Twitter Sentiment Polarity Classification using Barrier Features
Authors: Anita Alicante, Anna Corazza, Antonio Pironti (Università di Napoli Federico II, 2016)
Topic: Machine Translation

Abstract
Recent advances in automatic post-editing (APE) have shown that it is possible to automatically correct systematic errors made by machine translation systems. However, most of the current APE techniques have only been tested in controlled batch environments, where training and test data are sampled from the same distribution and the training set is fully available. In this paper, we propose an online APE system based on an instance selection mechanism that is able to efficiently work with a stream of data points belonging to different domains. Our results on a mix of two datasets show that our system is able to: i) outperform state-of-the-art online APE solutions and ii) significantly improve the quality of rough MT output.

Introduction
Nowadays, machine translation (MT) is a core element in the computer-assisted translation (CAT) framework AUTHOR. The motivation for integrating MT in the CAT framework lies in its capability to provide useful suggestions for unseen segments, thus increasing translators productivity. However, it has been observed that MT is often prone to systematic errors that human post-editing has to correct before publication. The by-product of this ``translation as post-editing'' process is an increasing amount of parallel data consisting of MT output on one side and its corrected version on the other side. Besides being used to improve the MT system itself AUTHOR, this data can be leveraged to develop automatic MT quality estimation tools AUTHOR and automatic post-editing (APE) systems AUTHOR. The APE components explored in this paper should be capable not only to spot recurring MT errors, but also to correct them. Thus, integrating an APE system inside the CAT framework can further improve the quality of the suggested segments, reduce the workload of human post-editors and increase the productivity of translation industries. In the last decade many studies on APE have shown that the quality of the machine translated text can be improved significantly by post-processing the translations with an APE system AUTHOR. These systems mainly follow the phrase-based machine translation approach where the MT outputs (with optionally the source sentence) are used as the source language corpus and the post-edits are used as the target language corpus. Although these standard approaches showed promising results, they lack of the ability to continuously update their inner models by incorporating human feedback from a stream of data. To address this problem, several online systems have been proposed in MT, but only few of them have been applied to the APE scenario AUTHOR, only in a controlled working environment where they are trained and evaluated on homogeneous/coherent data sets. In this paper, we propose a novel online APE system that is able to efficiently leverage data from different domains. Our system is based on an instance selection technique that is able to retrieve the most relevant training instances from a pool of multi-domain data for each segment to post-edit. The selected data is then used to train and tune the APE system on-the-fly. The relevance of a training sample is measured by a similarity score that takes into account the context of the segment to be post-edited. This technique allows our online APE system to be flexible enough to decide if it has the correct knowledge for post-editing a sentence or if it is safer to keep the MT output untouched, avoiding possible damages. The results of our experiments over the combination of two data sets show that our approach is robust enough to work in a multi-domain environment and to generate reliable post-edits with significantly better performance than a state-of-the-art online APE system.

Conclusion
We addressed the problem of building a robust online APE system that is able to efficiently work on a stream of data points belonging to different domains. In this condition, our APE has shown its capability to continuously adapt to the dynamics of diverse data processed in real-time. In particular, the instance selection mechanism allows our APE method to reduce the number of %harming wrong modifications, which result in significant improvements in precision over the state-of-the-art online APE system, and thus making it a viable solution to be deployed in a real-word CAT framework.