JOINT CONTEXTUAL MODELING FOR ASR CORRECTION and LANGUAGE UNDERSTANDING
Table 1: SLU results. All the “J" models perform LU in joint manner, while for the other models (C), output of the ranker or correction module is fed into a separate BiLSTM-CRF for tagging and dialogue act detection. The ASR correction performance is masked to ensure fair comparison for WCN-Word-Generation-Head-MultiHeads-Attention.
['Experiments', 'WER', 'SER', 'DA-Acc', 'Slot-F1', 'TER', 'FER']
Even SLM trained and GPT-LM fine-tuned on 11k training utterances perform significantly better than the 1-best on ASR metrics. However this does not translate into improvement in the LU metrics. In fact, the output reranked using SLM does worse on the LU metrics. This indicates that just reducing WER and SER doesn’t lead to improvement in LU. The Hier-CNN-RNN Ranker model achieves 14% lower WER while also improving the LU metrics (5.2% reduction in FER). The GPT based discriminatory ranker also improves both ASR (13% reduction in WER) and LU (10% reduction in FER). This indicates that training a discriminatory ranker which identifies the oracle would do better than training a task-specific Language Model. Some of the models even out-perform the oracle on DA-Acc (>2% absolute improvement) because the Dialogue Act prediction head uses an encoding of all hypotheses (including oracle).