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20182020
most citedInducing Syntactic Trees from BERT Representations

13 citations · 14 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.CL2020

Introducing Orthogonal Constraint in Structural Probes

Tomasz Limisiewicz, David Mareček

With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing…

cs.CL20201 cited

Syntax Representation in Word Embeddings and Neural Networks -- A Survey

Tomasz Limisiewicz, David Mareček

Neural networks trained on natural language processing tasks capture syntax even though it is not provided as a supervision signal. This indicates that syntactic analysis is essent…

cs.CL201913 cited

Inducing Syntactic Trees from BERT Representations

Rudolf Rosa, David Mareček

We use the English model of BERT and explore how a deletion of one word in a sentence changes representations of other words. Our hypothesis is that removing a reducible word (e.g.…

cs.CL2019

Derivational Morphological Relations in Word Embeddings

Tomáš Musil, Jonáš Vidra, David Mareček

Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes. In this paper, we explore the potential of word…

cs.CL2019

From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions

David Mareček, Rudolf Rosa

We inspect the multi-head self-attention in Transformer NMT encoders for three source languages, looking for patterns that could have a syntactic interpretation. In many of the att…

cs.CL2018

Input Combination Strategies for Multi-Source Transformer Decoder

Jindřich Libovický, Jindřich Helcl, David Mareček

In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper…