emnlp emnlp2011 emnlp2011-93 knowledge-graph by maker-knowledge-mining

93 emnlp-2011-Minimum Imputed-Risk: Unsupervised Discriminative Training for Machine Translation


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Author: Zhifei Li ; Ziyuan Wang ; Jason Eisner ; Sanjeev Khudanpur ; Brian Roark

Abstract: Discriminative training for machine translation has been well studied in the recent past. A limitation of the work to date is that it relies on the availability of high-quality in-domain bilingual text for supervised training. We present an unsupervised discriminative training framework to incorporate the usually plentiful target-language monolingual data by using a rough “reverse” translation system. Intuitively, our method strives to ensure that probabilistic “round-trip” translation from a target- language sentence to the source-language and back will have low expected loss. Theoretically, this may be justified as (discriminatively) minimizing an imputed empirical risk. Empirically, we demonstrate that augmenting supervised training with unsupervised data improves translation performance over the supervised case for both IWSLT and NIST tasks.

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 zwang4 0 khudanpur@ j hu edu Abstract Discriminative training for machine translation has been well studied in the recent past. [sent-4, score-0.164]

2 We present an unsupervised discriminative training framework to incorporate the usually plentiful target-language monolingual data by using a rough “reverse” translation system. [sent-6, score-0.35]

3 Intuitively, our method strives to ensure that probabilistic “round-trip” translation from a target- language sentence to the source-language and back will have low expected loss. [sent-7, score-0.204]

4 Theoretically, this may be justified as (discriminatively) minimizing an imputed empirical risk. [sent-8, score-0.583]

5 Empirically, we demonstrate that augmenting supervised training with unsupervised data improves translation performance over the supervised case for both IWSLT and NIST tasks. [sent-9, score-0.296]

6 But bilingual data for such supervised training may be relatively scarce for a particular language pair (e. [sent-20, score-0.171]

7 We propose an unsupervised training approach, called minimum imputed risk training, which is conceptually straightforward: First guess x (probabilistically) from the observed y using a reverse Englishto-Chinese translation model pφ(x | y). [sent-32, score-1.294]

8 o Tdheel pθ (y | x) to do a good job at translating this imputed x back to y, as measured by a given performance metric. [sent-34, score-0.638]

9 Intuitively, our method strives to ensure that probabilistic “round-trip” translation from a targetlanguage sentence to the source-language and back again will have low expected loss. [sent-35, score-0.204]

10 ec th2o0d1s1 i Ans Nsoactuiartaioln La fonrg Cuaogmep Purtoatcieosnsainlg L,in pgaugies ti 9c2s0–929, of in-domain bilingual development data to discriminatively tune a small number of parameters in φ; and (3) a large amount of in-domain English monolingual data. [sent-39, score-0.251]

11 The novelty here is to exploit (3) to discriminatively tune the parameters θ of all translation model components,2 pθ(y|x) and pθ(y), not merely train a generative language m) aodndel pθ(y), as is the norm. [sent-40, score-0.257]

12 BLEU) on a set of (x, y) pairs with our unsupervised discriminative training using only y. [sent-43, score-0.177]

13 One may hence contrast our approach with the traditional supervised methods applied to the MT task such as minimum error rate training (Och, 2003; Macherey et al. [sent-44, score-0.164]

14 , 2008), minimum risk (Smith and Eisner, 2006; Li and Eisner, 2009), and MIRA (Watanabe et al. [sent-47, score-0.236]

15 — — 2 Supervised Discriminative Training via Minimization of Empirical Risk Let us first review discriminative training in the supervised setting—as used in MERT (Och, 2003) and subsequent work. [sent-52, score-0.193]

16 One wishes to tune the parameters θ of some complex translation system δθ (x). [sent-53, score-0.162]

17 The goal of discriminative training is to minimize the expected loss of δθ (·), under a given taskspecific loss function L(y0, y) tuhnadt measures haoskw2Note that the extra monolingual data is used only for tuning the model weights, but not for inducing new phrases or rules. [sent-56, score-0.521]

18 4 The true p(x, y) is, of course, not known and, in practice, one typically minimizes empirical risk by replacing p(x, y) above with the empirical distribution p˜(x, y) given by a supervised training set {(xi, yi) , i= 1, . [sent-64, score-0.315]

19 So we propose to replace 3This goal is different from the minimum risk training of Li and Eisner (2009) in a subtle but important way. [sent-74, score-0.272]

20 In both cases, θ∗ minimizes risk or expected loss, but the expectation is w. [sent-75, score-0.28]

21 different distributions: the expectation in Li and Eisner (2009) is under the conditional distribution p(y | x), while the expectation idne (1) ies uconndderit tihonea joint tdriisbutrtiibountio pn(y p(x, y). [sent-78, score-0.164]

22 We seek a decision rule δθ (x) that will incur low expected loss on observations x that are generated from unseen states of nature. [sent-80, score-0.192]

23 L(δθ (xi) , yi) with the expectation Xpφ(x|yi)L(δθ(x),yi), Xx (3) where pφ(· | ·) is a “reverse prediction model” that attempts t(o· impute t “hree missing xi cdtiaotna. [sent-83, score-0.55]

24 m Woed cela”ll thhaet resulting variant of (2) the minimization of imputed empirical risk, and say that θ∗= argθminN1XiN=1Xxpφ(x|yi)L(δθ(x),yi) (4) is the estimate with the minimum imputed risk6. [sent-84, score-1.273]

25 The minimum imputed risk objective of (4) could be evaluated by brute force as follows. [sent-85, score-0.86]

26 For each unsupervised example yi, use the reverse prediction model pφ(· | yi) to impute possible reverse translations Xi = {xi1, xi2, . [sent-87, score-0.985]

27 }, asnibdl ar edvde seeac thra (xij , yi) pair, weighted by pφ(xij | yi) ≤ 1, to an imputed training set . [sent-90, score-0.649]

28 Perform the supervised training of (2) on the imputed and weighted training data. [sent-92, score-0.743]

29 The second step means that we must use δθ to forward-translate each imputed xij, evaluate the loss of the translations yi0j against the corresponding true translation yi, and choose the θ that minimizes the weighted sum of these losses (i. [sent-93, score-0.965]

30 , the empirical risk when the empirical distribution p˜(x, y) is derived from the imputed training set). [sent-95, score-0.813]

31 Specific to our MT task, this tries to ensure that probabilistic “roundtrip” translation, from the target-language sentence yi to the source-language and back again, will have a low expected loss. [sent-96, score-0.287]

32 7 The trouble with this method is that the reverse model pφ generates a weighted lattice or hypergraph Xi encoding exponentially many translations ofyi, a Xnd it is computationally infeasible to forwardtranslate each xij ∈ Xi. [sent-97, score-0.653]

33 6One may exploit both supervised data {(xi , yi)} and unsupervised dmaatay {yj } tiot b perform semi-supervised training v uian an interpolation o {fy (2) at ond p (4). [sent-100, score-0.161]

34 2 The Reverse Prediction Model pφ A crucial ingredient in (4) is the reverse prediction model pφ(· |·) that attempts to impute the missing xi. [sent-106, score-0.643]

35 We will tr(·a|i·n) tthhaist mattoemdepl tisn t advance, doing tshien g be xst job we can from available data, including any outof-domain bilingual data as well as any in-domain monolingual data8 x. [sent-107, score-0.173]

36 Whereas δθ is a translation system that aims to produce a single, low-loss translation, the reverse version pφ is rather a probabilistic model. [sent-111, score-0.425]

37 It is supposed to give an accurate probability distribution over possible values xij of the missing input sentence xi. [sent-112, score-0.203]

38 All of these values are taken into account in (4), regardless of the loss that they would incur if they were evaluated for translation quality relative to the missing xi. [sent-113, score-0.351]

39 Thus, φ does not need to be trained to minimize the risk itself (so there is no circularity). [sent-114, score-0.195]

40 It may be tolerable for pφ to impute mediocre translations xij. [sent-117, score-0.353]

41 All that is necessary is that the (forward) translations generated from the imputed xij “simulate” the competing hypotheses that we would see when translating the correct Chinese input xi. [sent-118, score-0.763]

42 3 The Forward Translation System δθ and The Loss Function L(δθ(xi) , yi) The minimum empirical risk objective of (2) is quite general and various popular supervised training methods (Lafferty et al. [sent-120, score-0.371]

43 , 2006; Smith and Eisner, 8In a translation task from x to y, one usually does not make use of in-domain monolingual data x. [sent-122, score-0.173]

44 But we can exploit x to train a language model pφ (x) for the reverse translation system, which will make the imputed xij look like true Chinese inputs. [sent-123, score-1.114]

45 The generality of (2) extends to our minimum imputed risk objective of (4). [sent-125, score-0.86]

46 10 9One can manipulate the loss function to support other methods that use deterministic decoding, such as Perceptron (Collins, 2002) and MIRA (Crammer et al. [sent-142, score-0.188]

47 10Again, one may manipulate the loss function to support other probabilistic methods that use randomized decoding, such as CRFs (Lafferty et al. [sent-144, score-0.195]

48 1, it is computationally infeasible to forward-translate each of the imputed reverse translations xij. [sent-152, score-1.004]

49 For each yi, add to the imputed training set only the k most probable translations {xi1, . [sent-156, score-0.717]

50 ambiguous weighted finite-state automaton Xi, (b) athme fiogruwoaursd wtreaingshltaetidon fi system δθ uist smtruatcotunr Xed in a certain way as a weighted synchronous context-free grammar, and (c) the loss function decomposes in a certain way. [sent-176, score-0.213]

51 Intuitively, the reason why the structure-sharing in the hypergraph Xi (genewrhayted th by tuhcet reverse system) ch aynpneorgt abep exploited during forward translating is that when the forward Hiero system translates a string xi ∈ Xi, it must parse it into recursive phrases. [sent-184, score-0.925]

52 But the structure-sharing within the hypergraph of Xi has already parsed xi into recursive phrases, in a way determined by the reverse Hiero system; each translation phrase (or rule) corresponding to a hyperedge. [sent-185, score-0.622]

53 To exploit structure-sharing, we can use a forward translation system that decomposes according to that existing parse of xi. [sent-186, score-0.323]

54 We can do that by considering only forward translations that respect the hypergraph structure of Xi. [sent-187, score-0.34]

55 The simplest way to dthoe thhyisp eirsg to require complete isomorphism of the SCFG trees used for the reverse and forward translations. [sent-188, score-0.463]

56 Our deterministic test-time translation system δθ simply 12Note that the forward translation of a WFSA is tractable by using a lattice-based decoder such as that by Dyer et al. [sent-197, score-0.432]

57 For large γ, our training objective approaches the imputed risk of the deterministic test-time system while remaining differentiable. [sent-205, score-0.886]

58 EM The notion of imputing missing data is familiar from other settings (Little and Rubin, 1987), particularly the expectation maximization (EM) algorithm, a widely used generative approach. [sent-213, score-0.18]

59 So it is instructive to compare EM with minimum imputed risk. [sent-214, score-0.653]

60 (14) Notice that if we replace pθt (x|yi) with pφ(x | yi) in the equation above, and xa|dymit negated loglikelihood as a loss function, then the EM update (14) becomes identical to (4). [sent-221, score-0.218]

61 In other words, the minimum imputed risk approach of Section 3. [sent-222, score-0.819]

62 1 differs from EM in (i) using an externally-provided and static pφ, instead of refining it at each iteration based on the current pθt , and (ii) using a specific loss function, namely negated log-likelihood. [sent-223, score-0.192]

63 13 In summary, EM would impute missing data using pθ(x | y) and predict outputs using pθ (y | x), both being )co annddit piorneadli tfor omutsp otfs uthsien same joint model pθ(x, y). [sent-233, score-0.348]

64 Our minimum imputed risk training method is similar, but it instead uses a pair of 13Analogously, discriminative CRFs have become more popular than generative HMMs because they permit efficient training even with a wide variety of log-linear features (Lafferty et al. [sent-234, score-0.99]

65 By sticking to conditional models, we can efficiently use more sophisticated model features, and we can incorporate the loss function when we train θ, which should improve both efficiency and accuracy at test time. [sent-237, score-0.186]

66 1 IWSLT Task We train both reverse and forward baseline systems. [sent-243, score-0.49]

67 The translation models are built using the corpus for the IWSLT 2005 Chinese to English translation task (Eck and Hori, 2005), which comprises 40,000 pairs of transcribed utterances in the travel domain. [sent-244, score-0.23]

68 2 Target-rule Bigram Features In this paper, we do not attempt to discriminatively tune a separate parameter for each bilingual rule in the Hiero grammar. [sent-260, score-0.18]

69 Note that the reverse model φ is always trained using the supervised data of Dev φ, while the forward model θ may be trained in a supervised or semisupervised manner, as we will show below. [sent-277, score-0.579]

70 In all three data sets, each Chinese sentence xi has 16 English reference translations, so each yi is actually a set of 16 translations. [sent-278, score-0.372]

71 When we impute data from yi (in the semi-supervised scenario), we 14Ideally, we should train φ to minimize the conditional cross-entropy (5) as suggested in section 3. [sent-279, score-0.566]

72 Dev φ is used for discriminatively training of the reverse model φ, Dev θ is for the forward model, and Eval θ is for testing. [sent-283, score-0.57]

73 actually impute 16 different values of xi, by using pφ to separately reverse translate each sentence in yi. [sent-285, score-0.55]

74 4), where each xi is a different input sentence (imputed) in each case, but yi is always the original set of 16 references. [sent-287, score-0.372]

75 2 NIST Task For the NIST task, we use MT03 set (having 919 sentences) to tune the component parameters in both the forward and reverse baseline systems. [sent-290, score-0.495]

76 Additionally, we use the English side of MT04 (having 1788 sentences) to perform semi-supervised tuning of the forward model. [sent-291, score-0.168]

77 The supervised system (“Sup”) carries out discriminative training on a bilingual data set. [sent-296, score-0.298]

78 The semi-supervised system (“+Unsup”) additionally uses some monolingual English text for discriminative training (where we impute one Chinese translation per English sentence). [sent-297, score-0.591]

79 “+Unsup” means that we i 2n0cl0u×de1 6ad Ednigtli osnhal t (monolingual) English seeanntesn thceast from Dev θ for semi-supervised training; for each English sentence, we impute the 1-best Chinese translation. [sent-309, score-0.255]

80 1 Imputation with Different Reverse Models A critical component of our unsupervised method is the reverse translation model pφ(x | y). [sent-322, score-0.439]

81 We wonder how the performance of our unsupervised method changes when the quality of the reverse system varies. [sent-323, score-0.365]

82 To study this question, we used two different reverse translation systems, one with a language model trained on the Chinese side of the bitext (“WLM”), and the other one without using such a Chinese LM (“NLM”). [sent-324, score-0.397]

83 Table 4 (in the fully unsupervised case) shows that the imputed Chinese translations have a far lower BLEU score without the language model,15 and that this costs us about 1English 15The BLEU scores are low even with the language model because only one Chinese reference is available for scoring. [sent-325, score-0.723]

84 M96n7Uig with/without using a language model in the reverse × system. [sent-330, score-0.295]

85 A data size of 101 means that we use only the English sentences from a subset of Dev θ containing 101 Chinese sentences and 101 16 English translations; f1o0r1 e Cahchin English esnencteesn acned we impute tnhgeli 1sh-b tersant sClahtiinoensse; translation. [sent-331, score-0.255]

86 “WLM” means a Chinese language model is used in the reverse system, while “NLM” means no Chinese language model is used. [sent-332, score-0.295]

87 In addition to reporting the BLEU score on Eval θ, we also report “Imputed-CN BLEU”, the BLEU score of the imputed Chinese sentences against their corresponding Chinese reference sentences. [sent-333, score-0.583]

88 Still, even with the worse imputation (in the case of “NLM”), our forward translations improve as we add more monolingual data. [sent-335, score-0.446]

89 2 Imputation with Different k-best Sizes In all the experiments so far, we used the reverse translation system to impute only a single Chinese translation for each English monolingual sentence. [sent-338, score-0.853]

90 16 list, a sample, or a lattice for xi (see section 3. [sent-343, score-0.197]

91 6 Conclusions In this paper, we present an unsupervised discriminative training method that works with missing inputs. [sent-345, score-0.27]

92 The key idea in our method is to use a reverse model to impute the missing input from the observed output. [sent-346, score-0.643]

93 The training will then forward translate the imputed input, and choose the parameters of the forward model such that the imputed risk (i. [sent-347, score-1.704]

94 , 16In the present experiments, however, we simply weighted all k imputed translations equally, rather than in proportion to their posterior probabilities as suggested in Section 3. [sent-349, score-0.711]

95 r W Weaec hu English 1se6n mteonncoel we impute the k-best Chinese translations using the reverse system. [sent-354, score-0.674]

96 the expected loss of the forward translations with respect to the observed output) is minimized. [sent-355, score-0.427]

97 This matches the intuition that the probabilistic “roundtrip” translation from the target-language sentence to the source-language and back should have low expected loss. [sent-356, score-0.168]

98 In future work, we plan to test our method in settings where there are large amounts of monolingual training data (enabling many discriminative features). [sent-362, score-0.206]

99 First- and second-order expectation semirings with applications to minimumrisk training on translation forests. [sent-432, score-0.189]

100 Unsupervised discriminative language model training for machine translation using simulated confusion sets. [sent-445, score-0.237]


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