We introduce the task of incremental semantic role labeling ( iSRL ) , in which semantic roles are assigned to incomplete input ( sentence prefixes ) . iSRL is the semantic equivalent of incremental parsing , and is useful for language modeling , sentence completion , machine translation , and psycholinguistic modeling . We propose an iSRL system that combines an incremental TAG parser with a semantically enriched lexicon , a role propagation algorithm , and a cascade of classifiers . Our approach achieves an SRL Fscore of 78.38 % on the standard CoNLL 2009 dataset . It substantially outperforms a strong baseline that combines gold-standard syntactic dependencies with heuristic role assignment , as well as a baseline based on Nivre 's incremental dependency parser . Humans are able to assign semantic roles such as agent , patient , and theme to an incoming sentence before it is complete , i.e. , they incrementally build up a partial semantic representation of a sentence prefix . As an example , consider : ( 1 ) The athlete realized [ her goals ] PATIENT / THEME were out of reach . When reaching the noun phrase her goals , the human language processor is faced with a semantic role ambiguity : her goals can either be the PA-TIENT of the verb realize , or it can be the THEME of a subsequent verb that has not been encountered yet . Experimental evidence shows that the human language processor initially prefers the PA-TIENT role , but switches its preference to the theme role when it reaches the subordinate verb were . Such semantic garden paths occur because human language processing occurs word-by-word , and are well attested in the psycholinguistic literature ( e.g. , Pickering et al . , 2000 ) . Computational systems for performing semantic role labeling ( SRL ) , on the other hand , proceed non-incrementally . They require the whole sentence ( typically together with its complete syntactic structure ) as input and assign all semantic roles at once . The reason for this is that most features used by current SRL systems are defined globally , and can not be computed on sentence prefixes . In this paper , we propose incremental SRL ( iSRL ) as a new computational task that mimics human semantic role assignment . The aim of an iSRL system is to determine semantic roles while the input unfolds : given a sentence prefix and its partial syntactic structure ( typically generated by an incremental parser ) , we need to ( a ) identify which words in the input participate in the semantic roles as arguments and predicates ( the task of role identification ) , and ( b ) assign correct semantic labels to these predicate / argument pairs ( the task of role labeling ) . Performing these two tasks incrementally is substantially harder than doing it non-incrementally , as the processor needs to commit to a role assignment on the basis of incomplete syntactic and semantic information . As an example , take ( 1 ): on reaching athlete , the processor should assign this word the AGENT role , even though it has not seen the corresponding predicate yet . Similarly , upon reaching realized , the processor can complete the AGENT role , but it should also predict that this verb also has a PATIENT role , even though it has not yet encountered the argument that fills this role . A system that performs SRL in a fully incremental fashion therefore needs to be able to assign incomplete semantic roles , unlike existing full-sentence SRL models . The uses of incremental SRL mirror the applications of incremental parsing : iSRL models can be used in language modeling to assign better string probabilities , in sentence completion systems to provide semantically informed completions , in any real time application systems , such as dialog processing , and to incrementalize applications such as machine translation ( e.g. , in speech-tospeech MT ) . Crucially , any comprehensive model of human language understanding needs to combine an incremental parser with an incremental semantic processor ( Padó et al . , 2009 ; Keller , 2010 ) . The present work takes inspiration from the psycholinguistic modeling literature by proposing an iSRL system that is built on top of a cognitively motivated incremental parser , viz . , the Psycholinguistically Motivated Tree Adjoining Grammar parser of Demberg et al . ( 2013 ) . This parser includes a predictive component , i.e. , it predicts syntactic structure for upcoming input during incremental processing . This makes PLTAG particularly suitable for iSRL , allowing it to predict incomplete semantic roles as the input string unfolds . Competing approaches , such as iSRL based on an incremental dependency parser , do not share this advantage , as we will discuss in Section 4.3 . In this paper , we introduced the new task of incremental semantic role labeling and proposed a system that solves this task by combining an incremental TAG parser with a semantically enriched lexicon , a role propagation algorithm , and a cascade of classifiers . This system achieved a fullsentence SRL F-score of 78.38 % on the standard CoNLL dataset . Not only is the full-sentence score considerably higher than the Majority-Baseline ( which is a strong baseline , as it uses gold-standard syntactic dependencies ) , but we also observe that our iSRL system performs well incrementally , i.e. , it predicts both complete and incomplete semantic role triples correctly early on in the sentence . We attributed this to the fact that our TAG-based architecture makes it possible to predict upcoming syntactic structure together with the corresponding semantic roles .

Challenge: While humans can assign semantic roles to incomplete sentences as they unfold, current systems require whole sentences and process non-incrementally because of the global features.
Approach: They propose the incremental semantic role labelling task and an incremental TAG parser with a lexicon, a role propagation algorithm, and a cascade of classifiers.
Outcome: The proposed system outperforms a baseline with gold-standard syntactic dependencies on the CoNLL 2009 dataset and successfully predicts complete and incomplete semantic role triples.