Papers with word discovery
Tied Multitask Learning for Neural Speech Translation (N18-1)
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| Challenge: | Recent efforts in endangered language documentation focus on collecting spoken language resources . BULB project uses mobile app to collect spoken resources accompanied by spoken translations . |
| Approach: | They propose a model where the second task decoder receives information from the first task . they apply regularization that encourages transitivity and invertibility to the model . |
| Outcome: | The proposed model improves performance on low-resource speech transcription and translation tasks. |
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference (2022.acl-long)
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| Challenge: | No existing methods can achieve effective text segmentation and word discovery in open domain Chinese texts. |
| Approach: | They propose a Bayesian-based method that can achieve effective text segmentation and word discovery in open domain. |
| Outcome: | The proposed method enjoys robust performance and transparent interpretation when no training corpus and domain vocabulary are available. |
TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry via Bayesian Inference (2023.emnlp-main)
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| Challenge: | Experimental studies confirm that TopWORDS-Poetry can successfully segment poetry words without pre-given vocabulary or training corpus. |
| Approach: | They propose an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus. |
| Outcome: | Experimental results show that TopWORDS-Poetry can segment poetry lines into meaningful words with high quality without pre-given vocabulary or training corpus. |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |