Papers with Scheduled Sampling
Dynamic Oracle for Neural Machine Translation in Decoding Phase (L18-1)
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| Challenge: | Existing methods to improve NMT performance but there is a discrepancy between training and inference when decoding. |
| Approach: | They propose to use Scheduled Sampling to reduce the discrepancy between training and inference in NMT when decoding to mitigate the discrépancy. |
| Outcome: | The proposed methods improve translation quality over standard NMT system. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
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| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
| Approach: | They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives. |
| Outcome: | The proposed model outperforms baselines on three datasets and is highly accurate. |
Understanding and Bridging the Modality Gap for Speech Translation (2023.acl-long)
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| Challenge: | Existing methods to improve end-to-end speech translation (ST) use multitask learning, but there is always a modality gap between ST and MT due to the differences between speech and text. |
| Approach: | They propose a method to bridge the modality gap between ST and MT by leveraging (text) machine translation data. |
| Outcome: | The proposed method bridges the modality gap and achieves significant improvements over baseline in all eight directions. |