Scott Cyphers

A Framework for Developing Conversational User Interfaces

In this work we report our efforts to facilitate the creation of mixed-initiative conversational interfaces for novice and experienced developers of human language technology. Our focus has been on a framework that allows developers to easily specify the basic concepts of their applications, and rapidly prototype conversational interfaces for a variety of configurations. In this paper we describe the current knowledge representation, the compilation processes for speech understanding, generation, and dialogue turn management, as well as the user interfaces created for novice users and more experienced developers. Finally, we report our experiences with several user groups in which developers used this framework to prototype a variety of conversational interfaces.

Query understanding enhanced by hierarchical parsing structures

Query understanding has been well studied in the areas of information retrieval and spoken language understanding (SLU). There are generally three layers of query understanding: domain classification, user intent detection, and semantic tagging. Classifiers can be applied to domain and intent detection in real systems, and semantic tagging (or slot filling) is commonly defined as a sequence-labeling task - mapping a sequence of words to a sequence of labels. Various statistical features (e.g., n-grams) can be extracted from annotated queries for learning label prediction models; however, linguistic characteristics of queries, such as hierarchical structures and semantic relationships, are usually neglected in the feature extraction process. In this work, we propose an approach that leverages linguistic knowledge encoded in hierarchical parse trees for query understanding. Specifically, for natural language queries, we extract a set of syntactic structural features and semantic dependency features from query parse trees to enhance inference model learning. Experiments on real natural language queries show that augmenting sequence labeling models with linguistic knowledge can improve query understanding performance in various domains.

VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems

Continuous word and phrase vectors have proven useful in a number of NLP tasks. Here we describe our experience using them as a source of features for the SemEval-2015 task 3, consisting of two community question answering subtasks: Answer Selection for categorizing answers as potential, good, and bad with regards to their corresponding questions; and YES/NO inference for predicting a yes, no, or unsure response to a YES/NO question using all of its good answers. Our system ranked 6th and 1st in the English answer selection and YES/NO inference subtasks respectively, and 2nd in the Arabic answer selection subtask.

A Vector Space Approach for Aspect Based Sentiment Analysis

Vector representations for language has been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Aspect Based Sentiment Analysis. In particular, we target three sub-tasks namely aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vectorbased features and conduct extensive experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 scores of 79.91% for aspect term extraction, 86.75% for category detection, and the accuracy 72.39% for aspect sentiment prediction.

Asgard: A portable architecture for multilingual dialogue systems

Spoken dialogue systems have been studied for years, yet portability is still one of the biggest challenges in terms of language extensibility, domain scalability, and platform compatibility. In this work, we investigate the portability issue from the language understanding perspective and present the Asgard architecture, a CRF-based (Conditional Random Fields) and crowd-sourcing-centered framework, which supports expert-free development of multilingual dialogue systems and seamless deployment to mobile platforms. Combinations of linguistic and statistical features are employed for multilingual semantic understanding, such as n-grams, tokenization and part-of-speech. English and Mandarin systems in various domains (movie, flight and restaurant) are implemented with the proposed framework and ported to mobile platforms as well, which sheds lights on large-scale speech App development.

Spoken command of large mobile robots in outdoor environments

We describe a speech system for commanding robots in human-occupied outdoor military supply depots. To operate in such environments, the robots must be as easy to interact with as are humans, i.e. they must reliably understand ordinary spoken instructions, such as orders to move supplies, as well as commands and warnings, spoken or shouted from distances of tens of meters. These design goals preclude close-talking microphones and “push-to-talk” buttons that are typically used to isolate commands from the sounds of vehicles, machinery and non-relevant speech. We used multiple microphones to provide omnidirectional coverage. A novel voice activity detector was developed to detect speech and select the appropriate microphone to listen to. Finally, we developed a recognizer model that could successfully recognize commands when heard amidst other speech within a noisy environment. When evaluated on speech data in the field, this system performed significantly better than a more computationally intensive baseline system, reducing the effective false alarm rate by a factor of 40, while maintaining the same level of precision.

SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering.

Community question answering platforms need to automatically rank answers and questions with respect to a given question. In this paper, we present the approaches for the Answer Selection and Question Retrieval tasks of SemEval-2016 (task 3). We develop a bag-of-vectors approach with various vectorand text-based features, and different neural network approaches including CNNs and LSTMs to capture the semantic similarity between questions and answers for ranking purpose. Our evaluation demonstrates that our approaches significantly outperform the baselines.

Data collection and language understanding of food descriptions

This paper presents initial data collection and language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a users spoken meal description. We first summarize the data collection and annotation of food descriptions performed via Amazon Mechanical Turk. We then present semantic labeling experiments using a semi-Markov conditional random field (CRF) that obtains an F1 test score of 85.1. Finally, we report food segmentation experiments that explored three methods for associating foods with their corresponding attributes: a generative Markov model, transformation-based learning, and a CRF classifier. The CRF performed best, achieving an F1 test score of 87.1.