Index of papers in Proc. ACL 2009 that mention
  • Viterbi
Li, Zhifei and Eisner, Jason and Khudanpur, Sanjeev
Abstract
Therefore, most systems use a simple Viterbi approximation that measures the goodness of a string using only its most probable derivation.
Background 2.1 Terminology
2.3 Viterbi Approximation
Background 2.1 Terminology
To approximate the intractable decoding problem of (2), most MT systems (Koehn et al., 2003; Chiang, 2007) use a simple Viterbi approximation,
Background 2.1 Terminology
The Viterbi approximation is simple and tractable, but it ignores most derivations.
Experimental Results
Viterbi 35.4 32.6 MBR (K=1000) 35.8 32.7 Crunching (N =10000) 35.7 32.8
Introduction
This corresponds to a Viterbi approximation that measures the goodness of an output string using only its most probable derivation, ignoring all the others.
Variational Approximate Decoding
The Viterbi and crunching methods above approximate the intractable decoding of (2) by ignoring most of the derivations.
Variational Approximate Decoding
Lastly, note that Viterbi and variational approximation are different ways to approximate the exact probability p(y | c), and each of them has pros and cons.
Variational Approximate Decoding
Specifically, Viterbi approximation uses the correct probability of one complete
Viterbi is mentioned in 23 sentences in this paper.
Topics mentioned in this paper:
DeNero, John and Chiang, David and Knight, Kevin
Abstract
The minimum Bayes risk (MBR) decoding objective improves BLEU scores for machine translation output relative to the standard Viterbi objective of maximizing model score.
Computing Feature Expectations
This forest-based MBR approach improved translation output relative to Viterbi translations.
Consensus Decoding Algorithms
The standard Viterbi decoding objective is to find 6* = arg maxe A - 6( f, e).
Consensus Decoding Algorithms
For MBR decoding, we instead leverage a similarity measure 8(6; 6’) to choose a translation using the model’s probability distribution P(e| f), which has support over a set of possible translations E. The Viterbi derivation 6* is the mode of this distribution.
Experimental Results
Table 2: Translation performance improves when computing expected sentences from translation forests rather than 104-best lists, which in turn improve over Viterbi translations.
Experimental Results
Figure 3: N - grams with high expected count are more likely to appear in the reference translation that 71- grams in the translation model’s Viterbi translation, 6*.
Experimental Results
We endeavored to test the hypothesis that expected n-gram counts under the forest distribution carry more predictive information than the baseline Viterbi derivation 6* , which is the mode of the distribution.
Viterbi is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Sun, Xu and Okazaki, Naoaki and Tsujii, Jun'ichi
Recognition as a Generation Task
When a context expression (CE) with a parenthetical expression (PE) is met, the recognizer generates the Viterbi labeling for the CE, which leads to the PE or NULL.
Recognition as a Generation Task
Then, if the Viterbi labeling leads to the PE, we can, at the same time, use the labeling to decide the full form within the CE.
Results and Discussion
The CRF+GI produced a Viterbi labeling with a low probability, which is an incorrect abbreviation.
Results and Discussion
To perform a systematic analysis of the superior-performance of DPLVM compare to CRF+GI, we collected the probability distributions (see Figure 7) of the Viterbi labelings from these models (“DPLVM vs. CRF+GI” is highlighted).
Results and Discussion
A large percentage (37.9%) of the Viterbi labelings from the CRF+GI (ENG) have very small probability values (p < 0.1).
Viterbi is mentioned in 8 sentences in this paper.
Topics mentioned in this paper: