Multi-source Transformer for Automatic Post-Editing

Recent approaches to the Automatic Post-editing (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. In this paper, we pursue this objective by exploiting, for the first time in APE, the Transformer architecture. Our approach is much simpler than the best current solutions, which are based on ensembling multiple models and adding a final hypothesis re-ranking step. We evaluate our Transformer-based system on the English-German data released for the WMT 2017 APE shared task, achieving results that outperform the state of the art with a simpler architecture suitable for industrial applications.Gli approcci più efficaci alla correzione automatica di errori nella traduzione automatica (Automatic Postediting – APE) attualmente si basano su modelli neurali multi-source, capaci cioè di sfruttare informazione proveniente sia dalla frase da correggere che dalla frase nella lingua sorgente. Seguendo tale approccio, in questo articolo applichiamo per la prima volta l’architettura Transformer, ottenendo un sistema notevolmente meno complesso rispetto a quelli proposti fino ad ora (i migliori dei quali, basati sulla combinazione di più modelli). Attraverso esperimenti su dati Inglese-Tedesco rilasciati per l’APE task a WMT 2017, dimostriamo che, oltre a tale guadagno in termini di semplicità, il metodo proposto ottiene risultati superiori allo stato dell’arte

Multi-source Transformer for Automatic Post-Editing

Recent approaches to the Automatic Post-editing (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. In this paper, we pursue this objective by exploiting, for the first time in APE, the Transformerarchitecture. Our approach is much simpler than the best current solutions, which are based on ensembling multiple models and adding a final hypothesis reranking step. We evaluate our Transformer-based system on the English-German data released for the WMT 2017 APE shared task, achieving results that outperform the state of the art with a simpler architecture suitable for industrial applications

Data Augmentation for End-to-End Speech Translation: FBK@IWSLT '19

This paper describes FBK’s submission to the end-to-end speech translation (ST) task at IWSLT 2019. The task consists in the “direct” translation (ie without intermediate discrete representation) of English speech data derived from TED Talks or lectures into German texts. Our participation had a twofold goal: i) testing our latest models, and ii) evaluating the contribution to model training of different data augmentation techniques. On the model side, we deployed our recently proposed S-Transformer with logarithmic distance penalty, an ST-oriented adaptation of the Transformer architecture widely used in machine translation (MT). On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR). In particular, we exploited augmented data in different ways and at different stages of the process. We first trained an end-to-end ASR system and used the weights of its encoder to initialize the decoder of our ST model (transfer learning). Then, we used an English-German MT system trained on large data to translate the English side of the English-French training set into German, and used this newly-created data as additional training material. Finally, we trained our models using SpecAugment, an augmentation technique that randomly masks portions of the spectrograms in order to make them different at every training epoch. Our synthetic corpus and SpecAugment resulted in an improvement of 5 BLEU points over our baseline model on the test set of MuST-C En-De, reaching the score of 22.3 with a single end-to-end system

Multi-source transformer with combined losses for automatic post editing

Recent approaches to the Automatic Post-editing (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. To this aim, we present for the first time a neural multi-source APE model based on theTransformer architecture. Moreover, we employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics used for the task. These are the main features of our submissions to the WMT 2018APE shared task (Chatterjee et al., 2018), where we participated both in the PBSMT sub-task (i.e. the correction of MT outputs from a phrase-based system) and in the NMT sub-task (i.e. the correction of neural outputs).In the first subtask, our system improves over the baseline up to -5.3 TER and +8.23 BLEU points ranking second out of 11 submitted runs. In the second one, characterized by the higher quality of the initial translations, we report lower but statistically significant gains (up to -0.38 TER and +0.8 BLEU), ranking first out of 10 submissions

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Effort-Aware Neural Automatic Post-Editing

For this round of the WMT 2019 APE shared task, our submission focuses on addressing the “over-correction” problem in APE. Over-correction occurs when the APE system tends to rephrase an already correct MT output, and the resulting sentence is penalized by a reference-based evaluation against human post-edits. Our intuition is that this problem can be prevented by informing the system about the predicted quality of the MT output or, in other terms, the expected amount of needed corrections. For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing. Following the best submissions to the WMT 2018 APE shared task, our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text. We participated both in the English-German and English-Russian subtasks. In the first subtask, our best submission improved the original MT output quality up to +0.98 BLEU and -0.47 TER. In the second subtask, where the higher quality of the MT output increases the risk of over-correction, none of our submitted runs was able to improve the MT output

Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT Adaptation

Although machine translation (MT) traditionally pursues “human-oriented” objectives, humans are not the only possible consumers of MT output. For instance, when automatic translations are used to feed downstream Natural Language Processing (NLP) components in cross-lingual settings, they should ideally pursue “machine-oriented” objectives that maximize the performance of these components. Tebbifakhr et al. (2019) recently proposed a reinforcement learning approach to adapt a generic neural MT(NMT) system by exploiting the reward from a downstream sentiment classifier. But what if the downstream NLP tasks to serve are more than one? How to avoid the costs of adapting and maintaining one dedicated NMT system for each task? We address this problem by proposing a multi-task approach to machine-oriented NMT adaptation, which is capable to serve multiple downstream tasks with a single system. Through experiments with Spanish and Italian data covering three different tasks, we show that our approach can outperform a generic NMT system, and compete with single-task models in most of the settings

Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages

Neural Machine Translation (NMT) models are typically trained by considering humans as end-users and maximizing human-oriented objectives. However, in some scenarios, their output is consumed by automatic NLP components rather than by humans. In these scenarios, translations’ quality is measured in terms of their “fitness for purpose” (i.e. maximizing performance of external NLP tools) rather than in terms of standard human fluency/adequacy criteria. Recently, reinforcement learning techniques exploiting the feedback from downstream NLP tools have been proposed for “machine-oriented” NMT adaptation. In this work, we tackle the problem in a multilingual setting where a single NMT model translates from multiple languages for downstream automatic processing in the target language. Knowledge sharing across close and distant languages allows to apply our machine-oriented approach in the zero-shot setting where no labeled data for the test language is seen at training time. Moreover, we incorporate multi-lingual BERT in the source side of our NMT system to benefit from the knowledge embedded in this model. Our experiments show coherent performance gains, for different language directions over both i) “generic” NMT models (trained for human consumption), and ii) fine-tuned multilingual BERT. This gain for zero-shot language directions (e.g. Spanish–English) is higher when the models are fine-tuned on a closely-related source language (Italian) than a distant one (German)