Towards Developing Generation Algorithms for Text-to-Text Applications

Proceedings of the 43rd Annual Meeting of the ACL, pages 66–74, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Towards Developing Generation Algorithms for Text-to-Text Applications Radu Soricut and Daniel Marcu Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 � radu, marcu � @isi.edu Abstract We describe a new sentence realization framework for text-to-text applications. This framework uses IDL-expressions as a representation formalism, and a gener- ation mechanism based on algorithms for intersecting IDL-expressions with proba- bilistic language models. We present both theoretical and empirical results concern- ing the correctness and efficiency of these algorithms. 1 Introduction Many of today’s most popular natural language ap- plications – Machine Translation, Summarization, Question Answering – are text-to-text applications. That is, they produce textual outputs from inputs that are also textual. Because these applications need to produce well-formed text, it would appear nat- ural that they are the favorite testbed for generic generation components developed within the Natu- ral Language Generation (NLG) community. Over the years, several proposals of generic NLG systems have been made: Penman (Matthiessen and Bate- man, 1991), FUF (Elhadad, 1991), Nitrogen (Knight and Hatzivassiloglou, 1995), Fergus (Bangalore and Rambow, 2000), HALogen (Langkilde-Geary, 2002), Amalgam (Corston-Oliver et al., 2002), etc. Instead of relying on such generic NLG systems, however, most of the current text-to-text applica- tions use other means to address the generation need. In Machine Translation, for example, sentences are produced using application-specific “decoders”, in- spired by work on speech recognition (Brown et al., 1993), whereas in Summarization, summaries are produced as either extracts or using task-specific strategies (Barzilay, 2003). The main reason for which text-to-text applications do not usually in- volve generic NLG systems is that such applica- tions do not have access to the kind of informa- tion that the input representation formalisms of cur- rent NLG systems require. A machine translation or summarization system does not usually have access to deep subject-verb or verb-object relations (such as ACTOR, AGENT, PATIENT, POSSESSOR, etc.) as needed by Penman or FUF, or even shallower syntactic relations (such as subject, object, premod, etc.) as needed by HALogen. In this paper, following the recent proposal made by Nederhof and Satta (2004), we argue for the use of IDL-expressions as an application- independent, information-slim representation lan- guage for text-to-text natural language generation. IDL-expressions are created from strings using four operators: concatenation ( � ), interleave ( � ), disjunc- tion ( � ), and lock ( � ). We claim that the IDL formalism is appropriate for text-to-text generation, as it encodes meaning only via words and phrases, combined using a set of formally defined operators. Appropriate words and phrases can be, and usually are, produced by the applications mentioned above. The IDL operators have been specifically designed to handle natural constraints such as word choice and precedence, constructions such as phrasal com- bination, and underspecifications such as free word order. 66 CFGs via intersection with Deterministic Non−deterministic via intersection with probabilistic …

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