hrithikpiyush/acl-arc huggingface dataset:
In practice , we see that the choice of which language is source versus target matters and changes the mistakes made by the model ( the first row of panels in Figure 1 ) . The standard approach is to train two models independently and then intersect their predictions ( Och and Ney 2003 ) . However , we show that it is much better to train two directional models concurrently , coupling their posterior distributions over alignments to approximately agree .
In order to evaluate our algorithms , we first need to generate multimodal corpora for each of our nontextual modalities . We use the same method as Andrews et al. ( 2009 ) for generating our multimodal corpora : for each word token in the text corpus , a feature is selected stochastically from the word 's feature distribution , creating a word-feature pair . Words without grounded features are all given the same placeholder feature , also resulting in a wordfeature pair .5 That is , for the feature norm modality , we generate ( word , feature norm ) pairs ; for the SURF modality , we generate ( word , codeword ) pairs , etc. .
Normalizing by Pr ( v ) ( yielding MI ) allows us to use a constant threshold across all verbs . MI was also recently used for inference-rule SPs by Pantel et al. ( 2007 ) . classifier for each predicate independently .
The most similar efforts to ours , mainly ( DeNero et al. , 2006 ) , conclude that segmentation variables in the generative translation model lead to overfitting while attaining higher likelihood of the training data than the heuristic estimator . Based on this advise ( Moore and Quirk , 2007 ) exclude the latent segmentation variables and opt for a heuristic training procedure . In this work we also start out from a generative model with latent segmentation variables .
Some of this new data will be manually annotated . For example , 10 million words of the American National Corpus ( Ide et al. , 2002 ) will have manually corrected POS tags , a tenfold increase over the Penn Treebank ( Marcus et al. , 1993 ) , currently used for training POS taggers . This will require more efficient learning algorithms and implementations .
Write wjk as ( pjk , vjk ) , and let w1jk = ( p1jk , v1 jk ) denote the weight of the edge from j to k. 19 Then it can be shown that w0n = ( p0n , Ej , k p0jv1jkpkn ) . The forward and backward probabilities , p0j and pkn , can be computed using single-source algebraic path for the simpler semiring ( R , + , x , ∗ ) -- or equivalently , by solving a sparse linear system of equations over R , a much-studied problem at O ( n ) space , O ( nm ) time , and faster approximations ( Greenbaum , 1997 ) . • A Viterbi variant of the expectation semiring exists : replace ( 3 ) with if ( p1 > p2 , ( p1 , v1 ) , ( p2 , v2 ) ) .
The general form is illustrated by 3Conceptually , the parameters represent the probabilities of reading another a ( A ) ; reading another b ( ν ) ; transducing b to p rather than q ( µ ) ; starting to transduce p to a rather than x ( p ) . 4To prove ( 1 ) ⇒ ( 3 ) , express f as an FST and apply the well-known Kleene-Sch ¨ utzenberger construction ( Berstel and Reutenauer , 1988 ) , taking care to write each regexp in the construction as a constant times a probabilistic regexp . A full proof is straightforward , as are proofs of ( 3 ) ⇒ ( 2 ) , ( 2 ) ⇒ ( 1 ) .
Most DOP models , such as in Bod ( 1993 ) , Goodman ( 1996 ) , Bonnema et al. ( 1997 ) , Sima'an ( 2000 ) and Collins & Duffy ( 2002 ) , use a likelihood criterion in defining the best parse tree : they take ( some notion of ) the most likely ( i.e. most probable ) tree as a candidate for the best tree of a sentence . We will refer to these models as Likelihood-DOP models , but in this paper we will specifically mean by `` Likelihood-DOP '' the PCFG-reduction of Bod ( 2001 ) given in Section 2.2 .
State-of-theart machine learning and rule-based SBD systems achieve an error rate of 0.8 -- 1.5 % measured on the Brown corpus and the WSJ corpus . The best performance on the WSJ corpus was achieved by a combination of the SATZ system ( Palmer and Hearst 1997 ) with the Alembic system ( Aberdeen et al. 1995 ) : a 0.5 % error rate . The best performance on the Brown corpus , a 0.2 % error rate , was reported by Riley ( 1989 ) , who trained a decision tree classifier on a 25-million-word corpus .
frame definitions , and the extracted frames include details of specific prepositions . The extracted frames are noisy as a result of parser errors and so are filtered using the binomial hypothesis theory ( BHT ) , following Brent ( 1993 ) . Applying his technique to approximately four million words of New York Times newswire , Manning acquired 4,900 verb-subcategorization frame pairs for 3,104 verbs , an average of 1.6 frames per verb .
For sake of efficiency , we used the suffix array technique to get a compact representation of our training corpus . This method allows the efficient retrieval of arbitrary length n-grams ( Nagao and Mori , 94 ; Haruno et al. , 96 ; Ikehaxa et al. , 96 ; Shimohata et al. , 1997 ; Russell , 1998 ) . The literature abounds in measures that can help to decide whether words that co-occur are linguistically significant or not .
Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set , rivaling discriminative reranking approaches ( Charniak and Johnson , 2005 ) and products of latent variable grammars ( Petrov , 2010 ) , despite being a single generative PCFG . Our most accurate product model achieves an F score of 92.5 without the use of discriminative reranking and comes close to the best known numbers on this test set ( Zhang et al. , 2009 ) . In future work , we plan to investigate additional methods for increasing the diversity of our selftrained models .
The output of the transformation program can be used to derive entries which are appropriate for particular grammatical formalisms . To demonstrate that this is possible we have implemented a system which constructs dictionary entries for the PATR-II system ( Shieber , 1984 and references therein ) . PATR-II was chosen because it has been reimplemented in Cambridge and was therefore , available ; however , the task would be nearly identical if we were constructing entries for a system based on GPSG , FUG or LFG .
The manual translation demanded considerable efforts from the translator , and obscures the equivalence between the original and obtained grammars . Other works ( Kasper et al. , 1995 ; Becker and Lopez , 2000 ) convert HPSG grammars into LTAG grammars . However , given the greater expressive power of HPSG , it is impossible to convert an arbitrary HPSG grammar into an LTAG grammar .
PICO-based querying in information retrieval is merely an instance of faceted querying , which has been widely used by librarians since the introduction of automated retrieval systems ( e.g. , Meadow et al. 1989 ) . The work of Hearst ( 1996 ) demonstrates that faceted queries can be converted into simple filtering constraints to boost precision . The feasibility of automatically identifying outcome statements in secondary sources has been demonstrated by Niu and Hirst ( 2004 ) .
( Mohri , 2002 ) . Efficient hardware implementation is also possible via chip-level parallelism ( Rote , 1985 ) . • In many cases of interest , Ti is an acyclic graph .20 Then Tar an 's method computes w0j for each j in topologically sorted order , thereby finding ti in a linear number of ⊕ and ⊗ operations .
However , n ( u ) = Ev n ( u , v ) , which is not the same as the frequency of u , because each token of u can co-occur with several differentv 's . 2We could just as easily use other symmetric `` association '' measures , such as 02 ( Gale & Church , 1991 ) or the Dice coefficient ( Smadja , 1992 ) . co-occur is called a direct association .
9.4.1 A New Perspective on Salience . Krahmer and Theune ( 2002 ) have argued that Dale and Reiter 's ( 1995 ) dichotomy between salient and nonsalient objects ( where the objects in the domain are the salient ones ) should be replaced by an account that takes degrees of salience into account : No object can be too unsalient to be referred to , as long as the right properties are available . In effect , this proposal ( which measured salience numerically ) analyzes the black mouse as denoting the unique most salient object in the domain that is both black and a mouse .
A verb is just as likely to be mis-recognized or not recognized as is a noun , adjective , determiner , etc. . The acquisition of dialogue as implemented in VNLCE is reminiscent of the program synthesis methodology developed by Biermann and Krishnaswamy ( 1976 ) where program flowcharts were constructed from traces of their behaviors . However , the `` flowcharts '' in the current project are probabilistic in nature and the problems associated with matching incoming sentences to existing nodes has not been previously addressed .
Each punctuation symbol is considered a separate token . Character classes , such as punctuation , are defined according to the Unicode Standard ( Aliprand et al. , 2004 ) .
Secondly , the cooperative principle of Grice ( 1975 , 1978 ) , under the assumption that referential levels of a writer and a reader are quite similar , implies that the writer should structure the text in a way that makes the construction of his intended model easy for the reader ; and this seems to imply that he should appeal only to the most direct knowledge of the reader . Finally , it has been shown by Groesser ( 1981 ) that the ratio of derived to explicit information necessary for understanding a piece of text is about 8:1 ; furthermore , our reading of the analysis of five paragraphs by Crothers ( 1979 ) strongly suggests that only the most direct or obvious inferences are being made in the process of building a model or constructing a theory of a paragraph . Thus , for example , we can expect that in the worst case only one or two steps of such an iteration would be needed to find answers to wh-questions .
We denote the meaning of each sentence Si with the notation M ( Si ) . The exact form of M ( Si ) need not be discussed at this point ; it could be a conceptual dependence graph ( Schank and Abelson 1977 ) , a deep parse of Si , or some other representation . A user behavior is represented by a network , or directed graph , of such meanings .
She used an adapted experimental setup where test subjects had to assign discrete values -LCB- 0,1,2,3,4 -RCB- and word pairs were presented in isolation . This setup is also scalable to a higher number of word pairs ( 350 ) as was shown in Gurevych ( 2006 ) . Finkelstein et al. ( 2002 ) annotated a larger set of word pairs ( 353 ) , too .
Sridhar et al. ( 2009 ) obtain promising results in dialogue act tagging of the Switchboard-DAMSL corpus using lexical , syntactic and prosodic cues , while Gravano and Hirschberg ( 2009 ) examine the relation between particular acoustic and prosodic turn-yielding cues and turn taking in a large corpus of task-oriented dialogues . Louwerse et al. ( 2006 ) and Louwerse et al. ( 2007 ) study the relation between eye gaze , facial expression , pauses and dialogue structure in annotated English map-task dialogues ( Anderson et al. , 1991 ) and find correlations between the various modalities both within and across speakers . Finally , feedback expressions ( head nods and shakes ) are successfully predicted from speech , prosody and eye gaze in interaction with Embodied Communication Agents as well as human communication ( Fujie et al. , 2004 ; Morency et al. , 2005 ; Morency et al. , 2007 ; Morency et al. , 2009 ) .
DA labeling accuracy results should be compared to a baseline ( chance ) accuracy of 35 % , the relative frequency of the most frequent DA type ( STATEMENT ) in our test set .4 5.1 Dialogue Act Classification Using Words DA classification using words is based on the observation that different DAs use distinctive word strings . It is known that certain cue words and phrases ( Hirschberg and Litman 1993 ) can serve as explicit indicators of discourse structure . Similarly , we find distinctive correlations between certain phrases and DA types .
Rather , R must be treated as a separate logical level for these syntactic reasons , and because of its function -- being a pool of possibly conflicting semantic constraints . The last point may be seen better if we look at some differences between our system and KRYPTON , which also distinguishes between an object theory and background knowledge ( cfXXX Brachman et al. 1985 ) . KRYPTON 's A-box , encoding the object theory as a set of assertions , uses standard first order logic ; the T-box contains information expressed in a frame-based language equivalent to a fragment of FOL .
We use a reordering score based on the reordering penalty from the METEOR scoring metric . Though we could have used a further downstream measure like BLEU , METEOR has also been shown to directly correlate with translation quality ( Banerjee and Lavie , 2005 ) and is simpler to measure . All reordering augmented-loss experiments are run with the same treebank data as the baseline ( the training portions of PTB , Brown , and QTB ) .
Statistics about this split ( after conversion to the CATiB dependency format ) are given in Table 1 . For all experiments reported in this section we used the syntactic dependency parser MaltParser v1 .3 ( Nivre 2003 , 2008 ; Kübler , McDonald , and Nivre 2009 ) , a transition-based parser with an input buffer and a stack , which uses SVM classifiers 10 We use the term `` dev set '' to denote a non-blind test set , used for model development ( feature selection and feature engineering ) .
To address this issue , we use a version of the PATB3 training and dev sets manually annotated with functional gender , number , and rationality ( Alkuhlani and Habash 2011 ) .18 This is the first resource providing all three features ( ElixirFm only provides functional number , and to some extent functional gender ) . We conducted experiments with gold features to assess the potential of these features , and with predicted features , obtained from training a simple maximum likelihood estimation classifier on this resource ( Alkuhlani and Habash 2012 ) .19 The first part of Table 8 shows that the RAT ( rationality ) feature is very relevant ( in gold ) , but suffers from low accuracy ( no gains in machine-predicted input ) . The next two parts show the advantages of functional gender and number ( denoted with a FN * prefix ) over their surface-based counterparts .
In contrast , we use hand-written morphosyntactic transfer rules that focus on translating DA morphemes and lemmas to their MSA equivalents . In our previous work ( Salloum and Habash , 2011 ; Salloum and Habash , 2012 ) , we applied our approach to tokenized Arabic and our DA-MSA transfer component used feature transfer rules only . We did not use a language model to pick the best path ; instead we kept the ambiguity in the lattice and passed it to our SMT system .
Python is very easy to learn , read and write , and allows commands to be entered interactively into the interpreter , making it ideal for experimentation . It has already been used to implement a framework for teaching NLP ( Loper and Bird , 2002 ) . Using the Boost.Python C++ library ( Abrahams , 2003 ) , it is possible to reflect most of the components directly into Python with a minimal amount of coding .
In recent years , tree-based translation models1 are drawing more and more attention in the community of statistical machine translation ( SMT ) . Due to their remarkable ability to incorporate context structure information and long distance reordering into the translation process , tree-based translation models have shown promising progress in improving translation quality ( Liu et al. , 2006 , 2009 ; Quirk et al. , 2005 ; Galley et al. , 2004 , 2006 ; Marcu et al. , 2006 ; Shen et al. , 2008 ; Zhang et al. , 2011b ) . However , tree-based translation models always suffer from two major challenges : 1 ) They are usually built directly from parse trees , which are generated by supervised linguistic parsers .
, `` domain circumscription '' ( cfXXX Etherington and Mercer 1987 ) , and their kin . Similarly , the notion of R + M-abduction is spiritually related to the `` abductive inference '' of Reggia ( 1985 ) , the `` diagnosis from first principles '' of Reiter ( 1987 ) , `` explainability '' of Poole ( 1988 ) , and the subset principle of Berwick ( 1986 ) . But , obviously , trying to establish precise connections for the metarules or the provability and the R + M-abduction would go much beyond the scope of an argument for the correspondence of paragraphs and models .
Now they try to collect themselves in so-called ` blogspheres ' . On the other side , wikis started as collective works where each entry is not owned by a single author e.g. Wikipedia ( 2005 ) . Now personal wiki tools are arising for brainstorming and mind mapping .
A similar inconsistency appears in feminine nominals that are not inflected using sound gender suffixes , for example , the feminine form of the masculine singular adjective S , l Âzraq + φ ( ` blue ' ) is Abj zarqA ' + φ not U , j l * * Âzraq + ah . To address this inconsistency in the correspondence between inflectional features and morphemes , and inspired by Smrž ( 2007 ) , we distinguish between two types of inflectional features : formbased ( a.k.a. surface , or illusory ) features and functional features .6 Most available Arabic NLP tools and resources model morphology using formbased ( `` surface '' ) inflectional features , and do not mark rationality ; this includes the Penn Arabic Treebank ( PATB ) ( Maamouri et al. 2004 ) , the Buckwalter morphological analyzer ( Buckwalter 2004 ) , and tools using them such as the Morphological Analysis and Disambiguation for Arabic ( MADA ) toolkit ( Habash and Rambow 2005 ; Habash , Rambow , and Roth 2012 ) . The Elixir-FM analyzer ( Smrž 2007 ) readily provides the
Jiang et al. ( 2005 ) has built a semantic role classifier exploiting the interdependence of semantic roles . It has turned the single point classification problem into the sequence labeling problem with the introduction of semantic context features .
An exception is Grefenstette et al. ( 2004 ) , who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site . Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text ( Yang and Callan , 2005 ; Purpura and Hillard , 2006 ) . Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement .
In Zaidan et al. ( 2007 ) , we introduced the `` Movie Review Polarity Dataset Enriched with Annotator Rationales . '' 8 It is based on the dataset of Pang and Lee ( 2004 ) ,9 which consists of 1000 positive and 1000 negative movie reviews , tokenized and divided into 10 folds ( F0 -- F9 ) . All our experiments use F9 as their final blind test set .
other attributes associated with genes and the products they encode . The UMLS -- the Unified Medical Language System ( UMLS ) has been developed and maintained by National Library of Medicine ( NLM ) [ 17 ] . It contains three knowledge sources : the Metathesaurus ( META ) , the SPECIALIST lexicon , and the Semantic Network .
A multitude of approaches have been proposed to solve this problem , including transformation-based learning , Maximum Entropy models , Hidden Markov models and memory-based approaches . The data used in the experiment was selected from the Penn Treebank Wall Street Journal , and is the same used by Brill and Wu ( 1998 ) . The training set contained approximately 1M words and the test set approximately 200k words .
This conflicts with the standard assumption made in HPSG that only the properties changed by a lexical rule need be mentioned . As shown in Meurers ( 1994 ) this is a well-motivated convention since it avoids splitting up lexical rules to transfer the specifications that must be preserved for different lexical entries . * The authors are listed alphabetically .
So it is not necessary now to present a formal semantics here . The reader may consult recent papers on this subject ( e.g. Moens and Steedman 1987 ; Webber 1987 ) to see what a formal interpretation of events in time might look like . Since sentences can refer to events described by other sentences , we may need also a quotation operator ; Perlis ( 1985 ) describes how first order logic can be augmented with such an operator .
For Proceedings of EACL '99 example , the ALE parser ( Carpenter and Penn , 1994 ) presupposes a phrase structure backbone which can be used to determine whether a constraint is to be interpreted bottom-up or topdown . In the case of selective magic parsing we use so-called parse types which allow the user to specify how constraints in the grammar are to be interpreted .
The first as our goal is to investigate the role of SC knowlknowledge source ( KS ) is semantic class agreement edge in coreference resolution ; and ( 2 ) we do not ( SCA ) . Following Soon et al. ( 2001 ) , we represent use the ACE training data for acquiring our SC clasSCA as a binary value that indicates whether the insifier ; instead , we use the BBN Entity Type Corpus duced SCs of the two NPs involved are the same or ( Weischedel and Brunstein , 2005 ) , which consists of not . The second KS is mention , which is represented all the Penn Treebank Wall Street Journal articles as a binary value that indicates whether an NP bewith the ACE mentions manually identified and anlongs to one of the five ACE SCs mentioned above .
These lexicons were gathered from US Census data , Wikipedia category pages , and Wikipedia redirects ( and will be made publicly available upon publication ) . Following Ratinov and Roth ( 2009 ) , we also compare the performance of our system with a system using features based on the Brown clusters of the word types in a document . Since , as seen in section 2.1 , Brown clusters are hierarchical , we use features corresponding to prefixes of the path from the root to the leaf for each word type .
In Smadja 's collocation algorithm Xtract , the lowest-frequency words are effectively discarded as well ( Smadja 1993 ) . Church and Hanks ( 1990 ) use mutual information to identify collocations , a method they claim is reasonably effective for words with a frequency of not less than five . A frequency threshold of five seems quite low .
Misleading similarities in graphical form can sometime induce translation mistakes ( deceptive cognates ) ) These forbidden pairs normally involve only one of several possible parts of speech , hence the need to disambiguate them . We do this with a first-order HMM part-ofspeech tagger ( Merialdo [ 13 ] ) . I In the rest of the paper , we will use deceptive cognate very losely often to refer to normative usage of word in general .
Mapping it back into the entropy definition from equation ( 3 ) , we derive the tree entropy of w as follows : Using the bottom-up , dynamic programming technique ( see the appendix for details ) of computing inside probabilities ( Lari and Young 1990 ) , we can efficiently compute the probability of the sentence , P ( w | G ) . Similarly , the algorithm can be modified to compute the quantity E P ( v | G ) lg ( P ( v | G ) ) .
See , among others , ( Ramakrishnan et al. 1992 ) . As shown in ( Minnen , 1996 ) • The presented research was carried out at the University of Tubingen , Germany , as part of the Sonderforschungsbereich 340 . I A more detailed discussion of various aspects of the proposed parser can be found in ( Minnen , 1998 ) .
These chunks are sometimes called `` episodes , '' and sometimes `` paragraphs . '' According to Hinds ( 1979 ) , paragraphs are made up of segments , which in turn are made up of sentences or clauses , which in turn are made up of phrases . Paragraphs therefore give hierarchical structure to sentences .