Search Results for author:
Found 11 papers, 3 papers with code
Probing for Referential Information in Language Models
Language models keep track of complex information about the preceding context {--} including, e. g., syntactic relations in a sentence.
Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
We find that constraints in both learning and selection can foster mutual exclusivity, as long as they put words in competition for lexical meaning.
Towards Incremental Learning of Word Embeddings Using Context Informativeness
In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way.
Putting words in context: LSTM language models and lexical ambiguity
In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers.
How to represent a word and predict it, too: Improving tied architectures for language modelling
Recent state-of-the-art neural language models share the representations of words given by the input and output mappings.
Colorless green recurrent networks dream hierarchically
Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language.
Discontinuous Verb Phrases in Parsing and Machine Translation of English and German
In this paper, we focus on the verb-particle (V-Prt) split construction in English and German and its difficulty for parsing and Machine Translation (MT).
Multi-lingual Dependency Parsing Evaluation: a Large-scale Analysis of Word Order Properties using Artificial Data
We propose a method to evaluate the effects of word order of a language on dependency parsing performance, while controlling for confounding treebank properties.
