this study was inspired by vanpatten and uludag’s (2011) study on the transferability of training via processing instruction to output tasks and mori’s (2002) work on the development of talk-in-interaction during a group task. an interview was devised as the pretest, posttest, and delayed posttest to compare four intervention types for teaching the simple past passive: traditional intervention ...

Most state-of-the-art solutions to sound signal processing tasks such as the speech and noise separation task and the music style classification task are based on Recurrent Neural Network (RNN) architecture or Hidden Markov Model (HMM). Both RNN and HMM assume that the input is chain-structured so that each element in the chain is equally dependent on all its previous units. However in real-lif...

Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an exte...

In recent years, machine learning (ML) has been used more and more to solve complex tasks in different disciplines, ranging from Data Mining to Information Retrieval or Natural Language Processing (NLP). These tasks often require the processing of structured input, e.g., the ability to extract salient features from syntactic/semantic structures is critical to many NLP systems. Mapping such stru...

In this paper we tackle a unique and important problem of extracting a structured order from the conversation a customer has with an order taker at a restaurant. This is motivated by an actual system under development to assist in the order taking process. We develop a sequence-tosequence model that is able to map from unstructured conversational input to the structured form that is conveyed to...

We interpret the predictions of any blackbox structured input-structured output model around a specific input-output pair. Our method returns an “explanation” consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the black-box model with perturbed inputs, generating a graph over tokens from the responses, and solving a partitioning pr...

Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature representations during training. However, their input is tensor data, i.e., the...

The aim of this paper is to start a comparison between Recursive Neural Networks (RecNN) and kernel methods for structured data, specifically Support Vector Regression (SVR) machine using a Tree Kernel, in the context of regression tasks for trees. Both the approaches can deal directly with a structured input representation and differ in the construction of the feature space from structured dat...