Continuous spaces in statistical machine Translation

[EN] Classically, statistical machine translation relied on representations of words in a discrete space. Words and phrases were atomically represented as indices in a vector. In the last years, techniques for representing words and phrases in a continuous space have arisen. In this scenario, a word is represented in the continuous space as a real-valued, dense and low-dimensional vector. Statistical models can profit from this richer representation, since it is able to naturally take into account concepts such as semantic or syntactic relationships between words and phrases. This approach is encouraging, but it also entails new challenges. In this work, a language model which relies on continuous representations of words is developed. Such model makes use of a bidirectional recurrent neural network, which is able to take into account both the past and the future context of words. Since the model is costly to train, the training dataset is reduced by using bilingual sentence selection techniques. Two selection methods are used and compared. The language model is then used to rerank translation hypotheses. Results show improvements on the translation quality. Moreover, a new approach for machine translation has been recently proposed: The so-called neural machine translation. It consists in the sole use of a large neural network for carrying out the translation process. In this work, such novel model is compared to the existing phrase-based approaches of statistical machine translation. Finally, the neural translation models are combined with diverse machine translation systems, in order to provide a consensus translation, which aim to improve the translation given by each single system.[ES] Los sistemas clásicos de traducción automática estadística están basados en representaciones de palabras en un espacio discreto. Palabras y segmentos se representan como índices en un vector. Durante los últimos años han surgido técnicas para realizar la representación de palabras y segmentos en un espacio continuo. En este escenario, una palabra se representa en el espacio continuo como un vector de valores reales, denso y de baja dimensión. Los modelos estadísticos pueden aprovecharse de esta representación más rica, puesto que incluye de forma natural conceptos semánticos o relaciones sintácticas entre palabras y segmentos. Esta aproximación es prometedora, pero también conlleva nuevos retos. En este trabajo se desarrolla un modelo de lenguaje basado en representaciones continuas de palabras. Dicho modelo emplea una red neuronal recurrente bidireccional, la cual es capaz de considerar tanto el contexto pasado como el contexto futuro de las palabras. Debido a que este modelo es costoso de entrenar, se emplea un conjunto de entrenamiento reducido mediante técnicas de selección de frases bilingües. Se emplean y comparan dos métodos de selección. Una vez entrenado, el modelo se emplea para reordenar hipótesis de traducción. Los resultados muestran mejoras en la calidad de la traducción. Por otro lado, recientemente se propuso una nueva aproximación a la traducción automática: la llamada traducción automática neuronal. Consiste en el uso exclusivo de una gran red neuronal para llevar a cabo el proceso de traducción. En este trabajo, este nuevo modelo se compara al paradigma actual de traducción basada en segmentos. Finalmente, los modelos de traducción neuronales son combinados con otros sistemas de traducción automática, para ofrecer una traducción consensuada, que busca mejorar las traducciones individuales que cada sistema ofrecePeris Abril, Á. (2015). Continuous spaces in statistical machine Translation. http://hdl.handle.net/10251/68448Archivo delegad

Translation rescoring through recurrent neural network language models

This work is framed into the Statistical Machine Translation field, more specifically into the language modeling challenge. In this area, have classically predominated the n-gram approach, but, in the latest years, different approaches have arisen in order to tackle this problem. One of this approaches is the use of artificial recurrent neural networks, which are supposed to outperform the n-gram language models. The aim of this work is to test empirically these new language models. For doing that, the translation rescoring of three tasks of different complexity has been performed: in first place, the translation problem has been solved by means of the classic n-gram language models. Next, the different translation hypotheses have been rescored through the language models based on neural networks and the results have been compared. This comparison shows that the translations produced by the neural network language models have a better quality in all the experiments: the perplexity of the language models has been lowered and the BLEU score of the translations outputted by the system has yielded higher values with the neural network language model than with the classical n-gram language model.Peris Abril, Á. (2014). Translation rescoring through recurrent neural network language models. http://hdl.handle.net/10251/39898.Archivo delegad

Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning

[ES] El problema conocido como de secuencia a secuencia consiste en transformar una secuencia de entrada en una secuencia de salida. Bajo esta perspectiva se puede atacar una amplia cantidad de problemas, entre los cuales destacan la traducción automática o la descripción automática de objetos multimedia. La aplicación de redes neuronales profundas ha revolucionado esta disciplina, y se han logrado avances notables. Pero los sistemas automáticos todavía producen predicciones que distan mucho de ser perfectas. Para obtener predicciones de gran calidad, los sistemas automáticos se utilizan bajo la supervisión de un humano, quien corrige los errores. Esta tesis se centra principalmente en el problema de la traducción del lenguaje natural, usando modelos enteramente neuronales. Nuestro objetivo es desarrollar sistemas de traducción neuronal más eficientes. asentándonos sobre dos pilares fundamentales: cómo utilizar el sistema de una forma más eficiente y cómo aprovechar datos generados durante la fase de explotación del mismo. En el primer caso, aplicamos el marco teórico conocido como predicción interactiva a la traducción automática neuronal. Este proceso consiste en integrar usuario y sistema en un proceso de corrección cooperativo, con el objetivo de reducir el esfuerzo humano empleado en obtener traducciones de alta calidad. Desarrollamos distintos protocolos de interacción para dicha tecnología, aplicando interacción basada en prefijos y en segmentos, implementados modificando el proceso de búsqueda del sistema. Además, ideamos mecanismos para obtener una interacción con el sistema más precisa, manteniendo la velocidad de generación del mismo. Llevamos a cabo una extensa experimentación, que muestra el potencial de estas técnicas: superamos el estado del arte anterior por un gran margen y observamos que nuestros sistemas reaccionan mejor a las interacciones humanas. A continuación, estudiamos cómo mejorar un sistema neuronal mediante los datos generados como subproducto de este proceso de corrección. Para ello, nos basamos en dos paradigmas del aprendizaje automático: el aprendizaje muestra a muestra y el aprendizaje activo. En el primer caso, el sistema se actualiza inmediatamente después de que el usuario corrige una frase, aprendiendo de una manera continua a partir de correcciones, evitando cometer errores previos y especializándose en un usuario o dominio concretos. Evaluamos estos sistemas en una gran cantidad de situaciones y dominios diferentes, que demuestran el potencial que tienen los sistemas adaptativos. También llevamos a cabo una evaluación humana, con traductores profesionales. Éstos quedaron muy satisfechos con el sistema adaptativo. Además, fueron más eficientes cuando lo usaron, comparados con un sistema estático. El segundo paradigma lo aplicamos en un escenario en el que se deban traducir grandes cantidades de frases, siendo inviable la supervisión de todas. El sistema selecciona aquellas muestras que vale la pena supervisar, traduciendo el resto automáticamente. Aplicando este protocolo, redujimos de aproximadamente un cuarto el esfuerzo humano necesario para llegar a cierta calidad de traducción. Finalmente, atacamos el complejo problema de la descripción de objetos multimedia. Este problema consiste en describir en lenguaje natural un objeto visual, una imagen o un vídeo. Comenzamos con la tarea de descripción de vídeos pertenecientes a un dominio general. A continuación, nos movemos a un caso más específico: la descripción de eventos a partir de imágenes egocéntricas, capturadas a lo largo de un día. Buscamos extraer relaciones entre eventos para generar descripciones más informadas, desarrollando un sistema capaz de analizar un mayor contexto. El modelo con contexto extendido genera descripciones de mayor calidad que un modelo básico. Por último, aplicamos la predicción interactiva a estas tareas multimedia, disminuyendo el esfuerzo necesa[CA] El problema conegut com a de seqüència a seqüència consisteix en transformar una seqüència d'entrada en una seqüència d'eixida. Seguint aquesta perspectiva, es pot atacar una àmplia quantitat de problemes, entre els quals destaquen la traducció automàtica, el reconeixement automàtic de la parla o la descripció automàtica d'objectes multimèdia. L'aplicació de xarxes neuronals profundes ha revolucionat aquesta disciplina, i s'han aconseguit progressos notables. Però els sistemes automàtics encara produeixen prediccions que disten molt de ser perfectes. Per a obtindre prediccions de gran qualitat, els sistemes automàtics són utilitzats amb la supervisió d'un humà, qui corregeix els errors. Aquesta tesi se centra principalment en el problema de la traducció de llenguatge natural, el qual s'ataca emprant models enterament neuronals. El nostre objectiu principal és desenvolupar sistemes més eficients. Per a aquesta tasca, les nostres contribucions s'assenten sobre dos pilars fonamentals: com utilitzar el sistema d'una manera més eficient i com aprofitar dades generades durant la fase d'explotació d'aquest. En el primer cas, apliquem el marc teòric conegut com a predicció interactiva a la traducció automàtica neuronal. Aquest procés consisteix en integrar usuari i sistema en un procés de correcció cooperatiu, amb l'objectiu de reduir l'esforç humà emprat per obtindre traduccions d'alta qualitat. Desenvolupem diferents protocols d'interacció per a aquesta tecnologia, aplicant interacció basada en prefixos i en segments, implementats modificant el procés de cerca del sistema. A més a més, busquem mecanismes per a obtindre una interacció amb el sistema més precisa, mantenint la velocitat de generació. Duem a terme una extensa experimentació, que mostra el potencial d'aquestes tècniques: superem l'estat de l'art anterior per un gran marge i observem que els nostres sistemes reaccionen millor a les interacciones humanes. A continuació, estudiem com millorar un sistema neuronal mitjançant les dades generades com a subproducte d'aquest procés de correcció. Per a això, ens basem en dos paradigmes de l'aprenentatge automàtic: l'aprenentatge mostra a mostra i l'aprenentatge actiu. En el primer cas, el sistema s'actualitza immediatament després que l'usuari corregeix una frase. Per tant, el sistema aprén d'una manera contínua a partir de correccions, evitant cometre errors previs i especialitzant-se en un usuari o domini concrets. Avaluem aquests sistemes en una gran quantitat de situacions i per a dominis diferents, que demostren el potencial que tenen els sistemes adaptatius. També duem a terme una avaluació amb traductors professionals, qui varen quedar molt satisfets amb el sistema adaptatiu. A més, van ser més eficients quan ho van usar, si ho comparem amb el sistema estàtic. Pel que fa al segon paradigma, l'apliquem per a l'escenari en el qual han de traduir-se grans quantitats de frases, i la supervisió de totes elles és inviable. En aquest cas, el sistema selecciona les mostres que paga la pena supervisar, traduint la resta automàticament. Aplicant aquest protocol, reduírem en aproximadament un quart l'esforç necessari per a arribar a certa qualitat de traducció. Finalment, ataquem el complex problema de la descripció d'objectes multimèdia. Aquest problema consisteix en descriure, en llenguatge natural, un objecte visual, una imatge o un vídeo. Comencem amb la tasca de descripció de vídeos d'un domini general. A continuació, ens movem a un cas més específic: la descripció d''esdeveniments a partir d'imatges egocèntriques, capturades al llarg d'un dia. Busquem extraure relacions entre ells per a generar descripcions més informades, desenvolupant un sistema capaç d'analitzar un major context. El model amb context estés genera descripcions de major qualitat que el model bàsic. Finalment, apliquem la predicció interactiva a aquestes tasques multimèdia, di[EN] The sequence-to-sequence problem consists in transforming an input sequence into an output sequence. A variety of problems can be posed in these terms, including machine translation, speech recognition or multimedia captioning. In the last years, the application of deep neural networks has revolutionized these fields, achieving impressive advances. However and despite the improvements, the output of the automatic systems is still far to be perfect. For achieving high-quality predictions, fully-automatic systems require to be supervised by a human agent, who corrects the errors. This is a common procedure in the translation industry. This thesis is mainly framed into the machine translation problem, tackled using fully neural systems. Our main objective is to develop more efficient neural machine translation systems, that allow for a more productive usage and deployment of the technology. To this end, we base our contributions on two main cornerstones: how to better use of the system and how to better leverage the data generated along its usage. First, we apply the so-called interactive-predictive framework to neural machine translation. This embeds the human agent and the system into a cooperative correction process, that seeks to reduce the human effort spent for obtaining high-quality translations. We develop different interactive protocols for the neural machine translation technology, namely, a prefix-based and a segment-based protocols. They are implemented by modifying the search space of the model. Moreover, we introduce mechanisms for achieving a fine-grained interaction while maintaining the decoding speed of the system. We carried out a wide experimentation that shows the potential of our contributions. The previous state of the art is overcame by a large margin and the current systems are able to react better to the human interactions. Next, we study how to improve a neural system using the data generated as a byproduct of this correction process. To this end, we rely on two main learning paradigms: online and active learning. Under the first one, the system is updated on the fly, as soon as a sentence is corrected. Hence, the system is continuously learning from the corrections, avoiding previous errors and specializing towards a given user or domain. A large experimentation stressed the adaptive systems under different conditions and domains, demonstrating the capabilities of adaptive systems. Moreover, we also carried out a human evaluation of the system, involving professional users. They were very pleased with the adaptive system, and worked more efficiently using it. The second paradigm, active learning, is devised for the translation of huge amounts of data, that are infeasible to being completely supervised. In this scenario, the system selects samples that are worth to be supervised, and leaves the rest automatically translated. Applying this framework, we obtained reductions of approximately a quarter of the effort required for reaching a desired translation quality. The neural approach also obtained large improvements compared with previous translation technologies. Finally, we address another challenging problem: visual captioning. It consists in generating a description in natural language from a visual object, namely an image or a video. We follow the sequence-to-sequence framework, under a a multimodal perspective. We start by tackling the task of generating captions of videos from a general domain. Next, we move on to a more specific case: describing events from egocentric images, acquired along the day. Since these events are consecutive, we aim to extract inter-eventual relationships, for generating more informed captions. The context-aware model improved the generation quality with respect to a regular one. As final point, we apply the intractive-predictive protocol to these multimodal captioning systems, reducing the effort required for correcting the outputs.Section 5.4 describes an user evaluation of an adaptive translation system. This was done in collaboration with Miguel Domingo and the company Pangeanic, with funding from the Spanish Center for Technological and Industrial Development (Centro para el Desarrollo Tecnológico Industrial). [...] Most of Chapter 6 is the result of a collaboration with Marc Bolaños, supervised by Prof. Petia Radeva, from Universitat de Barcelona/CVC. This collaboration was supported by the R-MIPRCV network, under grant TIN2014-54728-REDC.Peris Abril, Á. (2019). Interactivity, Adaptation and Multimodality in Neural Sequence-to-sequence Learning [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134058TESI

NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

[EN] We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.Much of our Keras fork and the Multimodal Keras Wrapper libraries were developed together with Marc Bolaños. We also acknowledge the rest of contributors to these open-source projects. The research leading this work received funding from grants PROMETEO/2018/004 and CoMUN-HaT - TIN2015-70924-C2-1-R. We finally acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris-Abril, Á.; Casacuberta Nolla, F. (2018). NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning. The Prague Bulletin of Mathematical Linguistics. 111:113-124. https://doi.org/10.2478/pralin-2018-0010S11312411

Online Learning for Effort Reduction in Interactive Neural Machine Translation

[EN] Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.The authors wish to thank the anonymous reviewers for their valuable criticisms and suggestions. The research leading to these results has received funding from the Generalitat Valenciana under grant PROMETEOII/2014/030 and from TIN2015-70924-C2-1-R. We also acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris-Abril, Á.; Casacuberta Nolla, F. (2019). Online Learning for Effort Reduction in Interactive Neural Machine Translation. Computer Speech & Language. 58:98-126. https://doi.org/10.1016/j.csl.2019.04.001S981265

Interactive neural machine translation

This is the author’s version of a work that was accepted for publication in Computer Speech & Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech & Language 00 (2016) 1 20. DOI 10.1016/j.csl.2016.12.003.Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural machine translation systems, this technology is still not error-free. The outputs of a machine translation system must be corrected by a human agent in a post-editing phase. Interactive protocols foster a human computer collaboration, in order to increase productivity. In this work, we integrate the neural machine translation into the interactive machine translation framework. Moreover, we propose new interactivity protocols, in order to provide the user an enhanced experience and a higher productivity. Results obtained over a simulated benchmark show that interactive neural systems can significantly improve the classical phrase-based approach in an interactive-predictive machine translation scenario. c 2016 Elsevier Ltd. All rights reserved.The authors wish to thank the anonymous reviewers for their careful reading and in-depth criticisms and suggestions. This work was partially funded by the project ALMAMATER (PrometeoII/2014/030). We also acknowledge NVIDIA for the donation of the GPU used in this work.Peris Abril, Á.; Domingo-Ballester, M.; Casacuberta Nolla, F. (2017). Interactive neural machine translation. Computer Speech and Language. 1-20. https://doi.org/10.1016/j.csl.2016.12.003S12

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. 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Un modelo de lenguaje neuronal recurrente bidireccional para la traducción automática

[EN] A language model based in continuous representations of words is presented, which has been applied to a statistical machine translation task. This model is implemented by means of a bidirectional recurrent neural network, which is able to take into account both the past and the future context of a word in order to perform predictions. Due to its high temporal cost at training time, for obtaining relevant training data an instance selection algorithm is used, which aims to capture useful information for translating a test set. Obtained results show that the neural model trained with the selected data outperforms the results obtained by an n-gram language model[ES] Se presenta un modelo de lenguaje basado en representaciones continuas de las palabras, el cual se ha aplicado a una tarea de traducción automática estadística. Este modelo está implementado por una red neuronal recurrente bidireccional, la cual es capaz de tener en cuenta el contexto pasado y futuro de una palabra para realizar predicciones. Debido su alto coste temporal de entrenamiento, para obtener datos de entrenamiento relevantes se emplea un algoritmo de selección de oraciones, el cual busca capturar información útil para traducir un determinado conjunto de test. Los resultados obtenidos muestran que el modelo neuronal entrenado con los datos seleccionados es capaz de mejorar los resultados obtenidos por un modelo de lenguaje de n-gramas.The research leading to these results has received funding from the the Generalitat Valenciana under grant Prometeo/2009/014.Peris Abril, Á.; Casacuberta Nolla, F. (2015). A Bidirectional Recurrent Neural Language Model for Machine Translation. Procesamiento del Lenguaje Natural. (55):109-116. http://hdl.handle.net/10251/64243S1091165

Traducción automática neuronal

[EN] From the outset, automatic translation was dominated by systems based on linguistic information, but then later other approaches opened up the way, such as translation memories and statistical machine translation which draw on parallel language corpora. Recently the neuronal machine translation (NMT) models have become the cutting edge in automatic translation and many translation agencies and well-known web pages are successfully using these technologies. One NMT model is a kind of statistical model comprising a group of simple deeply interconnected process units. The parameters of these models are estimated from parallel corpora using efficient automatic learning algorithms and powerful graphic processors. Applying these neural models to automatic translation requires words to be represented in the form of vectors and use recurrent neural networks in order to process phrases.[ES] La traducción automática estuvo dominada desde el principio por los sistemas basados en el conocimiento lingüístico, posteriormente se abrieron paso otras aproximaciones, tales como las memorias de traducción y los sistemas estadísticos de traducción, que extraían el conocimiento de corpus paralelos. Recientemente, los modelos neuronales constituyen el estado de vanguardia de la traducción automática. Numerosas empresas de traducción y conocidas páginas web están utilizando estas tecnologías con éxito. Un modelo neuronal es un tipo de modelo estadístico formado por un conjunto de unidades de proceso simple densamente conectadas entre si. Los parámetros de estos modelos se estiman a partir de corpus paralelos gracias a eficientes algoritmos de aprendizaje automático y a potentes procesadores gráficos. La aplicación de los modelos neuronales a la traducción automática obliga a que las palabras se representen en forma de vectores y que para procesar frases se utilicen redes neuronales recurrentes.[CA] La traducció automàtica va estar dominada des del començament pels sistemes basats en el coneixement lingüístic, posteriorment es van obrir pas altres aproximacions, tals com les memòries de traducció i els sistemes estadístics de traducció, que extreien el coneixement de corpus paral·lels. Recentment, els models neuronals constitueixen l¿estat de vanguarda de la traducció automàtica. Nombroses empreses de traducció i conegudes pàgines web estan fent servir aquestes tecnologies amb èxit. Un model neuronal és un tipus de model estadístic format per un conjunt d¿unitats de procés simple densament connectades entre elles. Els paràmetres d¿aquests models s¿estimen a partir de corpus paral·lels gràcies a algoritmes d¿aprenentatge automàtic eficients i a processadors gràfics potents. L¿aplicació dels models neuronals a la traducció automàtica obliga a què les paraules es representen en forma de vectors i que per tal de processar frases s¿utilitzen xarxes neuronals recurrents.Casacuberta Nolla, F.; Peris-Abril, Á. (2017). Traducción automática neuronal. Tradumàtica. (15):66-74. https://doi.org/10.5565/rev/tradumatica.203S66741

Egocentric video description based on temporally-linked sequences

[EN] Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures. In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1339 events with 3991 descriptions, from 55¿days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.This work was partially founded by TIN2015-66951-C2, SGR 1219, CERCA, Grant 20141510 (Marato TV3), PrometeoII/2014/030 and R-MIPRCV network (TIN2014-54728-REDC). Petia Radeva is partially founded by ICREA Academia'2014. Marc Bolanos is partially founded by an FPU fellowship. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X GPU used for this research. The funders had no role in the study design, data collection, analysis, and preparation of the manuscript.Bolaños, M.; Peris-Abril, Á.; Casacuberta Nolla, F.; Soler, S.; Radeva, P. (2018). Egocentric video description based on temporally-linked sequences. Journal of Visual Communication and Image Representation. 50:205-216. https://doi.org/10.1016/j.jvcir.2017.11.022S2052165