character-level modeling for machine translation (MT).
Despite evidence in the literature that character-level systems are comparable
with subword systems, they are virtually never used in competitive setups in
WMT competitions. We empirically show that even with recent modeling
innovations in character-level natural language processing, character-level MT
systems still struggle to match their subword-based counterparts.
Character-level MT systems show neither better domain robustness, nor better
morphological generalization, despite being often so motivated. However, we are
able to show robustness towards source side noise and that translation quality
does not degrade with increasing beam size at decoding time.Comment: 16 pages, 4 figures; Findings of ACL 2022, camera-read