Beyond Prefix-Based Interactive Translation Prediction
نویسندگان
چکیده
Current automatic machine translation systems require heavy human proofreading to produce high-quality translations. We present a new interactive machine translation approach aimed at providing a natural collaboration between humans and translation systems. As such, we grant the user complete freedom to validate and correct any part of the translations suggested by the system. Our approach is then designed according to the requirements placed by this unrestricted proofreading protocol. In particular, the ability of the system to suggest new translations coherent with the set of potentially disjoint translation segments validated by the user. We evaluate our approach in a usersimulated setting where reference translations are considered the output desired by a human expert. Results show important reductions in the number of edits in comparison to decoupled post-editing and conventional prefix-based interactive translation prediction. Additionally, we provide evidence that it can also reduce the cognitive overload reported for interactive translation systems in previous user studies.
منابع مشابه
Refinements to Interactive Translation Prediction Based on Search Graphs
We propose a number of refinements to the canonical approach to interactive translation prediction. By more permissive matching criteria, placing emphasis on matching the last word of the user prefix, and dealing with predictions to partially typed words, we observe gains in both word prediction accuracy (+5.4%) and letter prediction accuracy (+9.3%).
متن کاملNeural Interactive Translation Prediction
We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6% vs. 43.3%) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means ...
متن کاملApplication of Word-Level Confidence Measures in Interactive Statistical Machine Translation
In this paper, we will address the question of how to efficiently integrate word confidence measures into a state-of-the-art interactive statistical machine translation system and improve prediction performance. Different methods will be presented: the selection of words according to their confidence as well as the rejection which has not been investigated so far. Experimental evaluation with r...
متن کاملInteractive-Predictive Machine Translation based on Syntactic Constraints of Prefix
Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of...
متن کاملInteractive-Predictive Translation Based on Multiple Word-Segments
Current machine translation systems require human revision to produce high-quality translations. This is achieved through a post-editing process or by means of an interactive human–computer collaboration. Most protocols belonging to the last scenario follow a left-to-right strategy, where the prefix of the translation is iteratively increased by successive validations and corrections made by th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016