REFERRING EXPRESSIONS FOR AN UNKNOWN AUDIENCE

One of the constraints that speakers have to take into account when producing a referring expression is their addressee’s knowledge. For example, a speaker can refer to Hemingway as “the writer who was born on 21st July 1899 in Oak Park, Illinois” or “the writer who wrote Farewell to Arms. ” The assumption that hearers will recognise the properties mentioned in a description is implicit in computational referring expression generation (REG). This article relaxes this assumption. We use the term an unknown audience for the situation when the speaker does not know what properties are known to the hearer. This problem occurs, for example, when the speaker does not know who the hearer is, as often happens when we write. We created two algorithms that use corpus-based statistics and the counts of properties in the knowledge base to select properties that are likely to be known and associated with the referent. The algorithms are based on three heuristics: Knowledge Heuristic Inspired by Clark and Marshall’s notion of communal common ground (Clark and Marshall, 1981), the heuristic uses a corpus of documents as an evidence of knowledge. The heuristic uses a collocation metric to rank the properties of a referent, assuming that more frequent properties are more likely to be known. Interestingness Heuristic This heuristic attempts to select properties that are likely to be distinguishing. The assumption of the heuristic is that infrequent properties, which are more distinguishing than frequent properties, are also interesting. The heuristic is based on a measure of interestingness used in knowledge discovery from knowledge bases (Geng and Hamilton, 2006). Termination Heuristic Because the algorithm does not know what the hearer knows, it cannot determine what entities to distinguish the referent from. As such, the traditional approach of terminating the algorithm when all distractors are ruled out does not work very well. We created a document retrieval-based heuristic that uses the counts of documents containing the created description to decide when enough properties has been included in a description. Algorithm The algorithm assigns a score to each property of a referent based on the scores of the interestingness and knowledge heuristic. The properties are then ordered from the highest scoring property to the lowest scoring property. Two versions of the algorithm were implemented. Version A1 uses the termination heuristic based on document retrieval and version A2 creates a description by taking the top 3 properties (an average number of properties observed in a corpus). Because it is not always clear how well human speakers perform in a task (e.g., Keysar and Henly, 2002), the evaluation focuses on the hearer. The evaluation used between-subject design so that each participant viewed the output of each algorithm. The main measure was the percentage of hearers who correctly identified the described referent. We compared the descriptions produced by the algorithms A1 and A2 against descriptions created by the frequently used IA (Dale and Reiter, 1995) and two versions of human authored descriptions. The knowledge base used by the algorithms was DBpedia, an ontology extracted from Wikipedia. Each entity has a property description, which contains a short description of the entity in natural language. We also collected descriptions produced by a native English speaker specifically