acl acl2011 acl2011-220 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jesus Gonzalez-Rubio ; Alfons Juan ; Francisco Casacuberta
Abstract: We present minimum Bayes-risk system combination, a method that integrates consensus decoding and system combination into a unified multi-system minimum Bayes-risk (MBR) technique. Unlike other MBR methods that re-rank translations of a single SMT system, MBR system combination uses the MBR decision rule and a linear combination of the component systems’ probability distributions to search for the minimum risk translation among all the finite-length strings over the output vocabulary. We introduce expected BLEU, an approximation to the BLEU score that allows to efficiently apply MBR in these conditions. MBR system combination is a general method that is independent of specific SMT models, enabling us to combine systems with heterogeneous structure. Experiments show that our approach bring significant improvements to single-system-based MBR decoding and achieves comparable results to different state-of-the-art system combination methods.
Reference: text
sentIndex sentText sentNum sentScore
1 e s Abstract We present minimum Bayes-risk system combination, a method that integrates consensus decoding and system combination into a unified multi-system minimum Bayes-risk (MBR) technique. [sent-4, score-0.641]
2 MBR system combination is a general method that is independent of specific SMT models, enabling us to combine systems with heterogeneous structure. [sent-7, score-0.23]
3 Experiments show that our approach bring significant improvements to single-system-based MBR decoding and achieves comparable results to different state-of-the-art system combination methods. [sent-8, score-0.367]
4 1 Introduction Once statistical models are trained, a decoding approach determines what translations are finally selected. [sent-9, score-0.205]
5 Two parallel lines of research have shown consistent improvements over the max–derivation decoding objective, which selects the highest probability derivation. [sent-10, score-0.18]
6 Consensus decoding procedures select translations for a single system with a minimum Bayes risk (MBR) (Kumar and Byrne, 2004). [sent-11, score-0.429]
7 System combination procedures, on the other hand, generate translations from the output of multiple component systems by combining the best fragments of these outputs (Frederking and Nirenburg, 1268 Alfons Juan Francisco Casacuberta D. [sent-12, score-0.352]
8 In this paper, we present minimum Bayes risk system combination, a technique that unifies these two approaches by learning a consensus translation over multiple underlying component systems. [sent-17, score-0.442]
9 MBR system combination operates directly on the outputs of the component models. [sent-18, score-0.339]
10 We perform an MBR decoding using a linear combination of the component models’ probability distributions. [sent-19, score-0.417]
11 Instead of re-ranking the translations provided by the component systems, we search for the hypothesis with the minimum expected translation error among all the possible finite-length strings in the target language. [sent-20, score-0.442]
12 , 2002), we avoid the hypothesis alignment problem that is central to standard system combination approaches (Rosti et al. [sent-22, score-0.255]
13 MBR system combination assumes only that each translation model can produce expectations of n-gram counts; the latent derivation structures ofthe component systems can differ arbitrary. [sent-24, score-0.404]
14 over the best single system max-derivation, and state-ofthe-art performance in the system combination task of the ACL 2010 workshop on SMT. [sent-29, score-0.264]
15 c s 2o0ci1a1ti Aonss foocria Ctioomnp fourta Ctioomnaplu Ltaintigouniaslti Lcisn,g puaigsetsic 1s268–1277, 2 Related Work MBR system combination is a multi-system generalization of MBR decoding where the space of hypotheses is not constrained to the space ofevidences. [sent-32, score-0.658]
16 We expand the space of hypotheses following some underlying ideas of system combination techniques. [sent-33, score-0.416]
17 1 Minimum Bayes risk In SMT, MBR decoding allows to minimize the loss of the output for a single translation system. [sent-35, score-0.376]
18 (2010) present an MBR decoding that makes use of a mixture of different SMT systems to improve translation accuracy. [sent-45, score-0.281]
19 (2010) present model combination, a multi-system lattice MBR decoding on the conjoined evidences spaces of the component systems. [sent-49, score-0.681]
20 Our technique differs in that we perform the search in an extended search space not restricted to the provided evidences, have fewer parameters to learn, and optimizes an expected BLEU score instead of the linear BLEU approximation. [sent-50, score-0.27]
21 2 System Combination System combination techniques in MT take as input the outputs {e1, · · · , eN} of N translation systems, ew ohuetpreu en eis a s·tr ,uectu}r oedf Ntrant raslnastiloanti object (or N-best lists thereof), typically viewed as a sequence of words. [sent-55, score-0.336]
22 A new search space is constructed from these backbone-aligned outputs and then a voting procedure of feature-based model predicts a final consensus translation (Rosti et al. [sent-57, score-0.304]
23 MBR system combination entirely avoids this alignment problem by considering hypotheses as n-gram occurrence vectors rather than word sequences. [sent-59, score-0.351]
24 MBR system combination performs the decoding in a larger search space and includes statistics from the components’ posteriors, whereas system combination techniques typically do not. [sent-60, score-0.725]
25 Despite these advantages, system combination may be more appropriate in some settings. [sent-61, score-0.209]
26 In particular, MBR system combination is designed primarily for statistical systems that generate N-best or lattice outputs. [sent-62, score-0.268]
27 MBR system combination can integrate non-statistical systems that generate either a single or an unweighted output. [sent-63, score-0.251]
28 However, we would not expect the same strong performance from MBR system combination in these constrained settings. [sent-64, score-0.228]
29 3 Minimum Bayes risk Decoding MBR decoding aims to find the candidate hypothesis that has the least expected loss under a probability model (Bickel and Doksum, 1977). [sent-65, score-0.398]
30 If the loss function between any two hypotheses can be bounded: L(e, e0) ≤ Lmax, the MBR deccoande br can u bned eredw:ri Ltt(een, ien )te ≤rm Lof a similarity function S(e, e0) = Lmax − L(e, e0). [sent-72, score-0.208]
31 (3) (4) MBR decoding can use different spaces for hypothesis selection and gain computation (arg max and summatory in Eq. [sent-74, score-0.377]
32 Therefore, the MBR decoder can be more generally written as follows: e = areg0∈mEhaxeX∈EeP(e|f) · S(e,e0) , (5) where Eh refers to the hypotheses space form where the translations are chosen and Ee refers to the evidences space that is used to compute the Bayes gain. [sent-76, score-0.596]
33 We will investigate the expansion of the hypotheses space while keeping the evidences space as provided by the decoder. [sent-77, score-0.528]
34 4 MBR System Combination MBR system combination is a multi-system generalization of MBR decoding. [sent-78, score-0.209]
35 It uses the MBR decision rule on a linear combination of the probability distributions of the component systems. [sent-79, score-0.259]
36 Unlike existing MBR decoding methods that re-rank translation outputs, MBR system combination search for the minimum risk hypotheses on the complete set of finite-length hypotheses over the output vocabulary. [sent-80, score-0.941]
37 We assume the component systems to be statistically independent and define the Bayes gain as a linear 1270 combination of the Bayes gains of the components. [sent-81, score-0.352]
38 Each system provides its own space of evidences Dn(f) and its posterior distribution over translations Pn(e|f). [sent-82, score-0.423]
39 It is worth mentioning that by using a linear combination instead of a mixture model, we avoid the problem of component systems not sharing the same search space (Duan et al. [sent-84, score-0.408]
40 MBR system combination parameters training and decoding in the extended hypotheses space are described below. [sent-86, score-0.592]
41 We used BLEU, choosing the scaling factors to maximize BLEU score of the set of translations predicted by MBR system combination. [sent-91, score-0.174]
42 2 Model Decoding In most MBR algorithms, the hypotheses space is equal to the evidences space. [sent-94, score-0.463]
43 Following the underlying idea of system combination, we are interested in extend the hypotheses space by including new sentences created using fragments of the hypotheses in the evidences spaces of the component models. [sent-95, score-0.787]
44 AMS algorithm perform a search on a hypotheses space equal to the free monoid Σ∗ of the vocabulary of the evidences Σ = V oc(Ee). [sent-100, score-0.559]
45 If the Bayes gain of any of the new edited hypotheses is higher than the Bayes gain of the current hypothesis (Line 17), we repeat the loop with this new hypotheses in other case, we return the current hypothesis. [sent-104, score-0.474]
46 AMS algorithm takes as input an initial hypothesis e and the combined vocabulary of the evidences spaces Σ. [sent-105, score-0.382]
47 Its output is a possibly new hypothesis whose Bayes gain is assured to be higher or equal than the Bayes gain of the initial hypothesis. [sent-106, score-0.19]
48 5 Computing BLEU-based Gain We are interested in performing MBR system combination under BLEU. [sent-109, score-0.209]
49 The evidences space Dn(f) may contain a huge numThbeer e ovifd heynpcoesth sepseasc1e w Dhich often make impractical to compute Eq. [sent-118, score-0.342]
50 (2008) propose linear BLEU, an approximation to the BLEU score to efficiently perform MBR decoding when the search space is represented with lattices. [sent-121, score-0.294]
51 However, our hypotheses space is the full set of finite-length strings in the target vocabulary and can not be represented in a lattice. [sent-122, score-0.235]
52 (9), we have one hypothesis e0 that is to be compared to a set of evidences e ∈ Dn(f) which fcoolmlopwa a probability fd eisvtidriebuntcieosn Pn(e|f) . [sent-124, score-0.302]
53 Instead of computing the expected BLEU score by ncastlecaudlating the BLEU score with respect to each of the evidences, our approach will be to use the expected n-gram counts and sentence length of the evidences to compute a single-reference BLEU score. [sent-125, score-0.427]
54 (10)) by the expected statistics (r0 and m0n) given the pos1For example, in a lattice the number of hypotheses exponential in the size of its state set. [sent-127, score-0.298]
55 Both, the expected length of the evidences r0 and their expected n-gram counts m0k can be pre-computed efficiently from N-best lists and translation lattices (Kumar et al. [sent-137, score-0.513]
56 R43m∗ax- derivation decoding (Best MAX), the best single system minimum Bayes risk decoding (Best MBR) and minimum Bayes risk system combination (MBR-SC) combining three systems. [sent-152, score-0.937]
57 For each system, we report the performance of max-derivation decoding (MAX) and 1000-best3 MBR decoding (Kumar and Byrne, 2004). [sent-161, score-0.316]
58 2 Experimental Results Table 2 compares MBR system combination (MBRSC) to the best MAX and MBR systems. [sent-163, score-0.209]
59 MBR-SC uses expected BLEU as gain function using the conjoined evidences spaces of the three systems to compute expected BLEU statistics. [sent-168, score-0.693]
60 MBR system combination improves single Best MAX system by +2. [sent-171, score-0.264]
61 This improvement could arise due to multiple reasons: the expected BLEU gain, the larger evidences space, the extended hypotheses space, or the MERT tuned scaling factor values. [sent-173, score-0.545]
62 Best MBR and MBR-SC-Expected differ only in the gain function: MBR uses sentence level BLEU while MBR-SC-Expected uses the expected BLEU gain described in Section 5. [sent-176, score-0.219]
63 MBRSC-Expected performance is comparable to MBR decoding on the 1000-best list from the single best system. [sent-177, score-0.158]
64 We now extend the evidences space to the conjoined 1000-best lists (MBR-SC-E/Conjoin). [sent-179, score-0.45]
65 This implies that either the expected BLEU statistics computed in the conjoined evidences space are stronger or the larger conjoined evidences spaces introduce better hypotheses. [sent-181, score-1.006]
66 When we restrict the BLEU statistics to be computed from only the best system’s evidences space 1273 (MBR-SC-E/C/evidences-best), BLEU scores dramatically decrease relative to MBR-SC-E/Conjoin. [sent-182, score-0.364]
67 This implies that the expected BLEU statistics computed over the conjoined 1000-best lists are stronger than the corresponding statistics from the single best system. [sent-183, score-0.345]
68 On the other hand, if we restrict the search space to only the 1000-best list of the best system (MBR-SC-E/C/hypotheses-best), BLEU scores also decrease relative to MBR-SC-E/Conjoin. [sent-184, score-0.161]
69 This implies that the conjoined search space also contains better hypotheses than the single best system’s search space. [sent-185, score-0.417]
70 The linear combination of the probability distributions in the conjoined evidences spaces allows to compute much stronger statistics for the expected BLEU gain and also contains some better hypotheses than the single best system’s search space does. [sent-187, score-1.101]
71 We next expand the conjoined evidences spaces using the decoding algorithm described in Section 4. [sent-188, score-0.568]
72 In this case, the expected BLEU statistics are computed from the conjoined 1000-best lists of the three systems, but the hypotheses space where we perform the decod- ing is expanded to the set of all possible finitelength hypotheses over the vocabulary of the evidences. [sent-190, score-0.624]
73 We take the output of MBR-SC-E/Conjoin as the initial hypotheses of the decoding (see Algorithm 1). [sent-191, score-0.3]
74 Since these two systems are identical in their expected BLEU statistics, the improvements in BLEU imply that the extended search space has introduced better hypotheses. [sent-193, score-0.22]
75 The degradation in TER performance can be explained by the use of a BLEU-based gain function in the decoding process. [sent-194, score-0.249]
76 Figure 1: Performance of minimum Bayes risk system combination (MBR-SC) for different sizes of the evidences space in comparison to other MBR-SC setups. [sent-201, score-0.699]
77 MBR-SC-E/C/Ex/MERT is the standard setup for MBR system combination and, from now, on we will refer to it as MBR-SC. [sent-202, score-0.209]
78 We next evaluate performance of MBR system combination on N-best lists of increasing sizes, and compare it to MBR-SC-E/C/Extended and MBRSC-E/Conjoin in the same N-best lists. [sent-203, score-0.236]
79 MBR-SCConjoin is consistently better than the Best MAX system, and differences in BLEU increase with the size of the evidences space. [sent-206, score-0.256]
80 This implies that the linear combination of posterior probabilities allow to compute stronger statistics for the expected BLEU gain, and, in addition, the larger the evidences space is, the stronger the computed statistics are. [sent-207, score-0.771]
81 This result show that the extended search space always contains better hypotheses than the conjoined evidences spaces; also confirms the soundness of Algorithm 1 that allows to reach them. [sent-210, score-0.643]
82 Figure 2 display the MBR system combination translation and compare it to the max-derivation translations of the three component systems. [sent-213, score-0.411]
83 3 Comparison to System Combination Figure 3 compares MBR system combination (MBR-SC) with state-of-the-art system combination techniques presented to the system combination task of the ACL 2010 workshop on MT (WMT2010). [sent-222, score-0.627]
84 All system combination techniques build a “word sausage” from the outputs of the different component systems and choose a path trough the sausage with the highest score under different models. [sent-223, score-0.387]
85 In this task, the output of the component systems are single hypotheses or unweighted lists thereof. [sent-226, score-0.286]
86 Therefore, we lack of the statistics of the components’ posteriors which is one of the main advantages of MBR system combination over system combination techniques. [sent-227, score-0.461]
87 However, we find that, even in these constrained setting, MBR system combination performance is similar to the best system combination techniques for all translation directions. [sent-228, score-0.517]
88 MBR system combination yields state-of-the-art performance while avoiding the challenge of aligning translation hypotheses. [sent-230, score-0.31]
89 7 Conclusion MBR system combination integrates consensus decoding and system combination into a unified multisystem MBR technique. [sent-231, score-0.639]
90 Component systems can have varied decoding strategies; we only require that each system produce an N-best list (or a lattice) of translations. [sent-234, score-0.234]
91 (2010) generate intermediate translations in several pivot languages, translate them separately into the target language, and generate a consensus translation out ofthese using a system combination technique. [sent-237, score-0.399]
92 MBR system combination has two significant advantages over current approaches to system combi- nation. [sent-239, score-0.264]
93 Aligning translation hypotheses can be challenging and has a substantial effect on combination performance (He et al. [sent-241, score-0.376]
94 Instead of aligning the sentences, we view the sentences as vectors of n-gram counts and compute the expected statistics of the BLEU score to compute the Bayes gain. [sent-243, score-0.181]
95 Choosing a backbone system can also be challenging and also affects system combination performance (He and Toutanova, 2009). [sent-245, score-0.302]
96 MBR system combination sidesteps this issue by working directly on the conjoined evidences space produced by the outputs of the component systems, and allows the consensus model to express system preferences via scaling factors. [sent-246, score-0.953]
97 Despite its simplicity, MBR system combination provides strong performance by leveraging different consensus, decoding and training techniques. [sent-247, score-0.367]
98 In addition, it obtains state- of-the-art performance in a constrained setting better suited for dominant system combination techniques. [sent-249, score-0.228]
99 Mixture model-based minimum bayes risk decoding using multiple machine translation systems. [sent-276, score-0.522]
100 Efficient minimum error rate training and minimum bayes-risk decoding for translation hypergraphs and lattices. [sent-326, score-0.394]
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