Accepted papers

Computational Characterization of Mental States: A Natural Language Processing Approach

Author: Facundo Carrillo

Psychiatry is an area of medicine that strongly bases its diagnoses on the psychiatrists subjective appreciation. The task of diagnosis loosely resembles the common pipelines used in supervised learning schema. Therefore, we propose to augment the psychiatrists diagnosis toolbox with an artificial intelligence system based on natural language processing and machine learning algorithms. This approach has been validated in many works in which the performance of the diagnosis has been increased with the use of automatic classi- fication.

Improving Distributed Representations of Tweets - Present and Future

Author: Ganesh Jawahar

Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models

Bilingual Word Embeddings with Bucketed CNN for Parallel Sentence Extraction

Authors: Jeenu Grover and Pabitra Mitra

We propose a novel model which can be used to align the sentences of two different languages using neural architectures. First, we train our model to get the bilingual word embeddings and then, we create a similarity matrix between the words of the two sentences. Because of different lengths of the sentences involved, we get a matrix of varying dimension. We dynamically pool the similarity matrix into a matrix of fixed dimension and use Convolutional Neural Network (CNN) to classify the sentences as aligned or not. To further improve upon this technique, we bucket the sentence pairs to be classified into different groups and train CNN’s separately. Our approach not only solves sentence alignment problem but our model can be regarded as a generic bag-of-words similarity measure for monolingual or bilingual corpora.

nQuery - A Natural Language Statement to SQL Query Generator

Authors: Nandan Sukthankar, Sanket Maharnawar, Pranay Deshmukh, Yashodhara Haribhakta and Vibhavari Kamble

In this research, an intelligent system is designed between the user and the database system which accepts natural language input and then converts it into an SQL query. The research focuses on incorporating complex queries along with simple queries irrespective of the database. The system accommodates aggregate functions, multiple conditions in WHERE clause, advanced clauses like ORDER BY, GROUP BY and HAVING. The system handles single sentence natural language inputs, which are with respect to selected database. The research currently concentrates on MySQL database system. The natural language statement goes through various stages of Natural Language Processing like morphological, lexical, syntactic and semantic analysis resulting in SQL query formation.

V for Vocab : An Intelligent Flashcard Application

Authors: Nihal V. Nayak, Tanmay Chinchore, Aishwarya Hanumanth Rao, Shane Michael Martin, Sagar Nagaraj Simha, G. M. Lingaraju and H. S. Jamadagni

Students choose to use flashcard applications available on the Internet to help memorize word-meaning pairs. This is helpful for tests such as GRE, TOEFL or IELTS, which emphasize on verbal skills. However, monotonous nature of flashcard applications can be diminished with the help of Cognitive Science through Testing Effect. Experimental evidences have shown that memory tests are an important tool for long term retention (Roediger and Karpicke, 2006). Based on these evidences, we developed a novel flashcard application called “V for Vocab” that implements short answer based tests for learning new words. Furthermore, we aid this by implementing our short answer grading algorithm which automatically scores the user’s answer. The algorithm makes use of an alternate thesaurus instead of traditional Wordnet and delivers state-of-theart performance on popular word similarity datasets. We also look to lay the foundation for analysis based on implicit data collected from our application.

Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions 

Author: Sudha Rao

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clari- fication questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation Systems

Authors: Yui Suzuki, Tomoyuki Kajiwara and Mamoru Komachi

We propose a novel sentential paraphrase acquisition method. To build a wellbalanced corpus for Paraphrase Identifi- cation, we especially focus on acquiring both non-trivial positive and negative instances. We use multiple machine translation systems to generate positive candidates and a monolingual corpus to extract negative candidates. To collect nontrivial instances, the candidates are uniformly sampled by word overlap rate. Finally, annotators judge whether the candidates are either positive or negative. Using this method, we built and released the first evaluation corpus for Japanese paraphrase identification, which comprises 655 sentence pairs.

Segmentation Guided Attention Networks for Visual Question Answering

Authors: Vasu Sharma, Ankita Bishnu and Labhesh Patel

In this paper we propose to solve the problem of Visual Question Answering by using a novel segmentation guided attention based network which we call SegAttendNet. We use image segmentation maps, generated by a Fully Convolutional Deep Neural Network to refine our attention maps and use these refined attention maps to make the model focus on the relevant parts of the image to answer a question. The refined attention maps are used by the LSTM network to learn to produce the answer. We presently train our model on the visual7W dataset and do a category wise evaluation of the 7 question categories. We achieve state of the art results on this dataset and beat the previous benchmark on this dataset by a 1.5% margin improving the question answering accuracy from 54.1% to 55.6% and demonstrate improvements in each of the question categories. We also visualize our generated attention maps and note their improvement over the attention maps generated by the previous best approach.

Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks

Authors: Kaixin Ma, Catherine Xiao and Jinho D. Choi

We propose a convolutional neural network model for text-based speaker identification on multiparty dialogues extracted from the TV show, Friends. While most previous works on this task rely heavily on acoustic features, our approach attempts to identify speakers in dialogues using their speech patterns as captured by transcriptions to the TV show. It has been shown that different individual speakers exhibit distinct idiolectal styles. Several convolutional neural network models are developed to discriminate between differing speech patterns. Our results confirm the promise of text-based approaches, with the best performing model showing an accuracy improvement of over 6% upon the baseline CNN model.

Variation Autoencoder Based Network Representation Learning for Classification 

Authors: Hang Li, Haozheng Wang, Zhenglu Yang and Masato Odagaki

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study introduces a deep network representation model that seamlessly integrates the text information and structure of a network. The model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. The representation learned with a paragraph vector model is merged with that learned with the VAE to obtain the network representation, which preserves both structure and text information. Comprehensive experiments is conducted on benchmark datasets and find that the introduced model performs better than state-of-the-art techniques.

Blind Phoneme Segmentation With Temporal Prediction Errors 

Authors: Paul Michel, Okko Räsänen, Roland Thiolliere and Emmanuel Dupoux

Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural networks. Our approach consists in analyzing the error profile of a model trained to predict speech features frameby-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.

Automatic Generation of Jokes in Hindi

Authors: Srishti Aggarwal and Radhika Mamidi

When it comes to computational language generation systems, humour is a relatively unexplored domain, especially more so for Hindi (or rather, for most languages other than English). Most researchers agree that a joke consists of two main parts - the setup and the punchline, with humour being encoded in the incongruity between the two. In this paper, we look at Dur se Dekha jokes, a restricted domain of humorous three liner poetry in Hindi. We analyze their structure to understand how humour is encoded in them and formalize it. We then develop a system which is successfully able to generate a basic form of these jokes.

Word Embedding for Response-To-Text Assessment of Evidence

Authors: Haoran Zhang and Diane Litman

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students’ writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.

Domain Specific Automatic Question Generation from Text

Author: Katira Soleymanzadeh

The goal of my doctoral thesis is to automatically generate interrogative sentences from descriptive sentences of Turkish biology text. We employ syntactic and semantic approaches to parse descriptive sentences. Syntactic and semantic approaches utilize syntactic (constituent or dependency) parsing and semantic role labeling systems respectively. After parsing step, question statements whose answers are embedded in the descriptive sentences are going to be formulated by using some predefined rules and templates. Syntactic parsing is done using an open source dependency parser called MaltParser (Nivre et al. 2007). Whereas to accomplish semantic parsing, we will construct a biological proposition bank (BioPropBank) and a corpus annotated with semantic roles. Then we will employ supervised methods to automatic label the semantic roles of a sentence.

SoccEval: An Annotation Schema for Rating Soccer Players

Authors: Jose Ramirez, Matthew Garber and Xinhao Wang

This paper describes the SoccEval Annotation Project, an annotation schema designed to support machine-learning classi- fication efforts to evaluate the performance of soccer players based on match reports taken from online news sources. In addition to factual information about player attributes and actions, the schema annotates subjective opinions about them. After explaining the annotation schema and annotation process, we describe a machine learning experiment. Classifiers trained on features derived from annotated data performed better than a baseline trained on unigram features. Initial results suggest that improvements can be made to the annotation scheme and guidelines as well as the amount of data annotated. We believe our schema could be potentially expanded to extract more information about soccer players and teams.

Accent Adaptation for the Air Traffic Control Domain

Authors: Matthew Garber, Meital Singer and Christopher Ward

Automated speech recognition (ASR) plays a significant role in training and simulation systems for air traffic controllers. However, because English is the default language used in air traffic control (ATC), ASR systems often encounter dif- ficulty with speakers’ non-native accents, for which there is a paucity of data. This paper examines the effects of accent adaptation on the recognition of non-native English speech in the ATC domain. Accent adaptation has been demonstrated to be an effective way to model under-resourced speech, and can be applied to a variety of models. We use Subspace Gaussian Mixture Models (SGMMs) with the Kaldi Speech Recognition Toolkit to adapt acoustic models from American English to German-accented English, and compare it against other adaptation methods. Our results provide additional evidence that SGMMs can be an efficient and effective way to approach this problem, particularly with smaller amounts of accented training data

Generating Steganographic Text with LSTMs

Authors: Tina Fang, Martin Jaggi and Katerina Argyraki

Motivated by concerns for user privacy, we design a steganographic system (“stegosystem”) that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem based on a Long ShortTerm Memory (LSTM) neural network. We demonstrate our approach on the Twitter and Enron email datasets and show that it yields high-quality steganographic text while significantly improving capacity (encrypted bits per word) relative to the state-of-the-art.

Predicting Depression for Japanese Blog Text

Author: Misato Hiraga

This study aims to predict clinical depression, a prevalent mental disorder, from blog posts written in Japanese by using machine learning approaches. The study focuses on how data quality and various types of linguistic features (characters, tokens, and lemmas) affect prediction outcome. Depression prediction achieved 95.5% accuracy using selected lemmas as features.

Fast Forward Through Opportunistic Incremental Meaning Representation Construction

Authors: Petr Babkin and Sergei Nirenburg

One of the challenges semantic parsers face involves upstream errors originating from pre-processing modules such as ASR and syntactic parsers, which undermine the end result from the get go. We report the work in progress on a novel incremental semantic parsing algorithm that supports simultaneous application of independent heuristics and facilitates the construction of partial but potentially actionable meaning representations to overcome this problem. Our contribution to this point is mainly theoretical. In future work we intend to evaluate the algorithm as part of a dialogue understanding system on state of the art benchmarks.

Modeling Situations in Neural Chat Bots

Authors: Shoetsu Sato, Naoki Yoshinaga, Masashi Toyoda and Masaru Kitsuregawa

Social media accumulates vast amounts of online conversations that enable datadriven modeling of chat dialogues. It is, however, still hard to utilize the neural network-based SEQ2SEQ model for dialogue modeling in spite of its acknowledged success in machine translation. The main challenge comes from the high degrees of freedom of outputs (responses). This paper presents neural conversational models that have general mechanisms for handling a variety of situations that affect our responses. Response selection tests on massive dialogue data we have collected from Twitter confirmed the effectiveness of the proposed models with situations derived from utterances, users or time.

An Empirical Study on End-to-End Sentence Modelling

Author: Kurt Junshean Espinosa

Accurately representing the meaning of a piece of text, otherwise known as sentence modelling, is an important component in many natural language inference tasks. We survey the spectrum of these methods, which lie along two dimensions: input representation granularity and composition model complexity. Using this framework, we reveal in our quantitative and qualitative experiments the limitations of the current state-of-the-art model in the context of sentence similarity tasks.

Varying Linguistic Purposes of Emoji in (Twitter) Context

Authors: Noa Naaman, Hannah Provenza and Orion Montoya

Early research into emoji in textual communication has focused largely on highfrequency usages and ambiguity of interpretations. Investigation of a wide range of emoji usage shows these glyphs serving at least two very different purposes: as content and function words, or as multimodal affective markers. Identifying where an emoji is replacing textual content allows NLP tools the possibility of parsing them as any other word or phrase. Recognizing the import of non-content emoji can be a a significant part of understanding a message as well. We report on an annotation task on English Twitter data with the goal of classifying emoji uses by these categories, and on the effectiveness of a classifier trained on these annotations. We find that it is possible to train a classifier to tell the difference between those emoji used as linguistic content words and those used as paralinguistic or affective multimodal markers even with a small amount of training data, but that accurate sub-classification of these multimodal emoji into specific classes like attitude, topic, or gesture will require more data and more feature engineering.

Negotiation of Antibiotic Treatment in Medical Consultations: A Corpus Based Study

Author: Nan Wang

Doctor-patient conversation is considered a contributing factor to antibiotic overprescription. Some language practices have been identified as parent pressuring doctors for prescribing; other practices are considered as likely to engender parent resistance to non-antibiotic treatment recommendations. In social science studies, approaches such as conversation analysis have been applied to identify those language practices. Current research for dialogue systems offer an alternative approach. Past research proved that corpusbased approaches have been effectively used for research involving modeling dialogue acts and sequential relations. In this proposal, we propose a corpus-based study of doctor-patient conversations of antibiotic treatment negotiation in pediatric consultations. Based on findings from conversation analysis studies, we use a computational linguistic approach to assist annotating and modeling of doctor-patient language practices, and analyzing their influence on antibiotic over-prescribing.