hrithikpiyush/acl-arc huggingface dataset:
Another technique for making better use of unlabeled data is cotraining ( Blum and Mitchell 1998 ) , in which two sufficiently different learners help each other learn by labeling training data for one another . The work of Sarkar ( 2001 ) and Steedman , Osborne , et al. ( 2003 ) suggests that co-training can be helpful for statistical parsing . Pierce and Cardie ( 2001 ) have shown , in the context of base noun identification , that combining sample selection and cotraining can be an effective learning framework for large-scale training .
In Table 2 , lem refers to the LTAG parser ( Sarkar et al. , 2000 ) , ANSI C implementation of the two-phase parsing algorithm that performs the head corner parsing ( van Noord , 1994 ) without features ( phase 1 ) , and then executes feature unification ( phase 2 ) . TNT refers to the HPSG parser ( Torisawa et al. , 2000 ) , C++ implementation of the two-phase parsing algorithm that performs filtering with a compiled CFG ( phase 1 ) and then executes feature unification ( phase 2 ) . Table 2 clearly shows that the HPSG parser is significantly faster than the LTAG parser .
Each matching plan 's preconditions are checked ; if they are currently ( believed to be ) true , the planner then attempts to find all instantiations of the plan 's body . 1 ° The body of a plan can be an action or sequence of actions , a goal or sequence 9 Moore and Paris also note that `` a generation system must maintain the kinds of information outlined by Grosz and Sidner '' ( Moore and Paris 1989 , 203 ) . Their planner uses plan structures similar to IGEN 's , except that the plan operators they use are generally instantiations of rhetorical relations drawn from Rhetorical Structure Theory ( Mann and Thompson 1987 ) .
( Cut-free ) proof nets exhibit no spurious ambiguity and play the role in categorial grammar that parse trees play in phrase structure grammar . Surveys and articles on the topic include Lamarche and Retord ( 1996 ) , de Groote and Retord ( 1996 ) , and Morrill ( 1999 ) . Still , at the risk of proceeding at a slightly slower pace , we aim nonetheless to include here enough details to make the present paper self-contained .
The corpus and guidelines will also be shared with the research community . Another line of research that is correlated with ours is recognition of agreement/disagreement ( Misra and Walker , 2013 ; Yin et al. , 2012 ; Abbott et al. , 2011 ; Andreas et al. , 2012 ; Galley et al. , 2004 ; Hillard et al. , 2003 ) and classification of stances ( Walker et al. , 2012 ; Somasundaran and Wiebe , 2010 ) in online forums . For future work , we can utilize textual features ( contextual , dependency , discourse markers ) , relevant multiword expressions and topic modeling ( Mukherjee and Liu , 2013 ) , and thread structure ( Murakami and Raymond , 2010 ; Agrawal et al. , 2003 ) to improve the Agree/Disagree classification accuracy .
Most probability estimation methods require that there be a finite set of features on which the probability is conditioned . The standard way to handle this problem is to handcraft a finite set of features which provides a sufficient summary of the unbounded history ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2000 ) . The probabilities are then assumed to be independent of all the infoimation about the history which is not captured by the chosen features .
Typical tags include Agent , Patient , Source , etc. and some adjuncts such as Temporal , Manner , Extent , etc. . Since the arguments can provide useful semantic information , the SRL is crucial to many natural language processing tasks , such as Question and Answering ( Narayanan and Harabagiu 2004 ) , Information Extraction ( Surdeanu et al. 2003 ) , and Machine Translation ( Boas 2002 ) . With the efforts of many researchers ( Carreras and Màrquez 2004 , 2005 , Moschitti 2004 , Pradhan et al 2005 , Zhang et al 2007 ) , different machine learning methods and linguistics resources are applied in this task , which has made SRL task progress fast .
In current work on semantic relation analysis , the focus is on semantic roles -- relations between verbs and their arguments . Most approaches rely on VerbNet ( Kipper et al. , 2000 ) and FrameNet ( Baker et al. , 1998 ) to provide associations between verbs and semantic roles , that are then mapped onto the current instance , as shown by the systems competing in semantic role labelling competitions ( Carreras and Marquez , 2004 ; Carreras and Marquez , 2005 ) and also ( Gildea and Jurafsky , 2002 ; Pradhan et al. , 2005 ; Shi and Mihalcea , 2005 ) . These systems share two ideas which make them different from the approach presented here : they all analyse verb-argument relations , and they all use machine learning or probabilistic approaches ( Pradhan et al. , 2005 ) to assign a label to a new instance .
In previous work ( Bachenko et al. 1986 ) , we described an experimental text-to-speech system that determined prosodic phrasing for the Olive -- Liberman synthesizer ( Olive and Liberman 1985 ) . The system generated phrase boundaries using information derived from the syntactic structure of a sentence .
In order to calculate a ranking for each TL sentence produced , we multiply the weights of each chunk used in its construction . Note that this ensures that greater importance is attributed to longer chunks , as is usual in most EBMT systems ( cfXXX Sato and Nagao 1990 ; Veale and Way 1997 ; Carl 1999 ) .7 As an example , consider the translation into French of the house collapsed . Assume the conditional probabilities in ( 33 ) :
Another common approach to lexical rules is to encode them as unary phrase structure rules . This approach is taken , for example , in LKB ( Copestake 1992 ) where lexical rules are introduced on a par with phrase structure rules and the parser makes no distinction between lexical and nonlexical rules ( Copestake 1993 , 31 ) . A similar method is included in PATR-II ( Shieber et al. 1983 ) and can be used to encode lexical rules as binary relations in the CUF system ( Dorre and Eisele 1991 ; Done and Dorna 1993b ) or the TFS system ( Emele and Zajac 1990 ; Emele 1994 ) .
The way these predicates interconnect is represented in Figure 19 . 27 Briscoe and Copestake ( 1996 ) argue that semi-productivity of lexical rules , which can be understood as a generalization of exceptions to lexical rules , can be integrated with our approach by assigning probabilities to the automaton associated with a particular lexical entry . 28 In order to distinguish the different interaction predicates for the different classes of lexical entries , the compiler indexes the names of the interaction predicates .
The analysis of the data we have collected indicates that student satisfaction may be affected if the system rephrases student answers using different words ( for example , using better terminology ) but does n't explicitly explain the reason why different terminology is needed ( Dzikovska et al. , 2010 ) . Results from other systems show that measures of semantic coherence between a student and a system were positively associated with higher learning gain ( Ward and Litman , 2006 ) . Using a deep generator to automatically generate system feedback gives us a level of control over the output and will allow us to devise experiments to study those issues in more detail .
During the tectogrammatical parsing of Czech , the analytical tree structure is converted into the tectogrammatical one . These automatic transformations are based on linguistic rules ( Bohmova , 2001 ) . Subsequently , tectogrammatical functors are assigned by the C4 .5 classifier ( 2abokrtsk9 et al. , 2002 ) .
• Only qualitative observations of the responses were reported ( no formal evaluation was performed ) ( Lapalme and Kosseim 2003 ; Roy and Subramaniam 2006 ) . • Only an automatic evaluation was performed , which relied on having model responses ( Berger and Mittal 2000 ; Berger et al. 2000 ) . • A user study was performed , but it was either very small compared to the corpus ( Carmel , Shtalhaim , and Soffer 2000 ; Jijkoun and de Rijke 2005 ) , or the corpus itself was significantly smaller than ours ( Feng et al. 2006 ; Leuski et al. 2006 ) .
Traditional Arabic grammars often describe a very general three-way distinction into verbs , nominals , and particles . In comparison , the tag set of the Buckwalter Morphological Analyzer ( Buckwalter 2004 ) used in the PATB has a core POS set of 44 tags ( CORE44 ) before morphological extension .8 Cross-linguistically , a core set containing around 12 tags is often 7 We ignore the rare `` false idafa '' construction ( Habash 2010 , p. 102 ) .
These include the following : • use of low level knowledge from the speech recognition phase , • use of high level knowledge about the domain in particular and the dialogue task in general , • a `` continue '' facility and an `` auto-loop '' facility as described by Biermann and Krishnaswamy ( 1976 ) , • a `` conditioning '' facility as described by Fink et al. ( 1985 ) , • implementation of new types of paraphrasing , • checking a larger environment in the expectation acquisition algorithm when deciding if an incoming sentence is the same or similar to one already seen , and • examining inter-speaker dialogue patterns . All but two of these areas for expansion are aimed at moving the expectation system from one that finds patterns in a user 's dialogues and acquires historical knowledge about them to one that can acquire true procedures .
However , the phrases detected are not necessarily mentions that we need to discover . Ratinov and Roth ( 2009 ) present detailed studies on the task of named entity recognition , which discusses and compares different methods on multiple aspects including chunk representation , inference method , utility of non-local features , and integration of external knowledge . NER can be regarded as a sequential labeling problem , which can be modeled by several proposed models , e.g. Hidden Markov Model ( Rabiner , 1989 ) or Conditional Random Fields ( Sarawagi and Cohen , 2004 ) .
For English , known for its limited number of inflection patterns , lexicon-free general-purpose stem1 ` ' denotes the string concatenation operator . mers ( Lovins , 1968 ; Porter , 1980 ) demonstrably improve retrieval performance . This has been reported for other languages , too , dependent on the generality of the chosen approach ( J ¨ appinen and Niemist ¨ o , 1988 ; Choueka , 1990 ; Popovic and Willett , 1992 ; Ekmekc ¸ ioglu et al. , 1995 ; Hedlund et al. , 2001 ; Pirkola , 2001 ) .
The NP the tall fat giraffe , for example , can safely refer to an element b in a situation like the one below , where b is the only element that exceeds all distractors with respect to some dimension ( a different one for a than for c , as it happens ) while not being exceeded by any distractors in any dimension : Cases like this would be covered if the decision-theoretic property of Pareto optimality ( e.g. , Feldman 1980 ) was used as the sole criterion : Formally , an object r E C has a Pareto-optimal combination of Values V iff there is no other x E C such that 1 . ]
The grammar could parse 100 % of the training sentences and 84 % of the test sentences . A formula for the test set perplexity ( Lee 1989 ) is :13 where the wi are the sequence of all words in all sentences , N is the total number of words , including an `` end '' word after each sentence , and P ( w , I w , _ 1 , ... wi ) is the probability of the ith word given all preceding words .14 If all words are assumed equally likely , then P ( w , I w , _ 1 , ... w1 ) can be determined by counting all the words that could follow each word in the sentence , along all workable partial theories .
3.1.1 Document Retrieval ( Doc-Ret ) . This method follows a traditional Information Retrieval paradigm ( Salton and McGill 1983 ) , where a query is represented by the content terms it contains , and the system retrieves from the corpus a set of documents that best match this query . In our case , the query is a new request e-mail to be addressed by the system , and we have considered three views of the documents in the corpus : ( 1 ) previous response e-mails , ( 2 ) previous request e-mails , or ( 3 ) previous request -- response pairs .
In what follows we explain the properties of the model by applying it to a small number of adjective-noun combinations taken from the lexical semantics literature . Table 1 gives the interpretations of eight adjective-noun combinations discussed in Pustejovsky ( 1995 ) and Vendler ( 1968 ) . Table 2 shows the five most likely interpretations for these combinations as derived by the model discussed in the previous sections ( v1 is the most likely interpretation , v2 is the second most likely interpretation , etc. ) .
Additionally , we present results of the tagger on the NEGRA corpus ( Brants et al. , 1999 ) and the Penn Treebank ( Marcus et al. , 1993 ) . The Penn Treebank results reported here for the Markov model approach are at least equivalent to those reported for the Maximum Entropy approach in ( Ratnaparkhi , 1996 ) . For a comparison to other taggers , the reader is referred to ( Zavrel and Daelemans , 1999 ) .
from Penn-II and 221 from Penn-III . Briscoe and Carroll ( 1997 ) , by comparison , employ 163 distinct predefined frames .
Considerable research has been done on SP acquisition most of which has involved collecting argument headwords from data and generalizing to WordNet classes . Brockmann and Lapata ( 2003 ) have showed that WordNet-based approaches do not always outperform simple frequency-based models , and a number of techniques have been recently proposed which may offer ideas for refining our current unsupervised approach ( Erk , 2007 ; Bergsma et al. , 2008 ) . The number and type ( and combination ) of GRs for which SPs can be reliably acquired , especially when the data is sparse , requires also further investigation .
10Traditionally log ( strength ) values are called weights , but this paper uses `` weight '' to mean something else . fθ on demand ( Mohri et al. , 1998 ) can pay off here , since only part of fθ may be needed subsequently . ) As training data we are given a set of observed ( input , output ) pairs , ( xi , yi ) .
Combinatory categorial grammar does not concern itself with the capture of all ( or only ) the concatenatively valid combinatory schemata , but rather with incrementality , for example , on a shiftreduce design . An approach ( also based on regulation of the succession of rule application ) to the associated problem of spurious ambiguity is given in Hepple and Morrill ( 1989 ) but again , to our knowledge , there is no predictive relation between incremental combinatory processing and the kind of processing phenomena cited in the introduction . 3 .
The relevant equivalence is : This equivalence is doing essentially the same job as Pereira 's pronoun abstraction schema in Pereira ( 1990 ) . It will identify a pronoun with any term of type e elsewhere in the QLF , relying on the binding conditions to prevent impossible associations .
Sophisticated phrase table combination . Finally , we experiment with a method for combining phrase tables proposed in ( Nakov and Ng , 2009 ; Nakov and Ng , 2012 ) . The first phrase table is extracted from word alignments for the balanced concatenation with repetitions , which are then truncated so that they are kept for only one copy of the Indonesian -- English bi-text .
The majority of NLG focuses on the satisfaction of a communicative goal , with examples such as Belz ( 2008 ) which produces weather reports from structured data or Mitchell et al. ( 2013 ) which generates descriptions of objects from images . Our work is more similar to NLG work that concentrates on structural constraints such as generative poetry ( Greene et al. , 2010 ) ( Colton et al. , 2012 ) ( Jiang and Zhou , 2008 ) or song lyrics ( Wu et al. , 2013 ) ( Ramakrishnan A et al. , 2009 ) , where specified meter or rhyme schemes are enforced . In these papers soft semantic goals are sometimes also introduced that seek responses to previous lines of poetry or lyric .
We see that matching on requests yields more retrieved documents than matching on responses , and that matching on request -- response pairs yields even more retrieved documents . For the cases where retrieval took place , we used F-score ( van Rijsbergen 1979 ; Salton and McGill 1983 ) to determine the similarity between the response from the top-ranked document and the real response ( the formulas for F-score and its contributing factors , recall and precision , appear in Section 4.2 ) . The third column in Table 1 shows the proportion of requests for which this similarity is non-zero .
These phrases were then translated automatically by three on-line MT systems . These translations gave rise to a number of automatically constructed linguistic resources : ( 1 ) the original ( source , target ) phrasal translation pairs , ( 2 ) the marker lexicon , ( 3 ) the gen11 Thanks are due to one of the anonymous reviewers for pointing out that our wEBMT system , seeded with input from multiple translation systems , with a postvalidation process via the Web ( amounting to an n-gram target language model ) , in effect forms a multiengine MT system as described by Frederking and Nirenburg ( 1994 ) , Frederking et al. ( 1994 ) , and Hogan and Frederking ( 1998 ) . eralized lexicon , and ( 4 ) the word-level lexicon .
There have been a number of efforts to exploit weak or external signals of quality to train better prediction models . This includes work on generalized expectation ( Mann and McCallum , 2010 ) , posterior regularization ( Ganchev et al. , 2010 ) and constraint driven learning ( Chang et al. , 2007 ; Chang et al. , 2010 ) . The work of Chang et al. ( 2007 ) on constraint driven learning is perhaps the closest to our framework and we draw connections to it in Section 5 .
We have a list of 47 relations that manifest themselves in compound clauses , inside a simple clause or in noun phrases . The list , a synthesis of a number of relation lists cited in the literature , has been designed to be general , domainindependent ( Barker et al. , 1997a ) . Section 2 overviews research in semantic relation analysis .
The algorithm handles `` like '' coordination and exploits the traces used in the treebank in order to interpret LDDs . Unlike our approach , those of Xia ( 1999 ) and Hockenmaier , Bierner , and Baldridge ( 2004 ) include a substantial initial correction and clean-up of the Penn-II trees . Miyao , Ninomiya , and Tsujii ( 2004 ) and Nakanishi , Miyao , and Tsujii ( 2004 ) describe a methodology for acquiring an English HPSG from the Penn-II Treebank .
t , and C [ s ] = T [ s ] g the samples on which the rule applies and changes the classification from correct to incorrect ; similarly , bad ( r ) = jB ( r ) j. Given a newly learned rule b that is to be applied to S , the goal is to identify the rules r for which at least one of the sets G ( r ) , B ( r ) is modified by the application of rule b. Obviously , if both sets are not modified when applying rule b , then the value of the objective function for rule r remains unchanged . 2The algorithm was implemented by the the authors , following the description in Hepple ( 2000 ) . The presentation is complicated by the fact that , in many NLP tasks , the samples are not independent .
Examples such as ¨ Ozbal et al. ( 2013 ) and Valitutti et al. ( 2013 ) use template filling techniques guided by quantified notions of humor or how catchy a phrase is . Our motivation for generation of material for language education exists in work such as Sumita et al. ( 2005 ) and Mostow and Jang ( 2012 ) , which deal with automatic generation of classic fill in the blank questions . Our work is naturally complementary to these efforts , as their methods require a corpus of in-vocab text to serve as seed sentences .
Ravin ( 1999 ) introduced a rule-based approach that tackles both variation and ambiguity analysing the structure of names . In most recent research , NEs ( person , location and organisations ) are extracted from the text and used as a source of evidence to calculate the similarity between documents - see for instance ( Blume , 2005 ; Chen and Martin , 2007 ; Popescu and Magnini , 2007 ; Kalashnikov et al. , 2007 ) . For instance , Blume ( 2005 ) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process .
IBM Models 3 , 4 , and 5 attempt to capture fertility ( the tendency of each source word to generate several target words ) , resulting in probabilistically deficient , intractable models that require local heuristic search and are difficult to implement and extend . Many researchers use the GIZA + + software package ( Och and Ney 2003 ) as a black box , selecting IBM Model 4 as a compromise between alignment quality and efficiency . All of the models are asymmetric ( switching target and source languages produces drastically different results ) and the simpler models ( IBM Models 1 , 2 , and HMM ) do not enforce bijectivity ( the majority of words translating as a single word ) .
Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead . Some works abstract perception via the usage of symbolic logic representations ( Chen et al. , 2010 ; Chen and Mooney , 2011 ; Matuszek et al. , 2012 ; Artzi and Zettlemoyer , 2013 ) , while others choose to employ concepts elicited from psycholinguistic and cognition studies . Within the latter category , the two most common representations have been association norms , where subjects are given a
Over the past decade , researchers at IBM have developed a series of increasingly sophisticated statistical models for machine translation ( Brown et al. , 1988 ; Brown et al. , 1990 ; Brown et al. , 1993a ) . However , the IBM models , which attempt to capture a broad range of translation phenomena , are computationally expensive to apply .
A realization phase , during which the choice between base , superlative , and comparative forms is made , among other things . One area of current interest concerns the left-to-right arrangement of premodifying adjectives within an NP ( e.g. , Shaw and Hatzivassiloglou 1999 ; Malouf 2000 ) . Work in this area is often based on assigning adjectives to a small number of categories ( e.g. , Precentral , Central , Postcentral , and Prehead ) , which predict adjectives ' relative position .
The arrow connecting vk and uk ± i in Figure 1 represents an indirect association , since the association between vk and uk ± i arises only by virtue of the association between each of them and uk . Models of translational equivalence that are ignorant of indirect associations have `` a tendency ... to be confused by collocates '' ( Dagan et al. , 1993 ) . Fortunately , indirect associations are usually not difficult to identify , because they tend to be weaker than the direct associations on which they are based ( Melamed , 1996c ) .
• Lexical and Dialogue Features dialogue name and turn number question name and question depth ITSPOKE-recognized lexical items in turn ITSPOKE-labeled turn ( in ) correctness incorrect runs • User Identifier Features : gender and pretest score Note that although our feature set was drawn primarily from our prior uncertainty detection experiments ( Forbes-Riley and Litman , 2011a ; Drummond and Litman , 2011 ) , we have also experimented with other features , including state-of-theart acoustic-prosodic features used in the last Interspeech Challenges ( Schuller et al. , 2010 ; Schuller et al. , 2009b ) and made freely available in the openSMILE Toolkit ( Florian et al. , 2010 ) . To date , however , these features have only decreased the crossvalidation performance of our models .8 While some of our features are tutoring-specific , these have similar counterparts in other applications ( i.e. , answer ( in ) correctness corresponds to a more general notion of `` response appropriateness '' in other domains , while pretest score corresponds to the general notion of domain expertise ) .