Schedule 73. StuTS

This talk presents a mediating perspective on computational phrase composition between naïve additive composition and complex latent representations from regression models. The aim of the work presented is to reduce the unrestricted transformation of latent embeddings to a classification problem of convolutional filters at phrase level. The classifier is embedded within a Tree-LSTM (Tai et al. 2015) architecture and trained on truth-grounded sister node prediction. Preliminary results show evidence that a restricted selection of filters is enough for phrase construction in high-dimensional spaces, but also, there is more to it than addition.
This talk will introduce the distributional compositional semantics framework (Baroni 2014) and outline previous computational work into the texture of (semantic) merge, as well as present preliminary results from my Master thesis.

This talk presents a mediating perspective on computational phrase composition between naïve additive composition and complex latent representations from regression models. The aim of the work presented is to reduce the unrestricted transformation of latent embeddings to a classification problem of convolutional filters at phrase level. The classifier is embedded within a Tree-LSTM (Tai et al. 2015) architecture and trained on truth-grounded sister node prediction. Preliminary results show evidence that a restricted selection of filters is enough for phrase construction in high-dimensional spaces, but also, there is more to it than addition.
This talk will introduce the distributional compositional semantics framework (Baroni 2014) and outline previous computational work into the texture of (semantic) merge, as well as present preliminary results from my Master thesis.

Baroni, M., Bernardi, R. and Zamparelli, R., 2014. Frege in space: A program for compositional distributional semantics. Linguistic Issues in language technology, 9, pp.241-346.
Tai, K.S., Socher, R. and Manning, C.D., 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075.

Info

Day: 2023-05-27
Start time: 11:05
Duration: 00:30
Room: SH 0.107
Track: Theoretical Linguistics

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