Papers by Tom Bosc
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings (D18-1)
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| Challenge: | Monolingual dictionaries are widespread and semantically rich resources. |
| Approach: | They propose a model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. |
| Outcome: | The proposed model shows strong performance when trained exclusively on dictionary data and generalizes in one shot. |
The Emergence of Argument Structure in Artificial Languages (2022.tacl-1)
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| Challenge: | a new study shows that natural languages are shaped by cognitive and sociocultural factors. |
| Approach: | They propose a setup where agents talk about a variable number of entities that can be partially observed by the listener. |
| Outcome: | The proposed setup shows that awareness of object structure yields a more natural sentence organization. |
Do sequence-to-sequence VAEs learn global features of sentences? (2020.emnlp-main)
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| Challenge: | Autoregressive language models are often trained without explicit conditioning labels . authors question claim that latent vectors can capture global features in unsupervised manner . |
| Approach: | They propose to use a sequence-to-sequence architecture to learn latent variables . they find that VAEs are prone to memorizing the first words and sentence length . |
| Outcome: | The proposed model is prone to memorizing the first words and sentence length, the authors show . et al., 2016: a new model learns latent variables that are more global, more predictive of topic or topic labels . authors question this claim, say it is a waste of time and money . |