no code implementations • MSR (COLING) 2020 • Gábor Recski, Ádám Kovács, Kinga Gémes, Judit Ács, Andras Kornai
We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations.
1 code implementation • 9 Jun 2023 • Judit Acs, Endre Hamerlik, Roy Schwartz, Noah A. Smith, Andras Kornai
We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks.
1 code implementation • EACL 2021 • Judit {\'A}cs, {\'A}kos K{\'a}d{\'a}r, Andras Kornai
For POS tagging both of these strategies perform poorly and the best choice is to use a small LSTM over the subwords.
1 code implementation • 8 Dec 2020 • Judit Acs, Andras Kornai
By training the models on the same source but different target, we can compare what subwords are important for different tasks and how they relate to each other.
no code implementations • SEMEVAL 2020 • {\'A}d{\'a}m Kov{\'a}cs, Kinga G{\'e}mes, Andras Kornai, G{\'a}bor Recski
In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions.
no code implementations • LREC 2020 • {\'A}d{\'a}m Kov{\'a}cs, Judit {\'A}cs, Andras Kornai, G{\'a}bor Recski
We study a typical intermediary task to Machine Translation, the alignment of NPs in the bitext.
no code implementations • WS 2019 • {\'A}d{\'a}m Kov{\'a}cs, Evelin {\'A}cs, Judit {\'A}cs, Andras Kornai, G{\'a}bor Recski
The Surface Realization Shared Task involves mapping Universal Dependency graphs to raw text, i. e. restoring word order and inflection from a graph of typed, directed dependencies between lemmas.
no code implementations • 20 May 2019 • Zalan Gyenis, Andras Kornai
We describe a rational, but low resolution model of probability.