Aneurysmal bone cyst of proximal fibula treated with en-bloc excision: a rare case report

Aneurysmal bone cysts (ABCs) are benign but locally destructive, blood filled reactive lesions of the bone. Although a wider age group may be affected, most commonly they are seen in patients younger than 20 years of age, with a slight female preponderance. Most common sites include metaphysis of femur followed by tibia and then humerus. Vertebral lesions involving the posterior elements are common.Aneurysmal bone cyst of proximal fibula is a rare and uncommon. Here, we report a case of 13 year old female with classic histologic, clinical, and radiographic findings that was treated by en bloc resection. 

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

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

Towards Context-Aware Neural Performance-Score Synchronisation

PhD thesisMusic can be represented in multiple forms, such as in the audio form as a recording of a performance, in the symbolic form as a computer readable score, or in the image form as a scan of the sheet music. Music synchronisation provides a way to navigate among multiple representations of music in a unified manner by generating an accurate mapping between them, lending itself applicable to a myriad of domains like music education, performance analysis, automatic accompaniment and music editing. Traditional synchronisation methods compute alignment using knowledge-driven and stochastic approaches, typically employing handcrafted features. These methods are often unable to generalise well to different instruments, acoustic environments and recording conditions, and normally assume complete structural agreement between the performances and the scores. This PhD furthers the development of performance score synchronisation research by proposing data-driven, context-aware alignment approaches, on three fronts: Firstly, I replace the handcrafted features by employing a metric learning based approach that is adaptable to different acoustic settings and performs well in data-scarce conditions. Secondly, I address the handling of structural differences between the performances and scores, which is a common limitation of standard alignment methods. Finally, I eschew the reliance on both feature engineering and dynamic programming, and propose a completely data-driven synchronisation method that computes alignments using a neural framework, whilst also being robust to structural differences between the performances and scores

Contextual Handling in Neural Machine Translation: Look Behind, Ahead and on Both Sides

A salient feature of Neural Machine Translation (NMT) is the end-to-end nature of training employed, eschewing the need of separate components to model different linguistic phenomena. Rather, an NMT model learns to translate individual sentences from the labeled data itself. However, traditional NMT methods trained on large parallel corpora with a one-to-one sentence mapping make an implicit assumption of sentence independence. This makes it challenging for current NMT systems to model inter-sentential discourse phenomena. While recent research in this direction mainly leverages a single previous source sentence to model discourse, this paper proposes the incorporation of a context window spanning previous as well as next sentences as source-side context and previously generated output as target-side context, using an effective non-recurrent architecture based on self-attention. Experiments show improvement over non-contextual models as well as contextual methods using only previous context