Interactive-predictive neural multimodal systems

[EN] Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework.The research leading to these results has received funding from MINECO under grant IDIFEDER/2018/025 Sistemas de fabricacion inteligentes para la industria 4.0, action co-funded by the European Regional Development Fund 2014-2020 (FEDER), and from the European Commission under grant H2020, reference 825111 (DeepHealth). We also acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris, Á.; Casacuberta Nolla, F. (2019). Interactive-predictive neural multimodal systems. Springer. 16-28. https://doi.org/978-3-030-31332-6_2S162

A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration

Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation

In interactive machine translation (MT), human translators correct errors in auto- matic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source- language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional con- text for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human efforts in translation. Furthermore, when we model this source- and target-language syntactic information together as the con- ditional context, both types complement each other and our fully syntax-informed INMT model shows statistically significant reduction in human efforts for a French– to–English translation task in a reference- simulated setting, achieving 4.30 points absolute (corresponding to 9.18% relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01% relative) reduc- tion in terms of word stroke ratio (WSR) over the baseline