Learning Features that Predict Cue Usage

Learning Features that Predict Cue Usage Barbara Di Eugenio" Johanna D. Moore t Massimo Paolucci "+ University of Pittsburgh Pittsburgh, PA 15260, USA {dieugeni, jmoore ,paolucci}@cs .pitt. edu Abstract Our goal is to identify the features that pre- dict the occurrence and placement of dis- course cues in tutorial explanations in or- der to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on in- tuition or small numbers of constructed ex- amples. We apply a machine learning pro- gram, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previ- ously thought to affect cue usage. Our ex- periments enable us to identify the features with most predictive power, and show that machine learning can be used to induce de- cision trees useful for text generation. 1 Introduction Discourse cues are words or phrases, such as because, first, and although, that mark structural and seman- tic relationships between discourse entities. They play a crucial role in many discourse processing tasks, including plan recognition (Litman and Allen, 1987), text comprehension (Cohen, 1984; Hobbs, 1985; Mann and Thompson, 1986; Reichman-Adar, 1984), and anaphora resolution (Grosz and Sidner, 1986). Moreover, research in reading comprehension indicates that felicitous use of cues improves compre- hension and recall (Goldman, 1988), but that their indiscriminate use may have detrimental effects on recall (Millis, Graesser, and Haberlandt, 1993). Our goal is to identify general strategies for cue us- age that can be implemented for automatic text gen- eration. From the generation perspective, cue usage consists of three distinct, but interrelated problems: (1) occurrence: whether or not to include a cue in the generated text, (2) placement: where the cue should be placed in the text, and (3) selection: what lexical item(s) should be used. Prior work in text generation has focused on cue selection (McKeown and Elhadad, 1991; Elhadad and McKeown, 1990), or on the relation between *Learning Research & Development Center tComputer Science Department, and Learning Re- search ~z Development Center tlntelllgent Systems Program cue occurrence and placement and specific rhetori- cal structures (RSsner and Stede, 1992; Scott and de Souza, 1990; Vander Linden and Martin, 1995). Other hypotheses about cue usage derive from work on discourse coherence and structure. Previous research (Hobbs, 1985; Grosz and Sidner, 1986; Schiffrin, 1987; Mann and Thompson, 1988; Elhadad and McKeown, 1990), which has been largely de- scriptive, suggests factors such as structural features of the discourse (e.g., level of embedding and segment complexity), intentional and informational relations in that structure, ordering of relata, and syntactic form of discourse constituents. Moser and Moore (1995; 1997) coded a corpus of naturally occurring tutorial explanations for the range of features identified in prior work. Because they were also interested in the contrast between oc- currence and non-occurrence of cues, they exhaus- tively coded for all of the factors thought to con- tribute to cue usage in all of the text. From their study, …

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