- C. Rosé, L. Levin. 1998. An Interactive Domain Independent Approach to Robust Dialogue Interpretation. Abstract: We discuss an interactive approach to robust, interpretation in a large scale speech-to-speech translation system. Where other interactive approaches to robust interpretation ha,re depeuded upon domain dependent repair rules, the approach described here operates etIiciently without any such hand-coded repair knowledge and yields a 37% reduction in error rate over a corpus of noisy sentences. 1 I n t r o d u c t i o n Iu this paper we discuss lt.OSE, all interactive ap1)roach to robust interpretation developed in the context of the JANUS speech-to-speech translation system (Lavie et al., 1996). Previous interactive approaches to robust interl)retation have either required excessive amounts of interaction (II.os~ and Wail)el, 1994), depended upon domain dependent repair rules (Van Noord, 1996; l)anieli and Gerbino, 1995), or relied on the rninimum distance parsing approach (Hipp, 1992; Smith, 1992; Lehman, 11989) which has been shown to be intractable in a largescale system (F/.os~ and Lavie, 1997). in contrast, the ROSE approach operates efficiently without any hand-coded repair knowledge. All empirical evaluation demonstrates the efficacy of t.his domain independent approach. A further evaluation demonstrates that the ROSE approach combines easily with available domain knowledge in order to improve the quMity of the interaction. The ROSE approach is based on a model of human comnmnication between speakers of different languages with a small shared language base. Humans who share a very small language base are able to communicate when the need arises by simplifying their speech patterns and negotiating until they manage to transmit their ideas to one another (Hatch, 1983). As the speaker is speaking, the listener "casts his net" in order to catch those fragments of speech that are comprehensible to him, which he then attempts to fit together semantically. His subsequent negotiation with the speaker builds upon this partial understanding. Similarly, ROSE repairs ext.ragranmlatical input in two phases. The first phase, Repair ltypothesis Formation, is responsible for assembling a set of hypotheses about the meaning of the ungrammatical utterance. In the second phase, hlteraetion with the User, the system generates a set of queries, negotiating with the speaker in order to narrow down to a single best meaning representation hypothesis. This approach was evaluated in the context of the JANUS multi-lingual machine translation system. First, the system obtains a meaning representation for a sentence uttered in the source language. Then the resulting meaning representation structure is mapped onto a sentence in the target language using GENKIT (Tomita and Nyberg, 1988) with a sentence level generation grammar. Currently, the translation system deals with tlle scheduling domain where two speakers at tempt to schedule a meeting together over the l)hone. This paper focuses on the Interaction phase. Details about the llypothesis Formation phase are found ill (Rosd, 1997). 2 I n t e r a c t i v e R e p a i r In D e p t h As mentioned above, ROSE repairs extragrammatical input ill two phases. The first phase, t{epair ttypothesis Fornmtion, is responsible for assembling a ranked set of tell or fewer hypotheses about the meaning of the ungrammatical utterance expressed in the source language. This phase is itself divided into two stages, Partial Parsing and Combination. The Partial Parsing stag(" is similar to the concept of the listener "casting his net" for comprehensible fi'agments of speech. A robust skipping parser (Lavie, 1995) is used to obtain an analysis for islands of the speaker's sentence. Ill the Combination stage, the fi'agments fi'om the partial parse are assembled into a ranked set of alternative nmaning representation hypotheses. A genetic programming (Koza, 1992; Koza, 1994) approach is used to search for dill>rent ways to combine the fi'agments in order to avoid requiring any hand-crafted repair rules. Our genetic programming approach has been shown previously to be orders of magnitude more efficient than the mininmm distance parsing approach (Rosd and Lavie, 1997). In the second phase, Interaction with
