895 citations · 1.1k across the 38 of their papers we have counts for
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Verb Argument Structure Alternations in Word and Sentence Embeddings
Katharina Kann, Alex Warstadt, Adina Williams +1
Verbs occur in different syntactic environments, or frames. We investigate whether artificial neural networks encode grammatical distinctions necessary for inferring the idiosyncra…
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Ryan Cotterell, Christo Kirov, John Sylak-Glassman +10
The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the numb…
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Katharina Kann, Sascha Rothe, Katja Filippova
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric fo…
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting
Katharina Kann, Hinrich Schütze
Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a…
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy…
Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
Manuel Mager, Elisabeth Mager, Alfonso Medina-Urrea +2
Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translati…
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Katharina Kann, Manuel Mager, Ivan Meza-Ruiz +1
Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neur…