Papers by Paria Jamshid Lou
Neural Constituency Parsing of Speech Transcripts (N19-1)
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| Challenge: | a neural parser for transcribed speech can find EDITED disfluency nodes . this makes specialized mechanisms for parsing disfluencies unnecessary . |
| Approach: | They propose a neural self-attentive parser that finds EDITED disfluency nodes in transcribed speech. |
| Outcome: | The proposed parser finds EDITED disfluency nodes with an accuracy surpassing that of specialized systems. |
Improving Disfluency Detection by Self-Training a Self-Attentive Model (2020.acl-main)
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| Challenge: | Existing self-attentive parsers using contextualized word embeddings produce state-of-the-art results in joint parsing and disfluency detection. |
| Approach: | They propose to use contextualized word embeddings to train a neural model using unlabeled data to train parsers. |
| Outcome: | The proposed method produces state-of-the-art results in parsing and disfluency detection in speech transcripts. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
End-to-End Speech Recognition and Disfluency Removal (2020.findings-emnlp)
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| Challenge: | Disfluency detection is usually an intermediate step between an automatic speech recognition system and a downstream task. |
| Approach: | They propose to train models to directly map disfluent speech into fluent transcripts without relying on a separate disfluency detection model. |
| Outcome: | The proposed models learn to generate fluent transcripts, but their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. |