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Found 15 papers, 10 papers with code
Multi hash embeddings in spaCy
In this technical report we lay out a bit of history and introduce the embedding methods in spaCy in detail.
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations
The perturbed data, models, and code are available for reproduction at https://github. com/cmry/augtox
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
A natural language database interface (NLDB) can democratize data-driven insights for non-technical users.
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
Written language contains stylistic cues that can be exploited to automatically infer a variety of potentially sensitive author information.
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning
However, we do find that using an external machine translation model to generate the synthetic data sets results in better performance.
Improving Lemmatization of Non-Standard Languages with Joint Learning
Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword.
Lessons learned in multilingual grounded language learning
Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language.
Revisiting the Hierarchical Multiscale LSTM
Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input.
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
NeuralREG: An end-to-end approach to referring expression generation
Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function.
On the difficulty of a distributional semantics of spoken language
In the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units.
Imagination improves Multimodal Translation
We decompose multimodal translation into two sub-tasks: learning to translate and learning visually grounded representations.
Representation of linguistic form and function in recurrent neural networks
We present novel methods for analyzing the activation patterns of RNNs from a linguistic point of view and explore the types of linguistic structure they learn.
Learning language through pictures
We propose Imaginet, a model of learning visually grounded representations of language from coupled textual and visual input.
