Index of papers in Proc. ACL 2009 that mention
• loss function
Kruengkrai, Canasai and Uchimoto, Kiyotaka and Kazama, Jun'ichi and Wang, Yiou and Torisawa, Kentaro and Isahara, Hitoshi
 Introduction We describe k-best decoding for our hybrid model and design its loss function and the features appropriate for our task. Training method Given a training example (xt, yt), MIRA tries to establish a margin between the score of the correct path 3(xt,yt;w) and the score of the best candidate path 3(xt, j}; w) based on the current weight vector w that is proportional to a loss function L(yt, 37). Training method 4.3 Loss function Training method We instead compute the loss function through false positives (FF) and false negatives (FN).
loss function is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Kumar, Shankar and Macherey, Wolfgang and Dyer, Chris and Och, Franz
 Discussion In this paper, we have described how MERT can be employed to estimate the weights for the linear loss function to maximize BLEU on a development set. Experiments Note that N -best MBR uses a sentence BLEU loss function . Minimum Bayes-Risk Decoding This reranking can be done for any sentence-level loss function such as BLEU (Papineni et al., 2001), Word Error Rate, or Position-independent Error Rate.
loss function is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Li, Zhifei and Eisner, Jason and Khudanpur, Sanjeev
 Variational vs. Min-Risk Decoding They use the following loss function , of which a linear approximation to BLEU (Papineni et al., 2001) is a special case, Variational vs. Min-Risk Decoding With the above loss function , Tromble et al. Variational vs. Min-Risk Decoding 15 The MBR becomes the MAP decision rule of (1) if a so-called zero-one loss function is used: l(y, y’) = 0 if y = y’ ; otherwise l(y,y’) = 1.
loss function is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: