Granularity Effects in Tense Translation

Granularity Eeects in Tense Translation

Granularity E ects in Tense Translation

One of the daunting problems in machine translation (MT) is the mapping of tense. The paper singles out the problem of translating German present tense into English. This problem seems particularly instructive as its solution requires calculation of aspect as well as determination of the temporal location of events with respect to the time of speech. We present a disambiguation algorithm which ...

Segmenting Temporal Intervals for Tense and Aspect

Timelines interpreting interval temporal logic formulas are segmented into strings which serve as semantic representations for tense and aspect. The strings have bounded but refinable granularity, suitable for analyzing (im)perfectivity, durativity, telicity, and various relations including branching.

N-gram-based Tense Models for Statistical Machine Translation

Tense is a small element to a sentence, however, error tense can raise odd grammars and result in misunderstanding. Recently, tense has drawn attention in many natural language processing applications. However, most of current Statistical Machine Translation (SMT) systems mainly depend on translation model and language model. They never consider and make full use of tense information. In this p...

Classifier-Based Tense Model for SMT

Tense of one sentence can indicate the time when an event takes place. Therefore, it is very useful for natural language processing tasks such as Machine Translation (MT). However, the mapping of tense in MT is a very challenging problem as the usage of tenses varies from one language to another. Aiming at translating one language (source) which lacks overt tense markers into another language (...