HAVE SHOWN SUPERIOR PERFORMANCE COMPAREDBASED TRANSLATION MODELS...

2004) have shown superior performance compared

based translation models (Trans) yielded better per-

to word-based translation models. In this paper,

formance than the traditional methods (VSM, Okapi

the goal of phrase-based translation model is to

and LM) for question retrieval. These models ex-

translate a document

⁴

D into a queried question

ploit the word translation probabilities in a language

q. Rather than translating single words in isola-

modeling framework. Following Jeon et al. (2005)

tion, the phrase-based model translates one sequence

and Xue et al. (2008), the ranking function can be

of words into another sequence of words, thus in-

written as:

corporating contextual information. For example,

we might learn that the phrase “stuffy nose” can be

tr

ml

translated from “cold” with relative high probabil-

w

∈

q

ity, even though neither of the individual word pairs

(e.g., “stuffy”/“cold” and “nose”/“cold”) might have

_tr

_ml

_ml

a high word translation probability. Inspired by the

work of (Sun et al., 2010; Gao et al., 2010), we

t

∈

D

assume the following generative process: first the

where P (w|t) denotes the translation probability

document D is broken into K non-empty word se-

from word t to word w.

quences t

1

, . . . , t

K

, then each t is translated into a

new non-empty word sequence w

₁

, . . . , w

_K

, and fi-