gaof23/citation_sentiment_corpus huggingface dataset:
757 hbps strong tendency to overestimate the probability of rare bi-phrases; it is computed as in equation (2), except that bi-phrase probabilities are computed based on individual word translation probabilities, somewhat as in IBM model 1 (Brown et al. , 1993): Pr(t|s) = 1|s||t| productdisplay tt summationdisplay ss Pr(t|s)  The target language feature function htl: this is based on a N-gram language model of the target language.
Large volumes of training data of this kind are indispensable for constructing statistical translation models (Brown et al. , 1993; Melamed, 2000), acquiring bilingual lexicon (Gale and Church, 1991; Melamed, 1997), and building example-based machine translation (EBMT) systems (Nagao, 1984; Carl and Way, 2003; Way and Gough, 2003).
5.1.2 Learning Translation Model According to the standard statistical translation model (Brown et al., 1993), we can find the optimal model M by maximizing the probability of generating queries from documents or M = argmax M NY i=1 P(QijDi;M) 524 qw dw P(qwjdw,u) journal kdd 0.0176 journal conference 0.0123 journal journal 0.0176 journal sigkdd 0.0088 journal discovery 0.0211 journal mining 0.0017 journal acm 0.0088 music music 0.0375 music purchase 0.0090 music mp3 0.0090 music listen 0.0180 music mp3.com 0.0450 music free 0.0008 Table 1: Sample user profile To find the optimal word translation probabilities P(qwjdw;M ), we can use the EM algorithm.
Many existing systems for statistical machine translation (Garca-Varea and Casacuberta 2001; Germann et al. 2001; Nieen et al. 1998; Och, Tillmann, and Ney 1999) implement models presented by Brown, Della Pietra, Della Pietra, and Mercer (1993): The correspondence between the words in the source and the target strings is described by alignments that assign target word positions to each source word position.
Different approaches have been proposed for modeling Pr(s,a|t) in Equation (8): Zero-order models such as model 1, model 2,andmodel 3 (Brown et al. 1993) and the rstorder models such as model 4, model 5 (Brown et al. 1993), hidden Markov model (Ney et al. 2000), and model 6 (Och and Ney 2003).
Related Work The recent availability of large amounts of bilingual data has attracted interest in several areas, including sentence alignment (Gale and Church 1991b; Brown, Lai, and Mercer 1991; Simard, Foster and Isabelle 1992; Gale and Church 1993; Chen 1993), word alignment (Gale and Church 1991a; Brown et al. 1993; Dagan, Church, and Gale 1993; Fung and McKeown 1994; Fung 1995b), alignment of groups of words (Smadja 1992; Kupiec 1993; van der Eijk 1993), and statistical translation (Brown et al. 1993).
The usual Chinese NLP architecture first preprocesses input text through a word segmentation module (Chiang et al. 1992; Lin, Chiang, and Su 1992, 1993; Chang and Chen 1993; Wu and Tseng 1993; Sproat et al. 1994; Wu and Fung 1994), but, clearly, bilingual parsing will be hampered by any errors arising from segmentation ambiguities that could not be resolved in the isolated monolingual context because even if the Chinese segmentation is acceptable monolingually, it may not agree with the words present in the English sentence.
We are given a source (Chinese) sentence f = fJ1 = f1,,fj,,fJ, which is to be translated into a target (English) sentence e = eI1 = e1,,ei,,eI Among all possible target sentences, we will choose the sentence with the highest probability: eI1 = argmax eI1 {Pr(eI1|fJ1 )} (1) As an alternative to the often used source-channel approach (Brown et al. , 1993), we directly model the posterior probability Pr(eI1|fJ1 ) (Och and Ney, 2002) using a log-linear combination of feature functions.
1 Introduction The statistical machine translation framework (SMT) formulates the problem of translating a sentence from a source language S into a target language T as the maximization problem of the conditional probability: TM LM = argmaxT p(SjT) p(T), (1) where p(SjT) is called a translation model (TM), representing the generation probability from T into S, p(T) is called a language model (LM) and represents the likelihood of the target language (Brown et al. , 1993).
= = = = )(),( InverseM1 )(),( DirectM1 )(),( InverseMLE )(),( DirectMLE )|(),,( )|(),,( )(*, ),(),,(,*)( ),(),,( Atreelets s t Atreelets t s Atreelets Atreelets tspATSf stpATSf c cATSf c cATSf             We use word probability tables p(t | s) and p(s | t) estimated by IBM Model 1 (Brown et al. 1993).
By 17 0 10 20 30 40 50 60 70 80 90 100 10000 100000 1e+06 1e+07 Test Set Items with Translations (%) Training Corpus Size (num words) unigrams bigrams trigrams 4-grams Figure 1: Percent of unique unigrams, bigrams, trigrams, and 4-grams from the Europarl Spanish test sentences for which translations were learned in increasingly large training corpora increasing the size of the basic unit of translation, phrase-based machine translation does away with many of the problems associated with the original word-based formulation of statistical machine translation (Brown et al. , 1993).
1 Introduction Word alignmentdetection of corresponding words between two sentences that are translations of each otheris usually an intermediate step of statistical machine translation (MT) (Brown et al. , 1993; Och and Ney, 2003; Koehn et al. , 2003), but also has been shown useful for other applications such as construction of bilingual lexicons, word-sense disambiguation, projection of resources, and crosslanguage information retrieval.
1.2 Statistical modeling for translation Earlier work in statistical machine translation (Brown et al. , 1993) is based on the noisy-channel formulation where T = arg max T p(TjS) = argmax T p(T)p(SjT) (1) where the target language model p(T) is further decomposed as p(T) / productdisplay i p(tijti1, . . ., tik+1) where k is the order of the language model and the translation model p(SjT) has been modeled by a sequence of five models with increasing complexity (Brown et al. , 1993).
4.1.3 Letter Lexical Transliteration Similar to IBM Model-1 (Brown et al. , 1993), we use a bag-of-letter generative model within a block to approximate the lexical transliteration equivalence: P(fj+lj |ei+ki )= j+lproductdisplay jprime=j i+ksummationdisplay iprime=i P(fjprime|eiprime)P(eiprime|ei+ki ), (10) where P(eiprime|ei+ki ) similarequal 1/(k+1) is approximated by a bagof-word unigram.
To improve raw output from decoding, Portage relies on a rescoring strategy: given a list of n-best translations from the decoder, the system reorders this list, this time using a more elaborate loglinear model, incorporating more feature functions, in addition to those of the decoding model: these typically include IBM-1 and IBM-2 model probabilities (Brown et al. , 1993) and an IBM-1-based feature function designed to detect whether any word in one language appears to have been left without satisfactory translation in the other language; all of these feature functions can be used in both language directions, i.e. source-to-target and target-to-source.
Similar to work in image retrieval (Barnard et al. , 2003), we cast the problem in terms of Machine Translation: given a paired corpus of words and a set of video event representations to which they refer, we make the IBM Model 1 assumption and use the expectation-maximization method to estimate the parameters (Brown et al. , 1993):  =+ = m j ajm jvideowordpl Cvideowordp 1 )|()1()|( (1) This paired corpus is created from a corpus of raw video by first abstracting each video into the feature streams described above.
If we assign a probability a15a17a16a19a18 a12 a13a7a21a20a4a6a5a7a23a22 to each pair of strings a18 a12a14a13a7a25a24 a4 a5a7 a22, then according to Bayes decision rule, we have to choose the target string that maximizes the product of the target language model a15a17a16a19a18 a12a14a13a7 a22 and the string translation model a15a17a16a19a18a26a4a6a5 a7 a20 a12 a13 a7 a22 . Many existing systems for statistical machine translation (Berger et al. , 1994; Wang and Waibel, 1997; Tillmann et al. , 1997; Nieen et al. , 1998) make use of a special way of structuring the string translation model like proposed by (Brown et al. , 1993): The correspondence between the words in the source and the target string is described by alignments that assign one target word position to each source word position.
If the language model Pr(eI1) = p (eI1) depends on parameters and the translation model Pr(fJ1 jeI1) = p (fJ1 jeI1) depends on parameters, then the optimal parameter values are obtained by maximizing the likelihood on a parallel training corpus fS1;eS1 (Brown et al. , 1993):  = argmax SY s=1 p (fsjes) (3)  = argmax SY s=1 p (es) (4) Computational Linguistics (ACL), Philadelphia, July 2002, pp.
The score for a given candidate a9 is given by a modified IBM Model 1 probability (Brown et al. , 1993) as follows: a2a4a3a6a9a21a10a13a12a15a7a14a2 a15 a24a26a17a16 a2a4a3a6a9a19a18 a14a15a10a12 a7 (4) a2 a15 a20 a24a16a22a21a24a23a26a25a1a27a28a27a28a27 a20 a24a16a30a29a1a23a26a25 a31 a32 a33 a23a35a34a37a36 a3a38a12 a33 a10a12a9 a16a8a39 a7 (5) where a40 is the length of a9, a41 is the length of a12, a15 is a scaling factor based on the number of matches of a9 found, and a14 a33 is the index of the English word aligned with a12 a33 according to alignment a14 . The probability a36 a3a6a9 a16a8a39 a10a12 a33 a7 is a linear combination of the transliteration and translation score, where the translation score is a uniform probability over all dictionary entries for a12 a33 . The scored matches form the list of translation candidates.
To help our model learn that it is desirable to copy answer words into the question, we add to each corpus a list of identical dictionary word pairs w iw i . For each corpus, we use GIZA (Al-Onaizan et al. , 1999), a publicly available SMT package that implements the IBM models (Brown et al. , 1993), to train a QA noisy-channel model that maps flattened answer parse trees, obtained using the cut procedure described in Section 3.1, into questions.
The former term P(E) is called a language model, representing the likelihood of E. The latter term P(J|E) is called a translation model, representing the generation probability from E into J. As an implementation of P(J|E), the word alignment based statistical translation (Brown et al. , 1993) has been successfully applied to similar language pairs, such as FrenchEnglish and German English, but not to drastically dierent ones, such as JapaneseEnglish.
Within the generative model, the Bayes reformulation is used to estimate a31 a0a15a14a35a33a1a26a13a37a36 a31 a0a15a14a19a13 a31 a0a2a1a38a33a14a39a13 where a31 a0a15a14a39a13 is considered the language model, and a31 a0a2a1a38a33a14a19a13 is the translation model; the IBM (Brown et al. , 1993) models being the de facto standard.
Most of the phrase-based translation models have adopted the noisy-channel based IBM style models (Brown et al. , 1993): CMCT C1 BD BP CPD6CVD1CPDC CT C1 BD C8D6B4CU C2 BD CYCT C1 BD B5C8D6B4CT C1 BD B5 (1) In these model, we have two types of knowledge: translation model, C8D6B4CU C2 BD CYCT C1 BD B5 and language model, C8D6B4CT C1 BD B5.
Rather than learning how strings in one language map to strings in another, however, translation now involves learning how systematic patterns of errors in ESL learners English map to corresponding patterns in native English 2.2 A Noisy Channel Model of ESL Errors If ESL error correction is seen as a translation task, the task can be treated as an SMT problem using the noisy channel model of (Brown et al. , 1993): here the L2 sentence produced by the learner can be regarded as having been corrupted by noise in the form of interference from his or her L1 model and incomplete language models internalized during language learning.
is combined with [ ]E jiT,1+ to be aligned with [ ] F nmT,, then [ ]( ) [ ]( )ATTCNTATTr E K E i FEF jinmjinm,.Pr,P,1],[,],[ ],1[ += where K is the degree of .EiN Finally, the node translation probability is modeled as ( ) ( ) ( )tNtNlNlNNN EiFlEiFlEjFl PrPrPr  . And the text translation probability ( )EF ttPr is model using IBM model I (Brown et al 1993).
1 Introduction In a classical statistical machine translation, a foreign language sentence f J1 = f1, f2, fJ is translated into another language, i.e. English, eI1 = e1, e2,, eI by seeking a maximum likely solution of: eI1 = argmax eI1 Pr(eI1|f J1 ) (1) = argmax eI1 Pr( f J1|eI1)Pr(eI1) (2) The source channel approach in Equation 2 independently decomposes translation knowledge into a translation model and a language model, respectively (Brown et al. , 1993).
Learned vowels include (in order of generation probability): e, a, o, u, i, y. Learned sonorous consonants include: n, s, r, l, m. Learned non-sonorous consonants include: d, c, t, l, b, m, p, q. The model bootstrapping is good for dealing with too many parameters; we see a similar approach in Brown et als (1993) march from Model 1 to Model 5.
1 Introduction Aligning parallel text, i.e. automatically setting the sentences or words in one text into correspondence with their equivalents in a translation, is a very useful preprocessing step for a range of applications, including but not limited to machine translation (Brown et al. , 1993), cross-language information retrieval (Hiemstra, 1996), dictionary creation (Smadja et al. , 1996) and induction of NLP-tools (Kuhn, 2004).
The methodology used (Brown et al. , 1993) is based on the definition of a function Pr(tI1|sJ1) that returns the probability that tI1 is a 835 source Transferir documentos explorados a otro directorio interaction-0 Move documents scanned to other directory interaction-1 Move s canned documents to other directory interaction-2 Move scanned documents to a nother directory interaction-3 Move scanned documents to another f older acceptance Move scanned documents to another folder Figure 1: Example of CAT system interactions to translate the Spanish source sentence into English.
Berry et al (1993)) to yield W  W = U  S  V T as Figure 3 shows, where, for some order R lessmuch min(M,N) of the decomposition, U is a MR left singular matrix with rows ui, i = 1,,M, S is a RR diagonal matrix of singular values s1  s2    sR greatermuch 0, and V is NR a right singular matrix with rows vj, j = 1,,N. For each i, the scaled R-vector uiS may be viewed as representing wi, thei-th word in the vocabulary, and similarly the scaled R-vector vjS as representing dj, j-th document in the corpus.
These lists are rescored with the following models: (a) the different models used in the decoder which are described above, (b) two different features based on IBM Model 1 (Brown et al. , 1993), (c) posterior probabilities for words, phrases, n-grams, and sentence length (Zens and Ney, 2006; Ueffing and Ney, 2007), all calculated over the Nbest list and using the sentence probabilities which the baseline system assigns to the translation hypotheses.
4.3 Baseline We use a standard log-linear phrase-based statistical machine translation system as a baseline: GIZA++ implementation of IBM word alignment model 4 (Brown et al. , 1993; Och and Ney, 2003),8 the refinement and phrase-extraction heuristics described in (Koehn et al. , 2003), minimum-error-rate training 7More specifically, we choose the first English reference from the 7 references and the Chinese sentence to construct new sentence pairs.
To quickly (and approximately) evaluate this phenomenon, we trained the statistical IBM wordalignment model 4 (Brown et al. , 1993),1 using the GIZA++ software (Och and Ney, 2003) for the following language pairs: ChineseEnglish, Italian English, and DutchEnglish, using the IWSLT-2006 corpus (Takezawa et al. , 2002; Paul, 2006) for the first two language pairs, and the Europarl corpus (Koehn, 2005) for the last one.
1 Introduction For statistical machine translation (SMT), phrasebased methods (Koehn et al. , 2003; Och and Ney, 2004) and syntax-based methods (Wu, 1997; Alshawi et al. 2000; Yamada and Knignt, 2001; Melamed, 2004; Chiang, 2005; Quick et al. , 2005; Mellebeek et al. , 2006) outperform word-based methods (Brown et al. , 1993).
3.1 Model-based Phrase Pair Posterior In a statistical generative word alignment model (Brown et al., 1993), it is assumed that (i) a random variable a specifies how each target word fj is generated by (therefore aligned to) a source 1 word eaj; and (ii) the likelihood function f(f,a|e) specifies a generativeprocedurefromthesourcesentencetothe target sentence.
More specifically, by using translation probabilities, we can rewrite equation (11) and (12) as follow: nullnullnullnullnull null nullnull null nullnullnull null null nullnullnullnull null nullnull null null nullnull null   nullnull null null | null null null null nullnull null nullnull null nullnull null null null null nullnull null nullnull null  null null 1nullnull null nullnull null null null nullnull|nullnull (13) nullnullnullnullnull null nullnull null nullnullnull null null nullnullnullnull null nullnull null null nullnull null   nullnull null null | null null null null nullnull null nullnull null nullnull null null null null nullnull null nullnull null  null null 1nullnull null nullnull null null null nullnull|nullnull  (14) where nullnullnullnull|null null null  denotes the probability that topic term null  is the translation of null null . In our experiments, to estimate the probability nullnullnullnull|null null null , we used the collections of question titles and question descriptions as the parallel corpus and the IBM model 1 (Brown et al., 1993) as the alignment model.
This probability is computed using IBMs Model 1 (Brown et al., 1993): P(Q|A) = productdisplay qQ P(q|A) (3) P(q|A) = (1)Pml(q|A)+Pml(q|C) (4) Pml(q|A) = summationdisplay aA (T(q|a)Pml(a|A)) (5) where the probability that the question term q is generated from answer A, P(q|A), is smoothed using the prior probability that the term q is generated from the entire collection of answers C, Pml(q|C).
1 Introduction Parallel corpora have been shown to provide an extremely rich source of constraints for statistical analysis (e.g. , Brown et al. 1990; Gale & Church 1991; Gale et al. 1992; Church 1993; Brown et al. 1993; Dagan et al. 1993; Dagan & Church 1994; Fung & Church 1994; Wu & Xia 1994; Fung & McKeown 1994).
Therefore, P(g l e) is the sum of the probabilities of generating g from e over all possible alignments A, in which the position i in the target sentence g is aligned to the position ai in the source sentence e: P(gle) = I l m e ~, ~" IT t(g# le=jla(a~ Ij, l,m)= al=0 amm0j=l m ! e 1"I ~ t(g# l e,)a(ilj, t, m) (3) j=l i=0 (Brown et al. , 1993) also described how to use the EM algorithm to estimate the parameters a(i I j,l, m) and $(g I e) in the aforementioned model.
The simple model 1 (Brown et al. , 1993) for the translation of a SL sentence d = dldt in a TL sentence e = el em assumes that every TL word is generated independently as a mixture of the SL words: m l P(e\[d),,~ H ~ t(ej\[di) (2) j=l i=O In the equation above t(ej\[di) stands for the probability that ej is generated by di.
2.2.1 The evaluator The evaluator is a function p(t\[t', s) which assigns to each target-text unit t an estimate of its probability given a source text s and the tokens t' which precede t in the current translation of s. Our approach to modeling this distribution is based to a large extent on that of the IBM group (Brown et al. , 1993), but it diflhrs in one significant aspect: whereas the IBM model involves a "noisy channel" decomposition, we use a linear combination of separate predictions from a language model p(t\[t') and a translation model p(t\[s).
The sequence Ws is thought as a noisy version of WT and the best guess I)d~ is then computed as ^ W~ = argmax P(WTWs) wT = argmax P(WslWT)P(WT) (1) wT In (Brown et al. , 1993) they propose a method for maximizing P(WTIWs) by estimating P(WT) and P(WsIWT) and solving the problem in equation 1.
The model consists of a set of word-pair parameters p(t\[s) and position parameters p(j\[i,/); in model 1 (IBM1) the latter are fixed at 1/(1 + 1), as each position, including the empty position 0, is considered equally likely to contain a translation for w. Maximum likelihood estimates for these parameters can be obtained with the EM algorithm over a bilingual training corpus, as described in (Brown et al. , 1993).
In order to minimize the number of decision errors at the sentence level, we have to choose the sequence of target words eI1 according to the equation (Brown et al. 1993): eI1 = argmax eI1 n Pr(eI1jfJ1 ) o = argmax eI1 n Pr(eI1)Pr(fJ1 jeI1) o : Here, the posterior probability Pr(eI1jfJ1 ) is decomposed into the language model probability Pr(eJ1) and the string translation probability Pr(fJ1 jeI1).
If we assign a probability a13a15a14a17a16 a10a12a11a5a19a18a2 a3a5a21a20 to each pair of strings a16 a10 a11a5a12a22 a2a4a3a5 a20, then according to Bayes decision rule, we have to choose the English string that maximizes the product of the English language model a13a23a14a24a16 a10 a11a5 a20 and the string translation model a13a15a14a17a16a25a2 a3a5a26a18a10a27a11a5a28a20 . Many existing systems for statistical machine translation (Wang and Waibel, 1997; Nieen et al. , 1998; Och and Weber, 1998) make use of a special way of structuring the string translation model like proposed by (Brown et al. , 1993): The correspondence between the words in the source and the target string is described by alignments which assign one target word position to each source word position.
input pegging a ?transfer correct partially correct b incorrect 1 raw no M4 decoding c 7 4 4 2 stemmed yes M4 decoding 8 3 4 3 stemmed no M4 decoding 13 2 0 4 raw no gloss 13 1 1 5a stemmed yes gloss 8 3 4 5b stemmed yes gloss 12 2 1 6 stemmed no gloss 11 2 2 a pegging causes the training algorithm to consider a larger search space b correct top level category but incorrect sub-category c translation by maximizing the IBM Model 4 probability of the source/translation pair (Brown et al. , 1993; Brown et al. , 1995) classification might be performed by automatic procedures rather than humans.
We trained IBM Translation Model 4 (Brown et al. , 1993) both on our corpus alone and on the augmented corpus, using the EGYPT toolkit (Knight et al. , 1999; Al-Onaizan et al. , 1999), and then translated a number of texts using different translation models and different transfer methods, namely glossing (replacing each Tamil word by the most likely candidate from the translation tables created with the EGYPT toolkit) and Model 4 decoding (Brown et al. , 1995; Germann et al. , 2001).
6 Concluding remarks Our work presents a set of improvements on previous state of the art of Grammar Association: first, by providing better language models to the original system described in (Vidal et al. , 1993); second, by setting the technique into a rigorous statistical framework, clarifying which kind of probabilities have to be estimated by association models; third, by developing a novel and especially adequate association model: Loco C. On the other hand, though experimental results are quite good, we find them particularly relevant for pointing out directions to follow for further improvement of the Grammar Association technique.
However, in the Grammar Association context, when developing (using Bayes decomposition) the basic equations of the system presented in (Vidal et al. , 1993), it is said that the reverse model for a28 a13a37a3a38a5a39a32a21a0a35a7 does not seem to admit a simple factorization which is also correct and convenient, so crude heuristics were adopted in the mathematical development of the expression to be maximized.
We based our design on the IBM models 1 and 2 (Brown et al. , 1993), but taking into account that our model must generate correct derivations in a given grammar, not any seBEGIN some END<animals> eat <animals> (a) "some a88 animalsa89 eat a88 animalsa89 " BEGIN some END<animals> eat are <animals> dangerous (b) "some a88 animalsa89 are dangerous" BEGIN <animals> some END eat are <animals> dangerous (c) "a88 animalsa89 are dangerous" BEGIN snakes rats people some END eat are snakes rats people dangerous (d) Expansion of a88 animalsa89 Figure 3: Using a category a86 animalsa87 for "snakes", "rats" and "people" in the example of Figure 1.