Papers with utterance representation
N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses (2021.acl-short)
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| Challenge: | Spoken Language Understanding systems parse spoken utterances into semantic structures like dialog acts and slots. |
| Approach: | They propose to use concatenated N-best ASR alternatives to represent utterances . they propose to employ a simpler utteration representation with no special delimiter . |
| Outcome: | The proposed model outperforms the prior state-of-the-art model on DSTC2 dataset. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)
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| Challenge: | Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. |
| Approach: | They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation. |
| Outcome: | The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches. |