Abstract

Community Question Answering (cQA) forums have become a popular medium for soliciting answers to specific user questions from experts and experienced users in a given topic. However,for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this,the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) Deep Learning (DL) techniques which automatically learn the feature representations.In this paper, we propose a novel approach for AQP known as -“Deep Feature Fusion Network(DFFN)”which combines the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, a DFFN architecture indepen-dently - a) learns features using the Deep Neural Network (DNN) and b) computes handcrafted features leveraging various external resources and then combines the musing a fully connected neural network trained to predict the quality of the given answer. DFFN is an end-end differen-tiable model and trained as a single system. We propose two different DFFN architectures which vary mainly in the way they model the input question/answer pair - a) DFFN-CNN which uses a Convolutional Neural Network (CNN) and b) DFFN-BLNA which uses a Bi-directional LSTMwith Neural Attention (BLNA). Both these proposed variants of DFFN (DFFN-CNN and DFFN-BLNA) achieve state-of-the-art performance on the standard SemEval-2015 and SemEval-2016benchmark datasets and outperforms baseline approaches which individually employ either HCFor DL based techniques alone.

Abstract

In this paper, we introduce Vaidya, a spoken dialog system which is developed as part of the ITRA1project. The system is capable of providing an approximate diagnosis by accepting symptoms as free-form speech in real-time on both laptop and hand-held devices. The system focuses on challenges in speech recognition specific to Indian languages and capturing the intent of the user. Another challenge is to create models which are memory and CPU efficient for hand-held devices. We describe our progress, experiences and approaches in building the system that can handle English as the input speech. The system is evaluated using subjective statistical measure (Fleiss’ kappa) to assess the usability of the system

Abstract

We present Kathaa, an Open Source web-based Visual Programming Framework for Natural Language Processing (NLP) Systems. Kathaa supports the design, execution and analysis of complex NLP systems by visually connecting NLP components from an easily extensible Module Library. It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in their own NLP applications irrespective of their technical proficiency in Natural Language Processing. Kathaa exposes an intuitive web based Interface forthe users to interact with and modify complex NLP Systems; and a precise Module definition API to allow easy integration of new state of the art NLP components. Kathaa enables researchers to publish their services in a standardized format to enable the masses to use their services out ofthe box. The vision of this work is to pave the way for a system like Kathaa, to be the Lego blocks of NLP Research and Applications. As a practical use case we use Kathaa to visually implement the Sampark Hindi-Panjabi Machine Translation Pipeline and the Sampark Hindi-Urdu Machine Translation Pipeline, to demonstrate the fact that Kathaa can handle really complex NLP systems while still being intuitive for the end user.

Abstract

Designing various learning strategies has been gaining a lot of scientific interest during the recent progress of deep learning methodologies. Curriculum learning is a learning strategy aimed at training the neural network model by presenting the samples in a specific meaningful order rather than randomly sampling the training examples from the data distribution. In this work, we have explored starting small paradigm of curriculum learning technique for speech recognition. The starting small paradigm of curriculum learning is performed by a two step learning strategy. Training dataset is re-organized as a set of easily classifiable examples followed by the actual training dataset and the model is trained on the re-organized dataset. We hypothesize that by following the starting small learning paradigm the learning gets initialized in a better way and progresses to attain a better convergence. We propose to rank the toughness of the training example based on the posterior probabilities obtained using a previously trained model. Apart from re-arranging the training corpus starting small paradigm of curriculum learning is applied at model level. We consider the broad manner-class classification objective function as the smoother version of the phone class classification objective function. The model initially trained for broad class classification is later adapted for phone classification.In this work, we have used TIMIT and a subset of WallStreet Journal (WSJ) corpus to validate the experiments, both the learning strategies have shown consistently better performances across the two datasets compared to the baseline system trained by randomly sampling the dataset.

Abstract

Today, a large amount of audio data is available on the web in the form of audiobooks, podcasts, video lectures, video blogs, news bulletins, etc. In addition, we can effortlessly record and store audio data such as a read, lecture, or impromptu speech on handheld devices. These data are rich in prosody and provide a plethora of voices to choose from, and their availability can significantly reduce the overhead of data preparation and help rapid building of synthetic voices. But, a few problems are associated with readily using this data such as (1) these audio files are generally long, and audio-transcription alignment is memory intensive; (2) precise corresponding transcriptions are unavailable, (3) many times, no transcriptions are available at all; (4) the audio may contain dis-fluencies and non-speech noises, since they are not specifically recorded for building synthetic voices; and (5) if we obtain automatic transcripts, they will not be error free. Earlier works on long audio alignment addressing the first and second issue generally preferred reasonable transcripts and mainly focused on (1) less manual intervention, (2) mispronunciation detection, and (3) segmentation error recovery. In this work, we use a large vocabulary public domain automatic speech recognition (ASR) system to obtain transcripts, followed by confidence measure-based data pruning which together address the five issues with the found data and also ensure the above three points. For proof of concept, we build voices in the English language using an audiobook (read speech) in a female voice from LibriVox and a lecture (spontaneous speech) in a male voice from Coursera, using both reference and hypotheses transcriptions, and evaluate them in terms of intelligibility and naturalness with the help of a perceptual listening test on the Blizzard 2013 corpus.

Abstract

In this paper, an approach to detect the sentiment of a song based on its multi-modality natures (text and audio) is presented. The textual lyric features are extracted from the bag of words. By using these features, Doc2Vec will generate a single vector for each song. Support Vector Machine (SVM), Naive Bayes (NB) and a combination of both these classifiers are developed to classify the sentiment using the textual lyric features. Audio features are used as an add-on to the lyrical ones which include prosody features, temporal features, spectral features, tempo and chroma features. Gaussian Mixture Models (GMM), SVM and a combination of both these classifiers are developed to classify the sentiment using audio features. GMM are known for capturing the distribution in the features and SVM are known for discriminating the features. Hence these models are combined to improve the performance of sentiment analysis. Performance is further improved by combining the text and audio feature domains. These text and audio features are extracted at the beginning, ending and for the whole song. From our experimental results, it is observed that the first 30 seconds (s) of a song gives better performance for detecting the sentiment of the song rather than the last 30s or from the whole song.

Abstract

Code-mixing is a linguistic phenomena frequently observed in user generated content on social media, especially by multilingual users. Apart from the inherent linguistic complexity, the analysis of code-mixed content poses complex challenges owing to the presence of spelling variations, transliteration and non-adherence to a formal grammar. However, for any downstream Natural Language Processing task, tools that are able to process and analyze code-mixed data are required. Currently there is a lack of publicly available resources for code-mixed Hindi-English data, while the amount of such text is increasing everyday. In this study, our focus is on creation of a dataset that has codemixed Hindi-English sentences along with the associated language and normalisation labels. To the best of our knowledge, our work is the first attempt at the creation of a linguistic resource for this language pair, which is also made public. In this work, we also present an empirical study detailing the construction of a language identification and normalisation system designed for this language pair.

Abstract

The main goal of this paper is to perform the dialog act(DA) tagging for Telugu corpus. Annotation of utterances with dialog acts is necessary to recognize the intent of speaker in dialog systems. While English language follows a strict subject–verb–object(SVO) syntax, Telugu is a free word order language. The n-gram DA tagging methods proposed for English language will not work for free word order languages like Telugu. In this paper, we propose a method to perform DA tagging for Telugu corpus using advanced machine learning techniques combined with karaka dependency relation modifiers. In other words, we use syntactic features obtained from karaka dependencies and apply combination of language models(LMs) at utterance level with Hidden Markov Model(HMM) at context level for DA tagging. The use of karaka dependencies for free word order languages like Telugu helps in extracting the

Abstract

Part-of-Speech Tagging is a primary and an important step for many Natural Language Processing Applications. POS taggers have reported high accuracies on grammatically correct monolingual data. This paper reports work on annotating code mixed English-Telugu data collected from social media site Facebook and creating automatic POS Taggers for this corpus. POS tagging is considered as a classification problem and we use different classifiers like Linear SVMs, CRFs, Multinomial Bayes with different combinations of features which capture both context of the word and its internal structure. We also report our work on experimenting with combining monolingual POS taggers for POS tagging of this code mixed English-Telugu data.

Abstract

n this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at this http URL.