| other,11-1-E06-1035,ak | problem of automatically predicting <term> | segment boundaries | </term> in <term> spoken multiparty dialogue | #11410 In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue. | |
| other,67-5-E06-1035,ak | conversational cues </term> , such as <term> | cue phrases | </term> and <term> overlapping speech </term> | #11530 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | |
| other,5-6-E06-1035,ak | task </term> . We also find that the <term> | transcription errors | </term> inevitable in <term> ASR output </term> | #11550 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks. |
