acl acl2011 acl2011-325 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Chris Dyer ; Jonathan H. Clark ; Alon Lavie ; Noah A. Smith
Abstract: We introduce a discriminatively trained, globally normalized, log-linear variant of the lexical translation models proposed by Brown et al. (1993). In our model, arbitrary, nonindependent features may be freely incorporated, thereby overcoming the inherent limitation of generative models, which require that features be sensitive to the conditional independencies of the generative process. However, unlike previous work on discriminative modeling of word alignment (which also permits the use of arbitrary features), the parameters in our models are learned from unannotated parallel sentences, rather than from supervised word alignments. Using a variety of intrinsic and extrinsic measures, including translation performance, we show our model yields better alignments than generative baselines in a number of language pairs.
Reference: text
sentIndex sentText sentNum sentScore
1 Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA j hclark alavie nasmith} @ cs . [sent-2, score-0.096]
2 edu , , Abstract We introduce a discriminatively trained, globally normalized, log-linear variant of the lexical translation models proposed by Brown et al. [sent-4, score-0.581]
3 In our model, arbitrary, nonindependent features may be freely incorporated, thereby overcoming the inherent limitation of generative models, which require that features be sensitive to the conditional independencies of the generative process. [sent-6, score-0.865]
4 However, unlike previous work on discriminative modeling of word alignment (which also permits the use of arbitrary features), the parameters in our models are learned from unannotated parallel sentences, rather than from supervised word alignments. [sent-7, score-0.85]
5 Using a variety of intrinsic and extrinsic measures, including translation performance, we show our model yields better alignments than generative baselines in a number of language pairs. [sent-8, score-0.734]
6 1 Introduction Word alignment is an important subtask in statistical machine translation which is typically solved in one of two ways. [sent-9, score-0.452]
7 The more common approach uses a generative translation model that relates bilingual string pairs using a latent alignment variable to designate which source words (or phrases) generate which target words. [sent-10, score-0.987]
8 The parameters in these models can be learned straightforwardly from parallel sentences using EM, and standard inference techniques can recover most probable alignments (Brown et al. [sent-11, score-0.489]
9 This approach is attractive because it only requires parallel training data. [sent-13, score-0.229]
10 An alternative to the generative approach uses a discriminatively trained 409 alignment model to predict word alignments in the parallel corpus. [sent-14, score-0.98]
11 Discriminative models are attractive because they can incorporate arbitrary, overlapping features, meaning that errors observed in the predictions made by the model can be addressed by engineering new and better features. [sent-15, score-0.488]
12 In the case of discriminative alignment mod- els, manual alignment data is required for training, which is problematic for at least three reasons. [sent-17, score-0.761]
13 Manual alignments are notoriously difficult to create and are available only for a handful of language pairs. [sent-18, score-0.35]
14 Third, the “correct” alignment annotation for different tasks may vary: for example, relatively denser or sparser alignments may be optimal for different approaches to (downstream) translation model induction (Lopez, 2008; Fraser, 2007). [sent-20, score-0.797]
15 Generative models have a different limitation: the joint probability of a particular setting of the random variables must factorize according to steps in a process that successively “generates” the values of the variables. [sent-21, score-0.331]
16 At each step, the probability of some value being generated may depend only on the generation history (or a subset thereof), and the possible values a variable will take must form a locally nor- malized conditional probability distribution (CPD). [sent-22, score-0.453]
17 While these locally normalized CPDs may be paProceedinPgosrt olafn thde, 4 O9rtehg Aonn,n Juuanle M 1e9e-2tin4g, 2 o0f1 t1h. [sent-23, score-0.201]
18 Ac s2s0o1ci1a Atiosnso fcoirat Cioonm foprut Caotimonpaulta Lti nognuails Lti cnsg,u piasgteics 409–419, rameterized so as to make use of multiple, overlapping features (Berg-Kirkpatrick et al. [sent-25, score-0.332]
19 , 2010), the requirement that models factorize according to a particular generative process imposes a considerable restriction on the kinds of features that can be incorporated. [sent-26, score-0.579]
20 1 In this paper, we introduce a discriminatively trained, globally normalized log-linear model of lexical translation that can incorporate arbitrary, overlapping features, and use it to infer word alignments. [sent-29, score-0.941]
21 Our model enjoys the usual benefits of discriminative modeling (e. [sent-30, score-0.154]
22 , parameter regularization, wellunderstood learning algorithms), but is trained entirely from parallel sentences without gold-standard × word alignments. [sent-32, score-0.114]
23 Thus, it addresses the two limitations of current word alignment approaches. [sent-33, score-0.228]
24 We begin by introducing our model (§2), and follow this with a dinistrcoudsusicoinng go fo tractability, parameter estimation, tahn da inference using finite-state techniques (§3). [sent-35, score-0.186]
25 We then idnefsecrreinbece et uhes specific -fsetaattuer etesc we quuseeds (§4) a Wnde provdiedsec experimental iecva fleuaattuiroens wofe eth ues model, showing substantial improvements in three diverse language pairs (§5). [sent-36, score-0.083]
26 2 Model In this section, we develop a conditional model p(t | s) that, given a source language sentence s with length m = |s| , assigns probabilities steon a target sentleenncgeth ht mwi =th length n, sw phroerbea ebailcihti wso tord a tj rigs an eenl-ement in the finite target vocabulary Ω. [sent-39, score-0.869]
27 We begin by using the chain rule to factor this probability into two components, a translation model and a length model. [sent-40, score-0.435]
28 p(t | s) = p(t, n | s) = p(t | s, n) p|(t|{ z s,n}) tra|nslati{ozn mo}del p(n | s) |p(n {z | s }) le|ngth { zmod }el 1Moore (2005) likewise uses| this{ ezxam}ple to |mo {tivzate } the need for models that support arbitrary, overlapping features. [sent-41, score-0.24]
29 410 In the translation model, we then assume that each word tj is a translation of one source word, or a special null token. [sent-42, score-0.576]
30 We therefore introduce a latent alignment variable a = ha1, a2 , . [sent-43, score-0.369]
31 , ani ∈ [0, m]n, walihgenrme aj = a0r represents a special null tiok ∈en [. [sent-46, score-0.195]
32 0 p(t | s,n) =Xp(t,a | s,n) Xa So far, our model is identical to that of (Brown et al. [sent-47, score-0.06]
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