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Hierarchical Embeddings for Hypernymy Detection and Directionality
TLDR
A novel neural model HyperVec, which represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym$-$hyponym distributional hierarchy, is presented, able to generalize over unseen hypernymy pairs, and by mapping to other languages. Expand
Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech
TLDR
This work conducts extensive experiments using an attentive convolutional neural network with multi-view learning objective function for speech emotion recognition and achieves state-of-the-art results on the improvised speech data of IEMOCAP. Expand
GlobalPhone: A multilingual text & speech database in 20 languages
This paper describes the advances in the multilingual text and speech database GlobalPhone, a multilingual database of high-quality read speech with corresponding transcriptions and pronunciationExpand
Combining Recurrent and Convolutional Neural Networks for Relation Classification
TLDR
This paper presents a new context representation for convolutional neural networks for relation classification (extended middle context), and proposes connectionist bi-directional recurrent neural networks and introduces ranking loss for their optimization. Expand
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
TLDR
A novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity and integrated into the objective function of a skip-gram model is proposed. Expand
Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding
TLDR
A novel CNN architecture for sequence labeling is proposed which takes into account the previous context words with preserved order information and pays special attention to the current word with its surrounding context and combines the information from the past and the future words for classification. Expand
A first speech recognition system for Mandarin-English code-switch conversational speech
TLDR
This paper presents first steps toward a large vocabulary continuous speech recognition system (LVCSR) for conversational Mandarin-English code-switching (CS) speech and investigated statistical machine translation (SMT) - based text generation approaches for building code- Switched language models. Expand
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
TLDR
A novel neural network model AntSynNET is presented that exploits lexico-syntactic patterns from syntactic parse trees and successfully integrates the distance between the related words along the syntactic path as a new pattern feature. Expand
Generating exact lattices in the WFST framework
We describe a lattice generation method that is exact, i.e. it satisfies all the natural properties we would want from a lattice of alternative transcriptions of an utterance. This method does notExpand
Challenges of Computational Processing of Code-Switching
TLDR
This paper addresses challenges of Natural Language Processing on non-canonical multilingual data in which two or more languages are mixed by highlighting and discussing the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work. Expand
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