PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving On-line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation
Vicent Alabau, Alberto Sanchis and Francisco Casacuberta
In: ACL-HLT 2011(2011).


In interactive machine translation (IMT), a hu- man expert is integrated into the core of a ma- chine translation (MT) system. The human ex- pert interacts with the IMT system by partially correcting the errors of the system’s output. Then, the system proposes a new solution. This process is repeated until the output meets the desired quality. In this scenario, the in- teraction is typically performed using the key- board and the mouse. In this work, we present an alternative modality to interact within IMT systems by writing on a tactile display or us- ing an electronic pen. An on-line handwrit- ten text recognition (HTR) system has been specifically designed to operate with IMT sys- tems. Our HTR system improves previous ap- proaches in two main aspects. First, HTR de- coding is tightly coupled with the IMT sys- tem. Second, the language models proposed are context aware, in the sense that they take into account the partial corrections and the source sentence by using a combination of n- grams and word-based IBM models. The pro- posed system achieves an important boost in performance with respect to previous work.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8789
Deposited By:Alfons Juan
Deposited On:21 February 2012