| { |
| "paper_id": "P11-1024", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T08:46:25.357699Z" |
| }, |
| "title": "An exponential translation model for target language morphology", |
| "authors": [ |
| { |
| "first": "Michael", |
| "middle": [], |
| "last": "Subotin", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "University of Maryland", |
| "location": { |
| "settlement": "Paxfire" |
| } |
| }, |
| "email": "msubotin@gmail.com" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This paper presents an exponential model for translation into highly inflected languages which can be scaled to very large datasets. As in other recent proposals, it predicts targetside phrases and can be conditioned on sourceside context. However, crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of separate classifiers. We apply it to English-Czech translation, using a variety of features capturing potential predictors for case, number, and gender, and one of the largest publicly available parallel data sets. We also describe generation and modeling of inflected forms unobserved in training data and decoding procedures for a model with non-local target-side feature dependencies.", |
| "pdf_parse": { |
| "paper_id": "P11-1024", |
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| "abstract": [ |
| { |
| "text": "This paper presents an exponential model for translation into highly inflected languages which can be scaled to very large datasets. As in other recent proposals, it predicts targetside phrases and can be conditioned on sourceside context. However, crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of separate classifiers. We apply it to English-Czech translation, using a variety of features capturing potential predictors for case, number, and gender, and one of the largest publicly available parallel data sets. We also describe generation and modeling of inflected forms unobserved in training data and decoding procedures for a model with non-local target-side feature dependencies.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "Translation into languages with rich morphology presents special challenges for phrase-based methods. Thus, Birch et al (2008) find that translation quality achieved by a popular phrase-based system correlates significantly with a measure of targetside, but not source-side morphological complexity. Recently, several studies (Bojar, 2007; Avramidis and Koehn, 2009; Ramanathan et al., 2009; Yeniterzi and Oflazer, 2010) proposed modeling targetside morphology in a phrase-based factored models framework (Koehn and Hoang, 2007) . Under this approach linguistic annotation of source sentences is analyzed using heuristics to identify relevant structural phenomena, whose occurrences are in turn used to compute additional relative frequency (maximum likelihood) estimates predicting targetside inflections. This approach makes it difficult to handle the complex interplay between different predictors for inflections. For example, the accusative case is usually preserved in translation, so that nouns appearing in the direct object position of English clauses tend to be translated to words with accusative case markings in languages with richer morphology, and vice versa. However, there are exceptions. For example, some verbs that place their object in the accusative case in Czech may be rendered as prepositional constructions in English (Naughton, 2005) :", |
| "cite_spans": [ |
| { |
| "start": 108, |
| "end": 126, |
| "text": "Birch et al (2008)", |
| "ref_id": "BIBREF1" |
| }, |
| { |
| "start": 326, |
| "end": 339, |
| "text": "(Bojar, 2007;", |
| "ref_id": "BIBREF2" |
| }, |
| { |
| "start": 340, |
| "end": 366, |
| "text": "Avramidis and Koehn, 2009;", |
| "ref_id": null |
| }, |
| { |
| "start": 367, |
| "end": 391, |
| "text": "Ramanathan et al., 2009;", |
| "ref_id": "BIBREF20" |
| }, |
| { |
| "start": 392, |
| "end": 420, |
| "text": "Yeniterzi and Oflazer, 2010)", |
| "ref_id": "BIBREF23" |
| }, |
| { |
| "start": 505, |
| "end": 528, |
| "text": "(Koehn and Hoang, 2007)", |
| "ref_id": "BIBREF13" |
| }, |
| { |
| "start": 1344, |
| "end": 1360, |
| "text": "(Naughton, 2005)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "David was looking for Jana David hledal Janu David searched Jana-ACC Conversely, direct objects of some English verbs can be translated by nouns with genitive case markings in Czech:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "David asked Jana where Karel was David zeptal se Jany kde je Karel David asked SELF Jana-GEN where is Karel Furthermore, English noun modifiers are often rendered by Czech possessive adjectives and a verbal complement in one language is commonly translated by a nominalizing complement in another language, so that the part of speech (POS) of its head need not be preserved. These complications make it difficult to model morphological phenomena using closed-form estimates. This paper presents an alternative approach based on exponential phrase models, which can straightforwardly handle feature sets with arbitrarily elaborate source-side dependencies.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "We take as our starting point David Chiang's Hiero system, which generalizes phrase-based translation to substrings with gaps (Chiang, 2007) . Consider for instance the following set of context-free rules with a single non-terminal symbol:", |
| "cite_spans": [ |
| { |
| "start": 126, |
| "end": 140, |
| "text": "(Chiang, 2007)", |
| "ref_id": "BIBREF9" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Hierarchical phrase-based translation", |
| "sec_num": "2" |
| }, |
| { |
| "text": "A , A \u2192 A 1 A 2 , A 1 A 2 A , A \u2192 d A 1 id\u00e9es A 2 , A 1 A 2 ideas A , A \u2192 incolores , colorless A , A \u2192 vertes , green A , A \u2192 dorment A , sleep A A , A \u2192 f urieusement , f uriously", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Hierarchical phrase-based translation", |
| "sec_num": "2" |
| }, |
| { |
| "text": "It is one of many rule sets that would suffice to generate the English translation 1b for the French sentence 1a.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Hierarchical phrase-based translation", |
| "sec_num": "2" |
| }, |
| { |
| "text": "As shown by Chiang (2007) , a weighted grammar of this form can be collected and scored by simple extensions of standard methods for phrase-based translation and efficiently combined with a language model in a CKY decoder to achieve large improvements over a state-of-the-art phrase-based system. The translation is chosen to be the target-side yield of the highest-scoring synchronous parse consistent with the source sentence. Although a variety of scores interpolated into the decision rule for phrasebased systems have been investigated over the years, only a handful have been discovered to be consistently useful. In this work we concentrate on extending the target-given-source phrase model 1 .", |
| "cite_spans": [ |
| { |
| "start": 12, |
| "end": 25, |
| "text": "Chiang (2007)", |
| "ref_id": "BIBREF9" |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "1a. d' incolores id\u00e9es vertes dorment furieusement 1b. colorless green ideas sleep furiously", |
| "sec_num": null |
| }, |
| { |
| "text": "The model used in this work is based on the familiar equation for conditional exponential models:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "p(Y |X) = e w\u2022 f (X,Y ) Y \u2208GEN (X) e w\u2022 f (X,Y )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "where f (X, Y ) is a vector of feature functions, w is a corresponding weight vector, so that w", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 f (X, Y ) = i w i f i (X, Y )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": ", and GEN (X) is a set of values corresponding to Y . For a targetgiven-source phrase model the predicted outcomes are target-side phrases r y , the model is conditioned on a source-side phrase r x together with some context, and each GEN (X) consists of target phrases r y co-occurring with a given source phrase r x in the grammar.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Maximum likelihood estimation for exponential model finds the values of weights that maximize the likelihood of the training data, or, equivalently, its logarithm:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "LL( w) = log M m=1 p(Y m |X m ) = M m=1 log p(Y m |X m )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "where the expressions range over all training instances {m}. In this work we extend the objective using an 2 regularizer (Ng, 2004; Gao et al., 2007) . We obtain the counts of instances and features from the standard heuristics used to extract the grammar from a word-aligned parallel corpus.", |
| "cite_spans": [ |
| { |
| "start": 121, |
| "end": 131, |
| "text": "(Ng, 2004;", |
| "ref_id": "BIBREF18" |
| }, |
| { |
| "start": 132, |
| "end": 149, |
| "text": "Gao et al., 2007)", |
| "ref_id": "BIBREF10" |
| } |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Exponential models and other classifiers have been used in several recent studies to condition phrase model probabilities on source-side context (Chan et al 2007; Carpuat and Wu 2007a; Carpuat and Wu 2007b) . However, this has been generally accomplished by training independent classifiers associated with different source phrases. This approach is not well suited to modeling targetlanguage inflections, since parameters for the features associated with morphological markings and their predictors would be estimated separately from many, generally very small training sets, thereby preventing the model from making precisely the kind of generalization beyond specific phrases that we seek to obtain. Instead we continue the approach proposed in Subotin (2008) , where a single model defined by the equations above is trained on all of the data, so that parameters for features that are shared by rule sets with difference source sides reflect cumulative feature counts, while the standard relative frequency model can be obtained as a special case of maximum likelihood estimation for a model containing only the features for rules. 2 Recently, Jeong et al (2010) independently proposed an exponential model with shared features for target-side morphology in application to lexical scores in a treelet-based system.", |
| "cite_spans": [ |
| { |
| "start": 145, |
| "end": 162, |
| "text": "(Chan et al 2007;", |
| "ref_id": "BIBREF7" |
| }, |
| { |
| "start": 163, |
| "end": 184, |
| "text": "Carpuat and Wu 2007a;", |
| "ref_id": "BIBREF5" |
| }, |
| { |
| "start": 185, |
| "end": 206, |
| "text": "Carpuat and Wu 2007b)", |
| "ref_id": "BIBREF6" |
| }, |
| { |
| "start": 748, |
| "end": 762, |
| "text": "Subotin (2008)", |
| "ref_id": "BIBREF22" |
| }, |
| { |
| "start": 1148, |
| "end": 1166, |
| "text": "Jeong et al (2010)", |
| "ref_id": "BIBREF11" |
| } |
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| "section": "Exponential phrase models with shared features", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The feature space for target-side inflection models used in this work consists of features tracking the source phrase and the corresponding target phrase together with its complete morphological tag, which will be referred to as rule features for brevity. The feature space also includes features tracking the source phrase together with the lemmatized representation of the target phrase, called lemma features below. Since there is little ambiguity in lemmatization for Czech, the lemma representations were for simplicity based on the most frequent lemma for each token. Finally, we include features associating aspects of source-side annotation with inflections of aligned target words. The models include features for three general classes of morphological types: number, case, and gender. We add inflection features for all words aligned to at least one English verb, adjective, noun, pronoun, or determiner, excepting definite and indefinite articles. A separate feature type marks cases where an intended inflection category is not applicable to a target word falling under these criteria due to a POS mismatch between aligned words.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Features", |
| "sec_num": "4" |
| }, |
| { |
| "text": "The inflection for number is particularly easy to model in translating from English, since it is generally marked on the source side, and POS taggers based on the Penn treebank tag set attempt to infer it in cases where it is not. For word pairs whose source-side word is a verb, we add a feature marking the number of its subject, with separate features for noun and pronoun subjects. For word pairs whose source side is an adjective, we add a feature marking the number of the head of the smallest noun phrase that contains it.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Number", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Among the inflection types of Czech nouns, the only type that is not generally observed in English and does not belong to derivational morphology is inflection for case. Czech marks seven cases: nominal, genitive, dative, accusative, vocative, locative, and instrumental. Not all of these forms are overtly distinguished for all lexical items, and some words that function syntactically as nouns do not inflect at all. Czech adjectives also inflect for case and their case has to match the case of their governing noun. However, since the source sentence and its annotation contain a variety of predictors for case, we model it using only source-dependent features. The following feature types for case were included:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 The structural role of the aligned source word or the head of the smallest noun phrase containing the aligned source word. Features were included for the roles of subject, direct object, and nominal predicate.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 The preposition governing the smallest noun phrase containing the aligned source word, if it is governed by a preposition.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 An indicator for the presence of a possessive marker modifying the aligned source word or the head of the smallest noun phrase containing the aligned source word.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 An indicator for the presence of a numeral modifying the aligned source word or the head of the smallest noun phrase containing the aligned source word.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 An indication that aligned source word modified by quantifiers many, most, such, or half. These features would be more properly defined based on the identity of the target word aligned to these quantifiers, but little ambiguity seems to arise from this substitution in practice.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "\u2022 The lemma of the verb governing the aligned source word or the head of the smallest noun phrase containing the aligned source word. This is the only lexicalized feature type used in the model and we include only those features which occur over 1,000 times in the training data. Features corresponding to aspects of the source word itself and features corresponding to aspects of the head of a noun phrase containing it were treated as separate types.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Case", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Czech nouns belong to one of three cases: feminine, masculine, and neuter. Verbs and adjectives have to agree with nouns for gender, although this agreement is not marked in some forms of the verb. In contrast to number and case, Czech gender generally cannot be predicted from any aspect of the English source sentence, which necessitates the use of features that depend on another target-side word. This, in turn, requires a more elaborate decoding procedure, described in the next section. For verbs we add a feature associating the gender of the verb with the gender of its subject. For adjectives, we add a feature tracking the gender of the governing nouns. These dependencies are inferred from source-side annotation via word alignments, as depicted in figure 1, without any use of target-side dependency parses.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Gender", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "The procedure for decoding with non-local targetside feature dependencies is similar in its general outlines to the standard method of decoding with a language model, as described in Chiang (2007) . The search space is organized into arrays called charts, each containing a set of items whose scores can be compared with one another for the purposes of pruning. Each rule that has matched the source sentence belongs to a rule chart associated with its location-anchored sequence of non-terminal and terminal source-side symbols and any of its aspects which may affect the score of a translation hypothesis when it is combined with another rule. In the case of the language model these aspects include any of its target-side words that are part of still incomplete n-grams. In the case of non-local target-side dependencies this includes any information about features needed for this rule's estimate and tracking some target-side inflection beyond it or features tracking target-side inflections within this rule and needed for computation of another rule's estimate. We shall refer to both these types of information as messages, alluding to the fact that it will need to be conveyed to another point in the derivation to finish the computation. Thus, a rule chart for a rule with one nonterminal can be denoted as as", |
| "cite_spans": [ |
| { |
| "start": 183, |
| "end": 196, |
| "text": "Chiang (2007)", |
| "ref_id": "BIBREF9" |
| } |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
| }, |
| { |
| "text": "x i 1 i+1 Ax j j 1 +1", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
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| { |
| "text": ", \u00b5 , where we have introduced the symbol \u00b5 to represent the set of messages associated with a given item in the chart. Each item in the chart is associated with a score s, based on any submodels and heuristic estimates that can already be computed for that item and used to arrange the chart items into a priority queue. Combinations of one or more rules that span a substring of terminals are arranged into a different type of chart which we shall call span charts. A span chart has the form [i 1 , j 1 ; \u00b5 1 ], where \u00b5 1 is a set of messages, and its items are likewise prioritized by a partial score s 1 .", |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
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| { |
| "text": "The decoding procedure used in this work is based on the cube pruning method, fully described in Chiang (2007) . Informally, whenever a rule chart is combined with one or more span charts corresponding to its non-terminals, we select best-scoring items from each chart and update derivation scores by performing any model computations that become possible once we combine the corresponding items. Crucially, whenever an item in one of the charts crosses a pruning threshold, we discard the rest of that chart's items, even though one of them could generate a better-scoring partial derivation in com-bination with an item from another chart. It is therefore important to estimate incomplete model scores as well as we can. We estimate these scores by computing exponential models using all features without non-local dependencies.", |
| "cite_spans": [ |
| { |
| "start": 97, |
| "end": 110, |
| "text": "Chiang (2007)", |
| "ref_id": "BIBREF9" |
| } |
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| "ref_spans": [], |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Schematically, our decoding procedure can be illustrated by three elementary cases. We take the example of computing an estimate for a rule whose only terminal on both sides is a verb and which requires a feature tracking the target-side gender inflection of the subject. We make use of a cache storing all computed numerators and denominators of the exponential model, which makes it easy to recompute an estimate given an additional feature and use the difference between it and the incomplete estimate to update the score of the partial derivation. In the simplest case, illustrated in figure 2, the non-local feature depends on the position within the span of the rule's non-terminal symbol, so that its model estimate can be computed when its rule chart is combined with the span chart for its non-terminal symbol. This is accomplished using a feature message, which indicates the gender inflection for the subject and is denoted as m f (i), where the index i refers to the position of its \"recipient\". Figure 3 illustrates the case where the non-local feature lies outside the rule's span, but the estimated rule lies inside a non-terminal of the rule which contains the feature dependency. This requires sending a rule message m r (i), which includes information about the estimated rule (which also serves as a pointer to the score cache) and its feature dependency. The final example, shown in figure 4, illustrates the case where both types of messages need to be propagated until we reach a rule chart that spans both ends of the dependency. In this case, the full estimate for a rule is computed while combining charts neither of which corresponds directly to that rule.", |
| "cite_spans": [], |
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| "start": 1008, |
| "end": 1016, |
| "text": "Figure 3", |
| "ref_id": "FIGREF2" |
| } |
| ], |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
| }, |
| { |
| "text": "A somewhat more formal account of the decoding procedure is given in figure 5, which shows a partial set of inference rules, generally following the formalism used in Chiang (2007) , but simplifying it in several ways for brevity. Aside from the notation introduced above, we also make use of two updating functions. The message-updating function u m (\u00b5) takes a set of messages and outputs another set that includes those messages m r (k) and m f (k) whose destination k lies outside the span i, j of the chart. The score-updating function u s (\u00b5) computes those model estimates which can be completed using a message in the set \u00b5 and returns the difference between the new and old scores.", |
| "cite_spans": [ |
| { |
| "start": 167, |
| "end": 180, |
| "text": "Chiang (2007)", |
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| "section": "Decoding with target-side model dependencies", |
| "sec_num": "5" |
| }, |
| { |
| "text": "As a consequence of translating into a morphologically rich language, some inflected forms of target words are unobserved in training data and cannot be generated by the decoder under standard phrasebased approaches. Exponential models with shared features provide a straightforward way to estimate probabilities of unobserved inflections. This is accomplished by extending the sets of target phrases GEN (X) over which the model is normalized by including some phrases which have not been observed in the original sets. When additional rule features with these unobserved target phrases are included in the model, their weights will be estimated even though they never appear in the training examples (i.e, in the numerator of their likelihoods).", |
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| "ref_spans": [], |
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| "section": "Modeling unobserved target inflections", |
| "sec_num": "6" |
| }, |
| { |
| "text": "We generate unobserved morphological variants for target phrases starting from a generation procedure for target words. Morphological variants for words were generated using the\u00daFAL MORPHO tool (Kolovratn\u00edk and P\u0159ikryl, 2008) . The forms produced by the tool from the lemma of an observed inflected word form were subjected to several restrictions:", |
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| { |
| "start": 194, |
| "end": 225, |
| "text": "(Kolovratn\u00edk and P\u0159ikryl, 2008)", |
| "ref_id": "BIBREF15" |
| } |
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| "ref_spans": [], |
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| "section": "Modeling unobserved target inflections", |
| "sec_num": "6" |
| }, |
| { |
| "text": "\u2022 For nouns, generated forms had to match the original form for number.", |
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| "eq_spans": [], |
| "section": "Modeling unobserved target inflections", |
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| "text": "\u2022 For verbs, generated forms had to match the original form for tense and negation.", |
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| "text": "\u2022 For adjectives, generated forms had to match the original form for degree of comparison and negation.", |
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| "text": "\u2022 For pronouns, excepting relative and interrogative pronouns, generated forms had to match the original form for number, case, and gender.", |
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| "text": "\u2022 Non-standard inflection forms for all POS were excluded.", |
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| "text": "The following criteria were used to select rules for which expanded inflection sets were generated:", |
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| "text": "\u2022 The target phrase had to contain exactly one word for which inflected forms could be generated according to the criteria given above.", |
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| "text": "\u2022 If the target phrase contained prepositions or numerals, they had to be in a position not adjacent to the inflected word. The rationale for this criterion was the tendency of prepositions and numerals to determine the inflection of adjacent words.", |
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| "text": "\u2022 The lemmatized form of the phrase had to account for at least 25% of target phrases extracted for a given source phrase.", |
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| "text": "The standard relative frequency estimates for the p(X|Y ) phrase model and the lexical models do not provide reasonable values for the decoder scores for unobserved rules and words. In contrast, exponential models with surface and lemma features can be straightforwardly trained for all of them. For the experiments described below we trained an exponential model for the p(Y |X) lexical model. For greater speed we estimate the probabilities for the other two models using interpolated Kneser-Ney smoothing (Chen and Goodman, 1998) , where the surface form of a rule or an aligned word pair plays to role of a trigram, the pairing of the source surface form with the lemmatized target form plays the role of a bigram, and the source surface form alone plays the role of a unigram.", |
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| "text": "We investigate the models using the 2009 edition of the parallel treebank from\u00daFAL (Bojar an\u010f Zabokrtsk\u00fd, 2009) , containing 8,029,801 sentence pairs from various genres. The corpus comes with automatically generated annotation and a randomized split into training, development, and testing sets. Thus, the annotation for the development and testing sets provides a realistic reflection of what could be obtained for arbitrary source text. The English-side annotation follows the standards of the Penn Treebank and includes dependency parses and structural role labels such as subject and object. The Czech side is labeled with several layers of annotation, of which only the morphological tags and lemmas are used in this study. The Czech tags follow the standards of the Prague Dependency Treebank 2.0.", |
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| "text": "(Bojar an\u010f Zabokrtsk\u00fd, 2009)", |
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| "section": "Corpora and baselines", |
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| "text": "The impact of the models on translation accuracy was investigated for two experimental conditions:", |
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| "text": "\u2022 Small data set: trained on the news portion of the data, containing 140,191 sentences; development and testing sets containing 1500 sentences of news text each.", |
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| "text": "\u2022 Large data set: trained on all the training data; developing and testing sets each containing 1500 sentences of EU, news, and fiction data in equal portions. The other genres were excluded from the development and testing sets because manual inspection showed them to contain a considerable proportion of non-parallel sentences pairs.", |
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| "text": "All conditions use word alignments produced by sequential iterations of IBM model 1, HMM, and IBM model 4 in GIZA++, followed by \"diag-and\" symmetrization (Koehn et al., 2003) . Thresholds for phrase extraction and decoder pruning were set to values typical for the baseline system (Chiang, 2007) . Unaligned words at the outer edges of rules or gaps were disallowed. A 5-gram language model with modified interpolated Kneser-Ney smoothing (Chen and Goodman, 1998) was trained by the SRILM toolkit (Stolcke, 2002) on a set of 208 million running words of text obtained by combining the monolingual Czech text distributed by the 2010 ACL MT workshop with the Czech portion of the training data. The decision rule was based on the standard log-linear interpolation of several models, with weights tuned by MERT on the development set (Och, 2003) . The baselines consisted of the language model, two phrase translation models, two lexical models, and a brevity penalty.", |
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| "text": "(Chiang, 2007)", |
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| { |
| "text": "The proposed exponential phrase model contains several modifications relative to a standard phrase model (called baseline A below) with potential to improve translation accuracy, including smoothed estimates and estimates incorporating target-side tags. To gain better insight into the role played by different elements of the model, we also tested a second baseline phrase model (baseline B), which attempted to isolate the exponential model itself from auxiliary modifications. Baseline B was different from the experimental condition in using a grammar limited to observed inflections and in replacing the exponential p(Y |X) phrase model by a relative frequency phrase model. It was different from baseline A in computing the frequencies for the p(Y |X) phrase model based on counts of tagged target phrases and in using the same smoothed estimates in the other models as were used in the experimental condition.", |
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| "sec_num": "7" |
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| { |
| "text": "Parameter estimation was performed using a modified version of the maximum entropy module from SciPy (Jones et al., 2001 ) and the LBFGS-B algorithm (Byrd et al., 1995) . The objective included an 2 regularizer with the regularization trade-off set to 1. The amount of training data presented a practical challenge for parameter estimation. Several strategies were pursued to reduce the computational expenses. Following the approach of Mann et al (2009) , the training set was split into many approximately equal portions, for which parameters were estimated separately and then averaged for features observed in multiple portions. The sets of target phrases for each source phrase prior to generation of additional inflected variants were truncated by discarding extracted rules which were observed with frequency less than the 200-th most frequent target phrase for that source phrase. Additional computational challenges remained due to an important difference between models with shared features and usual phrase models. Features appearing with source phrases found in development and testing data share their weights with features appearing with other source phrases, so that filtering the training set for development and testing data affects the solution. Although there seems to be no reason why this would positively affect translation accuracy, to be methodologically strict we estimate parameters for rule and lemma features without inflection features for larger models, and then combine them with weights for inflection feature estimated from a smaller portion of training data. This should affect model performance negatively, since it precludes learning trade-offs between evidence provided by the different kinds of features, and therefore it gives a conservative assessment of the results that could be obtained at greater computational costs. The large data model used parameters for the inflection features estimated from the small data set. In the runs where exponential models were used they replaced the corresponding baseline phrase translation model. Table 1 shows the results. Aside from the two baselines described in section 7 and the full exponential model, the table also reports results for an exponential model that excluded gender-based features (and hence non-local target-side dependencies). The highest scores were achieved by the full exponential model, although baseline B produced surprisingly disparate effects for the two data sets. This suggests a complex interplay of the various aspects of the model and training data whose exploration could further improve the scores. Inclusion of genderbased features produced small but consistent improvements. Table 2 shows a summary of the grammars. We further illustrate general properties of these models using toy examples and the actual parameters estimated from the large data set. Table 3 shows representative rules with two different source sides. The column marked \"no infl.\" shows model estimates computed without inflection features. One can see that for both rule sets the estimated probabilities for rules observed a single time is only slightly higher than probabilities for generated unobserved rules. However, rules with relatively high counts in the second set receive proportionally higher estimates, while the difference between the singleton rule and the most frequent rule in the second set, which was observed 3 times, is smoothed away to an even greater extent. The last two columns show model estimates when various inflection features are included. There is a grammatical match between nominative case for the target word and subject position for the aligned source word and between accusative case for the target word and direct object role for the aligned source word. The other pairings represent grammatical mismatches. One can see that the probabilities for rules leading to correct case matches are considerably higher than the alternatives with incorrect case matches. ", |
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| { |
| "start": 101, |
| "end": 120, |
| "text": "(Jones et al., 2001", |
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| { |
| "start": 149, |
| "end": 168, |
| "text": "(Byrd et al., 1995)", |
| "ref_id": "BIBREF4" |
| }, |
| { |
| "start": 437, |
| "end": 454, |
| "text": "Mann et al (2009)", |
| "ref_id": "BIBREF16" |
| } |
| ], |
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| "start": 2076, |
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| "text": "Table 1", |
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| "text": "Table 2", |
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| "text": "Table 3", |
| "ref_id": "TABREF3" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Parameter estimation", |
| "sec_num": "8" |
| }, |
| { |
| "text": "This paper has introduced a scalable exponential phrase model for target languages with complex morphology that can be trained on the full parallel corpus. We have showed how it can provide estimates for inflected forms unobserved in the training data and described decoding procedures for features with non-local target-side dependencies. The results suggest that the model should be especially useful for languages with sparser resources, but that performance improvements can be obtained even for a very large parallel corpus.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Conclusion", |
| "sec_num": "10" |
| }, |
| { |
| "text": "To avoid confusion with features of the exponential models described below we shall use the term \"model\" rather than \"feature\" for the terms interpolated using MERT.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Note that this model is estimated from the full parallel corpus, rather than a held-out development set.", |
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| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "I would like to thank Philip Resnik, Amy Weinberg, Hal Daum\u00e9 III, Chris Dyer, and the anonymous reviewers for helpful comments relating to this work.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgments", |
| "sec_num": null |
| } |
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| "text": "Grammar sizes after and before generation of unobserved inflections (all filtered for dev/test sets)." |
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| "content": "<table><tr><td/><td/><td/><td>Sb</td><td>Obj</td></tr><tr><td>1</td><td>1</td><td>Dat</td><td colspan=\"2\">0.085 0.037 0.035</td></tr><tr><td>1</td><td>3</td><td>Acc</td><td colspan=\"2\">0.086 0.092 0.204</td></tr><tr><td>1</td><td>0</td><td colspan=\"3\">Nom 0.063 0.416 0.063</td></tr><tr><td>2</td><td>1</td><td>Instr</td><td colspan=\"2\">0.007 0.002 0.003</td></tr><tr><td>2</td><td>31</td><td colspan=\"3\">Nom 0.212 0.624 0.169</td></tr><tr><td>2</td><td>0</td><td>Acc</td><td colspan=\"2\">0.005 0.002 0.009</td></tr></table>", |
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| "text": "r x Count Case No infl." |
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| "text": "The effect of inflection features on estimated probabilities." |
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