We describe a syntax-based algorithm that automatically builds Finite State Automata ( word lattices ) from semantically equivalent translation sets . These FSAs are good representations of paraphrases . They can be used to extract lexical and syntactic paraphrase pairs and to generate new , unseen sentences that express the same meaning as the sentences in the input sets . Our FSAs can also predict the correctness of alternative semantic renderings , which may be used to evaluate the quality of translations . In the past , paraphrases have come under the scrutiny of many research communities . Information retrieval researchers have used paraphrasing techniques for query reformulation in order to increase the recall of information retrieval engines ( Sparck Jones and Tait , 1984 ) . Natural language generation researchers have used paraphrasing to increase the expressive power of generation systems ( Iordanskaja et al . , 1991 ; Lenke , 1994 ; Stede , 1999 ) . And researchers in multi-document text summarization ( Barzilay et al . , 1999 ) , information extraction ( Shinyama et al . , 2002 ) , and question answering ( Lin and Pantel , 2001 ; Hermjakob et al . , 2002 ) have focused on identifying and exploiting paraphrases in the context of recognizing redundancies , alternative formulations of the same meaning , and improving the performance of question answering systems . In previous work ( Barzilay and McKeown , 2001 ; Lin and Pantel , 2001 ; Shinyama et al . , 2002 ) , paraphrases are represented as sets or pairs of semantically equivalent words , phrases , and patterns . Although this is adequate in the context of some applications , it is clearly too weak from a generative perspective . Assume , for example , that we know that text pairs ( stock market rose , stock market gained ) and ( stock market rose , stock prices rose ) have the same meaning . If we memorized only these two pairs , it would be impossible to infer that , in fact , consistent with our intuition , any of the following sets of phrases are also semantically equivalent : { stock market rose , stock market gained , stock prices rose , stock prices gained } and { stock market , stock prices } in the context of rose or gained ; { market rose } , { market gained } , { prices rose } and { prices gained } in the context of stock ; and so on . In this paper , we propose solutions for two problems : the problem of paraphrase representation and the problem of paraphrase induction . We propose a new , finite-statebased representation of paraphrases that enables one to encode compactly large numbers of paraphrases . We also propose algorithms that automatically derive such representations from inputs that are now routinely released in conjunction with large scale machine translation evaluations ( DARPA , 2002 ) : multiple English translations of many foreign language texts . For instance , when given as input the 11 semantically equivalent English translations in Figure 1 , our algorithm automatically induces the FSA in Figure 2 , which represents compactly 49 distinct renderings of the same semantic meaning . Our FSAs capture both lexical paraphrases , such as { fighting , bat-tle } , { died , were killed } and structural paraphrases such as { last week 's fighting , the battle of last week } . The contexts in which these are correct paraphrases are also conveniently captured in the representation . In previous work , Langkilde and Knight ( 1998 ) used word lattices for language generation , but their method involved hand-crafted rules . Bangalore et al . ( 2001 ) and Barzilay and Lee ( 2002 ) both applied the technique of multi-sequence alignment ( MSA ) to align parallel corpora and produced similar FSAs . For their purposes , they mainly need to ensure the correctness of consensus among different translations , so that different constituent orderings in input sentences do not pose a serious prob- lem . In contrast , we want to ensure the correctness of all paths represented by the FSAs , and direct application of MSA in the presence of different constituent orderings can be problematic . For example , when given as input the same sentences in Figure 1 , one instantiation of the MSA algorithm produces the FSA in Figure 3 , which contains many " bad " paths such as the battle of last week 's fighting took at least 12 people lost their people died in the fighting last week 's fighting ( See Section 4.2.2 for a more quantitative analysis . ) . It 's still possible to use MSA if , for example , the input is pre-clustered to have the same constituent ordering ( Barzilay and Lee ( 2003 ) ) . But we chose to approach this problem from another direction . As a result , we propose a new syntax-based algorithm to produce FSAs . In this paper , we first introduce the multiple translation corpus that we use in our experiments ( see Section 2 ) . We then present the algorithms that we developed to induce finite-state paraphrase representations from such data ( see Section 3 ) . An important part of the paper is dedicated to evaluating the quality of the finite-state representations that we derive ( see Section 4 ) . Since our representations encode thousands and sometimes millions of equivalent verbalizations of the same meaning , we use both manual and automatic evaluation techniques . Some of the automatic evaluations we perform are novel as well . In this paper , we presented a new syntax-based algorithm that learns paraphrases from a newly available dataset . The multiple translation corpus that we use in this paper is the first instance in a series of similar corpora that are built and made publicly available by LDC in the context of a series of DARPA-sponsored MT evaluations . The algorithm we proposed constructs finite state representations of paraphrases that are useful in many contexts : to induce large lists of lexical and structural paraphrases ; to generate semantically equivalent renderings of a given meaning ; and to estimate the quality of machine translation systems . More experiments need to be carried out in order to assess extrinsically whether the FSAs we produce can be used to yield higher agreement scores between human and automatic assessments of translation quality . In our future work , we wish to experiment with more flexible merging algorithms and to integrate better the top-down and bottom-up processes that are used to in-duce FSAs . We also wish to extract more abstract paraphrase patterns from the current representation . Such patterns are more likely to get reused -which would help us get reliable statistics for them in the extraction phase , and also have a better chance of being applicable to unseen data .
Existing approaches to represent paraphrases as sets or pairs of semantically equivalent words, phrases and patterns that are weak for text generation purposes.
They propose a syntax-based algorithm that builds Finite State Automata from translation sets which are good representations of paraphrases.
Manual and automatic evaluations show that the representations extracted by the proposed method can be used for automatic translation evaluations.